Business Analytics Maturity

Model - an adaptation to the

e-commerce industry

Master’s Thesis 30 credits

Department of Business Studies

Uppsala University

Spring Semester of 2019

Date of Submission: 2019-05-29

André Dahlgren

Valentin Nilsson

Supervisor: Jukka Hohenthal

Abstract Maturity models have become a widely used framework for assessing various capabilities and

technologies among businesses. This thesis develops a maturity model for assessing Business

Analytics (BA) in Swedish e-commerce firms. Business Analytics has become an increasingly

important part of modern businesses, and firms are continuously looking for new ways to

perform analysis of the data available to them. The prominent previous maturity models within

BA have mainly been developed by IT-consultancy firms with the underlying intent of selling

their IT services. Consequently, these models have a primary focus on the technical factors

towards Business Analytics maturity, partly neglecting the importance of organisational factors.

This thesis develops a Business Analytic Maturity Model (BAMM) which fills an identified

research gap of academic maturity models with emphasis on the organisational factors of BA

maturity. Using a qualitative research design, the BAMM is adapted to the Swedish e-commerce

industry through two sequential evaluation stages. The study finds that organisational factors

have a greater impact on BA maturity than previous research suggests. The BAMM and the

study’s results contribute with knowledge of Business Analytics, as well as providing

e-commerce firms with insights into how to leverage their data.

Keywords: Maturity Model, Business Analytics, E-commerce, Swedish Businesses,

Organisational factors, Technical factors, Data, Data Analytics, Resource-based view.

Acknowledgements

We would like to thank our supervisor, Jukka Hohenthal, for providing us with guidance

throughout the writing of our thesis.

Furthermore, we would like to thank our opponent and the other members of our seminar group

for continuous feedback and suggestions for improvement, as well as friends and family for

providing us with useful insights.

A special thanks to all respondents and their firms who participated in this study. By taking the

time to provide us with your thoughts and opinions, you made this thesis possible.

We hope that you as a reader will find this thesis as interesting as we do.

_________________________ _________________________

André Dahlgren Valentin Nilsson

Uppsala, 2019.05.29 Uppsala, 2019.05.29

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Table of content

1. Introduction 4 1.1 Aim of Study 6 1.2 Relevance of Study 7 1.3 Research Question 7

2. Background 8 2.1 Resource-Based View 8 2.2 Business Analytics 9

2.2.1 Benefits & challenges 10 2.2.2 Business Analytics in e-commerce 11

3. Literature Review 13 3.1 Maturity Models 13

3.1.1 Advantages of achieving a high maturity 14 3.1.2 Criticism towards Maturity Models 15 3.1.3 Maturity Models in e-commerce research and other domains 16

3.2 Towards creating a Business Analytics Maturity Model (BAMM) 17 3.3 Review of previous Maturity Models within Analytics 19

3.3.1 Davenport & Harris (2017) - The Five Stages of Analytical Maturity 22 3.3.2 Cosic et al. (2015) - Business Analytics Capability Maturity Model 23 3.3.3 Tavallaei et al. (2015) - Business Intelligence Maturity Model 23

3.4 Summary and Conceptualisation of BAMM 24

4. Method 27 4.1 Research Design 27 4.2 Data Collection 28

4.2.1 Evaluation - Business Analytics experts (stage 1) 29 4.2.2 Evaluation - E-commerce firms (stage 2) 30

4.3 Operationalisation of BAMM Dimensions 32 4.4 Limitations to Research Design 32

5. Evaluation by BA experts (Stage 1) 34 5.1 Organisational Factors 35 5.2 Technical Factors 38 5.3 Modifications to the BAMM 40

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6. Evaluation by E-commerce Firms (stage 2) 42 6.1 Results 42

6.1.1 Management Support 42 6.1.2 Employee Skills 43 6.1.3 Enterprise Decision-making 44 6.1.4 Strategy 45 6.1.5 Culture 46 6.1.6 Data 47 6.1.7 Infrastructure & Tools 49

6.2 Discussions on BA Maturity in E-commerce 50 6.3 Final Modifications to the BAMM 54

7. Conclusions 56 7.1 Suggestions for Further Research 59

8. References 60

9. Appendix 66 9.1 Operationalised Framework based on Previous Academic Models 66 9.2 Operationalised Framework (prior to stage 2) 68 9.3 Interview Guide (stage 2) 70 9.4 Survey Business Analytics Maturity within E-commerce 72 9.5 List of Tables and Figures 74

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1. Introduction Business’ use of data analysis has become increasingly important during the recent decade.

E-commerce has emerged as a sector in which data analysis can provide important competitive

advantages. Amazon, eBay and Alibaba are among the market leaders in Business Analytics

(BA), while other firms are at the beginning of their journey towards learning and excelling in

data analysis. BA entails the gathering, analysis, and interpretation of data in order to improve

decision-making accuracy. Although organisations may already have implemented tools for

analysis, it does not automatically imply that they are using it to its fullest potential (Ransbotham

et al., 2015; Raguseo, 2018). The development of data analytic capabilities among businesses

can, in fact, be impeded for a number of reasons, dominantly caused by organisational factors

(McAfee & Brynjolfsson, 2012; Raguseo, 2018). For instance, personnel opposed to changes,

shortage of analytical talent, or simply lack of investments in analytic competencies are some of

the factors which hinders complete utilization of BA (Nikolic et al., 2014; McAfee &

Brynjolfsson, 2012; Raguseo, 2018).

This thesis deepens the knowledge of data analytics management through the lens of the

capability maturity assessment literature and the concept of maturity models. The process of

assessing a certain capability in an organisation theoretically stems from the Resource-Based

View (RBV) of the firm, which argues that a firm’s internal resources and capabilities constitute

the competitive advantage (Barney, 1991; Wade & Holland, 2004). In order to assess the

maturity of a capability, Software Engineering Institute was first to introduce a model called

Capability Maturity Model (Humphrey, 1988). The model evaluates an organisation’s current

maturity level with regards to a capability and gives explicit recommendations for how to reach a

higher desired maturity. Since the maturity model’s first introduction, several adaptations of

maturity models have been developed and tested on different capabilities and technologies. For

instance; Team collaboration (Boughzala & De Vreede, 2015), Cloud Services (Chen et al.,

2014) and Innovation (Hosseini et al., 2017).

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In terms of assessing Business Analytics as a capability, several similar maturity models have

been developed for this purpose (Chen & Nath, 2018; Cosic et al., 2015; Davenport & Harris,

2007; Tavallei et al., 2015). The specific use of maturity models within Business Analytics can

provide organisations with various ways of gaining competitive advantages (De Bruin et al.,

2005). For instance, a high BA maturity ensures data-driven decision-making, predictive

forecasting, and a higher return on analytical capability investments (Davenport & Harris, 2007;

McAfee & Brynjolfsson, 2012; Olszak, 2016; Ransbotham et al., 2015).

However, many previous BA Maturity Models are created without thorough documentation, as

the models’ studies contain insufficient explanation and reasoning behind each step of the

models’ development process. This lack of documentation decreases the validity of the available

BA maturity models research (Comuzzi & Patel, 2016; Rajteric, 2010; Tavallei et al., 2015).

Additionally, a majority of the previous BA Maturity Models are created and presented by

IT-consultancy firms and technology vendors, which focus have primarily been on the technical

issues of BA maturity, thus neglecting the organisational factors. This has created a lack of

available research studying the organisational factors towards achieving a higher BA maturity

(Chen & Nath, 2017).

By building on existing literature available in data analytics maturity, this study develops its own

Business Analytics Maturity Model (BAMM). With an extensive focus on the organisational

factors of analytics maturity, as well as thorough documentation of the model’s development

process, the BAMM fills the research gaps of well-documented and organisational-focused

models.

Maturity models are often not context-specific and limited research have been made to apply a

BAMM on a specific industry (Poeppelbuss & Roeglinger, 2011; Cosic et al., 2015). Although

the e-commerce sector has been studied in a few maturity studies, no previous maturity model

has been specifically developed for Business Analytics in e-commerce. As the Swedish

e-commerce market has seen exponential growth in the last decade it provides a particularly

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interesting industry to assess BA Maturity (Postnord, 2019). Business Analytics plays a

significant role due to the market’s highly competitive landscape and the need to gain

competitive advantages. Additionally, online retail automatically generates large amounts of

data, which enable e-commerce firms to easily conduct complex data analysis. For instance,

consumers’ online shopping leave trails of data which can then be analysed to track consumer

behaviour, generate sales recommendations or streamline logistics (Akter & Wamba, 2016;

Lehrer et al., 2018). For these reasons, this thesis’ BAMM is developed for application to the

e-commerce industry in Sweden, creating insights about the importance of the organisational and

technical factors. Moreover, the model provides e-commerce firms with tools to assess their own

analytical capabilities.

1.1 Aim of Study This study presents a Business Analytics Maturity Model with the suitable dimensions used to

assess BA maturity of Swedish e-commerce firms. The study divides the dimensions into

organisational factors and technical factors and discusses the characteristics and importance of

each dimension, as well as develops an operationalised framework used for assessing each

dimension. E-commerce is facing growth and the competition between online actors is fierce. In

order to succeed with online sales, organisations need to acknowledge and make use of their

available data. By focusing the study on Swedish e-commerce firms, the results aim to provide

the industry with insights into Business Analytics.

The BAMM and its dimensions are evaluated in two sequential stages. First by BA experts, then

by e-commerce firms. Consequently, the model continuously receives modifications and

adjustments throughout the study to better suit its intended purpose. This thesis serves

e-commerce firms with guidelines of how to improve their own Business Analytic maturity.

However, in order to be fully able to compare a firm with its peers, benchmarking data needs to

be collected. In a future research stage, the BAMM would, therefore, need to be quantified using

the operationalised framework presented in this study. By assessing the BA maturity amongst a

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large number of e-commerce firms, benchmarking data is formed, and the various maturity levels

of the BAMM will be formed and defined, ranging from low maturity to high maturity.

1.2 Relevance of Study The thesis makes an academic contribution by deepening the research on Capability Maturity

Models and adds knowledge to the literature by adapting the model to a specific context.

Moreover, the thesis contributes with further research within the field of Business Analytics, a

relatively new technology, by exploring how to utilise analytic capabilities in an organisation.

Lastly, the study’s model is iteratively validated as it receives evaluations by both experts within

Business Analytics as well as Swedish e-commerce firms. These evaluations ensure the model’s

practical relevance. There is also a practical contribution as this thesis collects data on BA

capabilities from Swedish e-commerce firms and the results provide other organisations with

insights on how to develop their BA maturity and what factors that impede this development.

1.3 Research Question What is a suitable Business Analytics Maturity Model framework for analysing the maturity of an

organisation’s analytical capabilities and data usage amongst Swedish e-commerce firms?

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2. Background This chapter motivates this thesis focus by firstly introducing the theoretical standpoint of the Resource-Based View. From there, the following sections define the concept of Business Analytics along with its benefits and challenges, and concludes with a summary of research of Business Analytics in e-commerce.

2.1 Resource-Based View A prominent area of business research within strategic management study how organisations

gain competitive advantages. Within this stream of research, two main perspectives are used:

external and internal. The external perspective analyses factors in the organisation’s

environment, for instance by using Porter’s (1979) famous “five forces model”. This perspective

assumes that organisations are identical to each other in terms of the relevant resources they

possess. If one organisation should develop or acquire any new resources, their competitive

advantage will be short-lived, as resources are mobile and can be easily sold or copied (Barney,

1991). The internal perspective of competitive advantage builds on different assumptions and is

referred to as the Resource-Based View (RBV) of a firm (Barney, 1991). RBV assumes that

organisations within the same industry are heterogeneous as they possess different strategic

resources. Firm resources, as defined by Daft (1983) is “...all assets, capabilities, organisational

processes, firm attributes, information, knowledge, etc. controlled by a firm that enable the firm

to conceive of and implement strategies that improve its efficiency and effectiveness”.

Furthermore, the RBV assumes that possession of strategic resources can be stable over time,

which in turn creates sustained competitive advantages (Barney, 1991).

Barney (1991) describes how organisations’ competitive advantages are distinguished by the

duplicability and mobility of their possessed resources. For example, if a resource is easily

attained by other organisations (e.g. computers), it has high mobility and the competitive

advantage of firms possessing this resource is likely to be short-term. On the contrary, if a

resource is difficult to attain and is not easily bought or copied by another firm (e.g.

organisational culture), the firm possessing the resource is likely to sustain a long-term

competitive advantage.

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The analytical capabilities of a firm is a typical example of a resource and therefore ought to be

examined through the perspective of RBV. Poeppelbuss et al. (2011) identify RBV as a useful

concept to help explain the competitive advantages created by efficiently utilising the firm’s

resources, such as Business Analytics.

2.2 Business Analytics For the past years, organisations have used an increasing amount of analytics tools to analyse

their available data and improve their decision-making. The definitions and terms used in the

area of analytics are dispersed. Within analytics, concepts such as Business Intelligence (BI),

Business Analytics (BA), and predictive analysis are often mentioned. What distinguishes the

concepts is that BI builds analysis based on past data as support for decision-making, while

analytics also have predictive capabilities that can analyse and suggest future changes or trends

ahead of time (Davenport & Harris, 2007). This thesis focuses on Business Analytics as an

umbrella term, including several concepts such as Business Intelligence (BI), decision-support,

and predictive analysis (see Chen & Nath, 2018).

“Business Analytics” helps us describe what is really referred to when going beyond the value of

data to discuss the analytical capabilities of firms. Davenport & Harris (2007), prominent

researchers within the field, use the following definition of Business Analytics: “the use of data,

information technology, statistical analysis, quantitative methods, and mathematical or

computer-based models to help managers gain improved insight about their operations, and

make better fact-based decisions”. The concept encompasses three different types of analytical

elements: descriptive, predictive, prescriptive (Davenport & Harris, 2007). Descriptive analytics

are insights into the past, such as standard reporting. This type of analysis answers the question

of “what happened?” while at the same time comparing it to the organisation’s previous

predictions. A typical example of descriptive analytics is budgets compared to the actual

outcomes. Predictive analytics provides insights into what is likely to happen in the future. By

analysing ongoing trends and patterns in real-time data, predictive analytics attempts to predict

future events and assists managers to take more accurate decisions. Finally, prescriptive analytics

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analyses the potential impacts of decisions taken by managers and calculates the possible effects

of their decisions. This element of analytics enables for a comparison of several possible future

scenarios, guiding managers towards the best decision (Davenport & Harris, 2007).

BA is associated with handling large amounts of data. In analytics, data is often characterised

using “the four V’s” - Volume, Variety, Veracity, and Velocity. The first characteristic, Volume,

stands for the immense amounts of data that currently produced. This provides interesting

opportunities for organisations to analyse the data to improve their decision-making (McAfee &

Brynjolfsson, 2012). Variety of data signifies the different types of data that exists, which can

possess challenges for data management. For instance, data can be created in several forms such

as messages, pictures, GPS signals and sensors. The third characteristic, the Veracity of data,

emphasises the importance of high-quality data as many decision-makers do not trust the data

they possess (Gandomi & Haider, 2015). The final characteristic of data emphasises the speed of

data, the Velocity. Real-time data enables organisations to instantly take data-driven decisions.

These characteristics of data all play an important role in the development of analytical

capabilities and usage of available data (Gandomi & Haider, 2015; McAfee & Brynjolfsson,

2012).

2.2.1 Benefits & challenges

Although some businesses may struggle with creating value out of analytics investments, the

literature about the benefits of analytics is clear. Previous research shows that organisations

using analytics tools have improved productivity and operational performance (Chae et al., 2014;

Muller et al., 2018). Raguseo (2018) presents support for several benefits of analytic capabilities,

such as better alignment of IT- and business strategy, the ability to produce enhanced products or

services, easier access to data, as well as improved data accuracy.

The challenges of Business Analytics have been identified by both academic researchers and

practitioners. Several researchers have identified the dominant challenges of BA as mainly

managerial, especially amongst senior management who are not familiar with the technology and

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are suddenly pressured to adapt to data-driven decision making (Chen & Nath, 2017; McAfee &

Brynjolfsson, 2012; Raguseo, 2018). In managerial decision-making, HiPPOs (Highest-Paid

Person’s Opinion) are still prevalent in many firms which can impede data-driven

decision-making (McAfee & Brynjolfsson, 2012; Love et al., 2005). One highly recognised risk

with analytic technologies relates to privacy issues (Wu & Wang, 2005; Raguseo, 2018). The

recent introduction of the General Data Protection Regulation (GDPR) in 2018 has put even

more pressure on organisations to address these issues. Additionally, other identified BA

challenges are misalignment between business- and IT-strategies, reluctance to adapt to new

changes, as well as the ability to present understandable and visually logical data reports (Love et

al., 2005; McAfee & Brynjolfsson, 2012; Vidgen et al., 2017).

2.2.2 Business Analytics in e-commerce

Electronic commerce is a growing market where businesses need to stay one step ahead of

competitors to ensure survival. Between 2012-2018, the Swedish e-commerce market more than

doubled in size, but it only accounted for 9,8% of Sweden’s retail industry’s revenue during

2018 (Postnord, 2019). However, e-commerce exclusively stands for the growth of the retail

industry in Sweden, as the traditional brick and mortar retail store growth is at a standstill (ibid).

This indicates that e-commerce is becoming increasingly important, as consumers gradually shift

from offline to online shopping.

Business Analytics insights are generated through analysing consumer data, predicting patterns

and providing an enhanced shopping experience. Kauffman et al. (2012) emphasised the critical

role of data analysis in e-commerce as early as 2012 but identified a lack of research on effective

ways for management to efficiently leverage the data. The issue for management was

determining what data is relevant and then using it to their benefit.

Business Analytics today provides e-commerce firms with several insights to improve their

business (Lehrer et al., 2018). Firstly, firms gain an understanding of their customers. BA assists

to demographically segment customers, which generates information on customer shopping

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behaviour, preferences, and habits. By recording and analysing how consumers navigate through

the business’ website (known as consumers’ “clickstream”) BA can create personalised shopping

experiences and run targeted campaigns to increases sales and conversion rates (amount of

website visitors making an actual purchase). Secondly, BA can enhance pricing strategies. Due

to the highly competitive landscape, pricing strategies are crucial to ensure profitability. With

BA technology analysing historical sales data, price elasticity, competitors prices etc, firms can

build dynamic pricing models which determine the optimum prices of products. Thirdly, BA can

streamline supply-chain management. For instance, BA can determine customers demand for

products based on available data to calculate the optimal amount of stock. This prevents loss of

sales due to understock as well as unnecessary overstock costs (ibid).

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3. Literature Review The literature review is structured as follows: First, a background to Maturity Models is provided which highlights the need for a new model with a focus on e-commerce. Next, a strategy for designing the model is presented by identifying the necessary requirements needed to create and present a model. The last section identifies and discusses previous Business Analytics Maturity Models, which serves as a foundation to our own first version of BAMM, presented at the end of the literature review.

3.1 Maturity Models In order to stay competitive, organisations need to constantly assess and develop their

capabilities. Maturity models are conceptually a helpful tool to assess precisely this (De Bruin et

al., 2005). The quality of organisations’ capabilities varies, as some organisations tend to be

more “mature” than others in their capability assessment (Andersen & Henriksen, 2006). In other

words, maturity models aim to assess organisational capabilities and locate the organisation

within a specific chronological maturity level, in most cases levels 1-5, where level 5 signifies

the highest maturity (de Bruin et al., 2005). Furthermore, maturity models can provide the

appropriate actions to reach a higher maturity level, thus increasing the quality of an

organisation’s capabilities (Becker et al., 2009; Kohlegger et al., 2009). By locating their current

maturity level, organisations are able to path their own way towards their desired maturity level.

There are several applications of the maturity model across various technologies. However, the

original model stems from the Capability Maturity Model (CMM) which was first introduced by

Watts Humphrey (1988) at the Software Engineering Institute, as a means to assess the software

capabilities in organisations. As a result of the CMM, organisations gained an understanding of

software process development and were able to improve their capabilities (Paulk et al., 1993).

Since its successful introduction, there has been a steady growth of presented maturity models

applicable to various IS domains (Poeppelbuss & Roeglinger, 2011).

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The purpose of the maturity model is versatile, however, the application of the model holds three

specific purposes: descriptive, comparative, and prescriptive (Poeppelbuss & Roeglinger, 2011).

The purposes are not mutually exclusive, as it is common that a model holds several purposes.

● Descriptive purpose: The model is applied as a diagnostic tool, determining the

organisation’s current capabilities (Becker et al., 2009). This purpose is described as an

“as-is” assessment due to the fact that the assigned maturity level represents the current

capabilities.

● Comparative purpose: A comparative model provides the ability for organisations to

benchmark their results across their industry to find and compare best practice

(Poeppelbuss & Roeglinger, 2012).

● Prescriptive purpose: The model provides indications of how an organisation can achieve

a higher level of maturity than its current level, serving as a road-map for improvement

(de Bruin et al., 2005). The prescriptive purpose of the model, described as a “to-be”

assessment, relates to its ability to identify desired higher levels of maturity and provide

guidelines for how to get there (Becker et al., 2009).

In summary, maturity models are used as a representation of the current situation (descriptive),

an instrument for controlling and comparing with other organisations across industries

(comparative) and recommended actions for progression (prescriptive) (Hain & Back, 2011,

p.11). This thesis BAMM holds a descriptive purpose as it could be used to assess a firm’s

current maturity. However, as mentioned, further research needs to quantify the BAMM by

assessing the model amongst a large sample of e-commerce firms. Quantifying the model will

provide it with a comparative purpose as firms are able to compare their maturity with

competitors in the industry. In turn, the benchmarking data could enable prescriptions of how

firms can improve their BA maturity.

3.1.1 Advantages of achieving a high maturity

Supported by the Resource-Based View of the firm, long-term competitive advantages are

secured when firms possess strategic resources that cannot be immediately duplicated by

competitors (Barney, 1991). Firms are able to develop these strategic resources by using maturity

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models (De Bruin et al., 2005). The premise of maturity models is that a higher level of maturity

will result in an advantage, such as higher performance, increased effectiveness, control, or

predictability (Boughzala & de Vreede, 2012 p.307; Vezzetti et al., 2014 p.900). Assessing the

maturity of a capability is commonly executed by professional vendors such as consultancy

firms, and the concept has just recently been widely adopted by academic researchers

(Poeppelbus et al., 2011). One common objection towards maturity models is therefore how

maturity really benefits an organisation.

Regarding Business Analytics, research conducted by Ransbotham et al. (2015) states that the

more complex analytics businesses are managing, the greater the competitive advantages they

achieve. Studies emphasise the advantages of Business Analytics maturity, as a high level of

maturity positively correlates with positive BA outcomes for the organisation (Chen & Nath,

2018; Davenport & Harris, 2007). It has also been shown that analytic maturity positively

correlates with improved managerial decision-making (Popovic et al., 2012). Decision-making

based on data insights rather than intuition can generate benefits such as reducing costs and time

to market, assembling new products and services, and increasing their quality (Mettler et al.,

2010 p.334). Furthermore, Ransbotham et al. (2015) discuss how a higher level of analytics

complexity (maturity) points towards an ability to manage predictive and prescriptive analytics

that can help the firm to forecast the future and make better decisions. Westerman et al. (2012)

also found that firms achieving a high level of digital maturity have greater revenue generation,

greater market valuation, and roughly 25% higher profitability than firms with a low level of

maturity.

3.1.2 Criticism towards Maturity Models

Although maturity models have become an accepted method for testing the maturity level of

various capabilities in organisations, they have received criticism. The most critical weakness is

the lack of documentation in the development process of new maturity models. Researchers take

the freedom to conceptualise their own models, without documenting proper explanations and

reasoning for their decisions during the development process (de Bruin et al., 2005; Kohlegger et

al., 2009; Solli-Saether & Gottschalk, 2010). This questions the validity of the available models

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(Becker et al., 2009). In order to be accepted in research, it is crucial that new maturity models

are theoretically solid and tested through empirical research during the development process.

Furthermore, researchers also need to document the development process (de Bruin et al., 2005;

Solli-Saether & Gottschalk, 2010). Another issue with maturity models regards the assessment

process itself. As the firms’ assess themselves, through the individual perceptions of their

employees, there is a risk of inaccurate results. However, this risk is mitigated through having

multiple employees make the assessment and giving them explicit instructions of different levels

of maturity should be assessed.

Due to the increasing amount of new maturity models constantly being developed, an issue

regarding the models’ irretrievability has emerged. Mettler et al. (2010 p.334) clarify: “As no

classification for precisely allocating different kinds of maturity models exist, the search for and

the selection of specific models is time-consuming and exhausting”. This implies that maturity

researchers must be critical in their analysis and only include previous models in their literature

review that are created with thorough documentation and a solid theoretical foundation. As a

result, this thesis’ literature review selection process was critical of previous Business Analytics

Maturity Models, choosing models which had been thoroughly developed.

3.1.3 Maturity Models in e-commerce research and other domains

Maturity models have been developed to suit several fields of research. However, maturity

models are most prominent in the field of Information Systems and Software Development

(Vezzetti et al., 2014 p.900). Poeppelbuss and Roeglinger (2011) identify an increased growth of

Information Systems maturity models.

Although no previous maturity models have specifically been created for Business Analytics

within the e-commerce industry, a few noteworthy models have been created for e-commerce

firms. These models are developed for academic research and only focus on the development of

the e-commerce firm. McKay et al.’s (2000) model “Stages of Maturity for E-business: The

SOG-e model” studies on the journey which traditional retailing businesses undergo towards

becoming an online actor for e-commerce. Some years later, Rao et al. (2003) created an

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e-commerce model similar to McKay et al., but with focus on depicting the growth of

e-commerce development for small- and medium-sized enterprises (SMEs). Chan & Swatman

(2004) were quick to build on Rao et al.’s research by creating a maturity model for

organisational growth in B2B e-commerce, applicable to the Australian market.

These identified models within the field of e-commerce share similarities in that they are all

focused on organisations’ development towards becoming an online actor. No previous research

of maturity models in e-commerce has been devoted to studying the specific use of Business

Analytics. This thesis aims to fill this apparent gap in research by creating its own Business

Analytics Maturity Model, applicable to the e-commerce sector.

3.2 Towards creating a Business Analytics Maturity Model (BAMM) In order to create a maturity model valid for future research and with a real contribution to the

field of data analytics, this thesis makes sure to complete the necessary requirements for

achieving this. The creation of the BAMM is, therefore, is structured in accordance to Becker et

al.’s (2009) 8 requirements in for developing a maturity model, which were developed as a

response to the rising popularity and increase of new maturity models created in research (ibid,

p.214). Their study “Developing Maturity Models for IT Management - A Procedure Model and

its Application” proposes a development method consisting of 8 requirements which have to be

fulfilled in order to create a valid new maturity model. Their development approach is widely

recognised and has been applied in several studies similar to this thesis (Comuzzi & Patel, 2016;

Tavallaei et al., 2015; Chen et al., 2014; Sukrat & Papasratorn, 2018).

The following sections present Beckers et al.’s (2009) 8 requirements (R1-R8) which this thesis

aims to fulfil. Each requirement is followed by a definition. We clarify how and in which chapter

of the thesis each requirement is fulfilled.

R1: Identification of problem relevance - Demonstrate the relevance of the problem solution. The

suggested maturity model should serve as a proposed solution to demand in research. The

introduction section in this study highlights a specific need for developing a maturity model with

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a larger focus on the organisational factors, thus giving the study a more academic approach. The

research question is stated in the introduction section 1.3.

R2: Problem Definition - Defining the problem and scope. Establishing the problem relevance

also requires the exact definition of the problem. This involves defining the target domain and

the target group of the model and the conditions for its application and intended scientific

contribution. The problem definition is clarified in the Introduction chapter 1.

R3: Comparison with existing Maturity Models - A comparison between existing maturity

models in the particular domain. The need for a new model must be evident using a comparison

of existing models. There are four basic design strategies to a new model: a completely new

model design, the enhancement of an existing model, the combination of several previous

models, and the transfer of structures or contents from an existing model to a whole new domain.

This thesis section 3.3 identifies and discusses the relevant literature needed to support the

development of our BAMM. The design strategy of our model is based on a combination of

several previous academic maturity models within Business Analytics, also presented in section

3.3.

R4: Multi-methodological Procedure - The maturity model must be developed using various

research methods which need to be well-founded and attuned. This thesis firstly conducts a

literature analysis (chapter 3) to develop a first version of the model. The model is then

evaluated by experts within the BA domain to validate the model’s dimensions. This evaluation

results in a modified version of the model, presented in chapter 5. The modified version is then

evaluated a second time, this time by data analysts in Swedish e-commerce firms. This causes

further alterations to the model, resulting in a final version of the model, presented in chapter 6.

This multi-methodological procedure increases the validity of the BAMM.

R5: Iterative Procedure - The model must be created iteratively, ie follow step-by-step

procedures. The sub-steps of this requirement are: selecting the domain, design strategy, creating

the model, and evaluating the model, all which must be iterated. The first version of the model is

presented in section 3.4 following a literature review. Two new versions are subsequently

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presented as the model undergoes two evaluation stages, refining the model further. The second

evaluation stage concludes in the final version of the BAMM, presented in section 6.3.

R6: Evaluation - All principles and premises for the development of a model must be evaluated

and tested for comprehensiveness, consistency, and problem adequacy. Evaluation of our model

was carried out through interviews with Business Analytics experts and Swedish e-commerce

firms.

R7: Scientific Documentation - The process of designing and developing a maturity model must

be documented in detail, with consideration of each research step, the chosen methods, the actors

involved, the results, and the conclusions. Only maturity models with thorough scientific

documentation should be considered valid for future research usage. This thesis documents every

step of the development process in order to serve as valid for further research purposes. A

well-documented research process increases the reliability of the study.

R8: Targeted Presentation of Results - Determining how to communicate and transfer the results

of the maturity model to the target audience and the specific user groups. As this study provides

both academic- and practical contributions, we consider how the results should be effectively

communicated. This thesis is published in the Digital Scientific Archive (DIVA), available at

http://www.diva-portal.org. Thus, the study will be available for any individual or organisation

aspiring to gain further knowledge about Business Analytics and Maturity Models.

3.3 Review of previous Maturity Models within Analytics The concept of capability maturity has been applied across several domains since its first

introduction. In fact, Pranicevic et al. (2011) identify over 150 Information System domains

where some kind of maturity assessment has been tested, ranging from software development to

e-business.

A common critique towards maturity models within analytics stem from the creation and

promotion of the model by IT-consulting firms and professional vendors (Comuzzi & Patel,

2016). These firms publish models to promote their own professional objectives. As a result,

their models have a large focus on the technical factors towards BA maturity, and relatively little

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focus on the organisational factors. This thesis separates maturity models created by researchers

for academic purposes from models created by professionals for promotional purposes. The

systematic review of previous data analytics maturity models leads to the selection of three

specific models which have been developed by researchers for academic purposes: Davenport &

Harris (2017), Cosic et al. (2015), Tavallaei et al. (2015). The models are presented in Table 1

below. Each model presents several dimensions which assess maturity. In some models,

dimensions are further segmented into sub-dimensions. The model in Table 1 has a larger focus

on the organisational factors of maturity, which is why we place heavier emphasis on the support

of these three academic models in the creation of our own BAMM.

We have also chosen to present a few maturity models by prominent professional firms,

presented in Table 2 below. However, as the models are created by profit-driven firms, we

critically question the underlying purpose of the models’. These models are not used in the

creation of our BAMM, but can still be suitable to gain a general understanding of maturity

models within analytics, which is why we have chosen not to neglect them entirely. Included in

the tables are also the maturity levels of the models, as these will be of interest for our proposed

future research stage (see section 7.1).

Table 1. BA Maturity Models for academic research

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The academic models in Table 1 are perceived as more suitable as a theoretical foundation to

support this thesis’ BAMM and are therefore further explained and discussed in the following

sections.

3.3.1 Davenport & Harris (2017) - The Five Stages of Analytical Maturity

The model is adapted from their book Competing on Analytics: The New Science of Winning.

Davenport & Harris are prominent researchers within analytics. Their research has assisted many

organisations to improve their data analysis and use it as a competitive advantage. The model has

an emphasis on the organisational factors for maturity, as it highlights the importance of

management support and in-house analytical competencies. Davenport & Harris’ first version of

the model from 2007 had five dimensions, however, an updated version was released in 2017

where the last dimensions Technology and Analytical Techniques were added. This update was

partly caused by the introduction of more complex technologies such as Big Data, which

presents organisations with opportunities to analyse larger sets of data using more advanced

technological capabilities.

In order to become what Davenport & Harris refer to as an analytical competitor (level 5, highest

maturity), organisations can choose between two approaches. The first approach is when top

management is in full support of the analytics department in the organisation. This approach is

called “full steam ahead”. With strong top-down support in the organisation, there are fewer

barriers towards a digital transformation. The second approach is more cautious, where top

management is in less support of analytics, sceptical towards initiating any larger analytics

projects. Instead, smaller analytics projects are initiated to gradually gain more support from top

management, until larger analytical projects can be approved and initiated. Although this

approach involves less risk, Davenport & Harris call it the “prove-it detour” as they estimate that

it takes the organisations a couple of extra years until they are able to become analytical

competitors.

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3.3.2 Cosic et al. (2015) - Business Analytics Capability Maturity Model

The Business Analytics Capability Maturity Model developed by Cosic et al. (2015) helps

organisations to scope and evaluate their current BA initiatives. Their model is grounded in the

Resource-Based View, developed from an analysis of existing IS capability maturity models.

Cosic et al. also criticise the majority of previously presented maturity models, argumenting that

they are created by professional vendors with consulting experience, which makes it difficult to

perceive them as unbiased. Additionally, the researchers believe previous BA maturity models

lack theoretical grounding as they focus too much on the data warehousing aspect of BA (ibid, p.

3). Cosic et al. (2015) therefore claim their model to be theoretically grounded as it provides a

holistic view of Business Analytics.

The model also has a dominant focus on the organisational factors which affect BA maturity.

The dimensions People, Culture and Governance are organisational factors while Technology

measures the analytical tool itself. Similar to this thesis, Cosic et al’s model has not been

empirically tested in their study, however, the authors give instructions for how the

quantification of the model should be conducted, in order to create a benchmark (Cosic et al.,

2015 p8).

3.3.3 Tavallaei et al. (2015) - Business Intelligence Maturity Model

This model emphasises the competitive advantages available through data analysis as the

researchers present the Business Intelligence Maturity Model. Although there are multiple

Business Intelligence Maturity Models available amongst previous research, Tavallaei et al.’s

model support this thesis for several reasons. Firstly, the researchers have conducted a thorough

literature review of previous models on which they build their model. This increases the

reliability of the Business Intelligence Maturity Model. Secondly, their model is relatively new

(published in 2015) and is, therefore, better updated to suit new and more complex technologies.

Thirdly, the model contains a few dimensions which make it easily understandable and widely

applicable.

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The dimensions Structure and Rules, Organisational Culture, and Technology are broad

dimensions but are still collectively exhaustive to assess maturity. Structure and Rules and

Organisational Culture are organisational factors, while Technology is a technical factor.

Structure and Rules dominantly refer to processes and structures, but also the amount of

centralisation of authority in the organisation. Organisational Culture covers employee’s

analytical talent and competence in the firm, as well as the extent of management support and the

availability of employee education. Technology covers the analytical tool and refers to the

required software, hardware and network.

3.4 Summary and Conceptualisation of BAMM In summary, the majority of previous BA maturity models have been created and published by

IT-consulting firms and technology vendors with an extensive focus on the technical factors to

BA maturity. However, the available BA maturity models from academia promote the

organisational aspects of developing analytical capabilities (Chen & Nath, 2017; McAfee &

Brynjolfsson, 2012; Nikolic et al., 2014; Raguseo, 2018). Therefore, this thesis’ BAMM puts

greater emphasis on organisational factors of BA maturity. An identified lack of unbiased and

empirical academic research further justifies this chosen approach (see Cosic et al., 2015).

As mentioned by Becker et al. (2009), there are several design strategies for developing a

maturity model (Requirement 3). To create our BAMM we combined the previously discussed

academic models by Davenport & Harris (2017), Cosic et al. (2015), and Tavallaei et al. (2015)

(as listed in Table 1 above). These models are perceived as too broad in their application, which

justifies the need for a more narrow analytics model with application to a specific industry, such

as e-commerce. By grouping the models’ dimensions and sub-dimensions, we created 8 new

dimensions, divided into 2 broader factors, Organisational- and Technical factors (see Figure 1

below). Prior to any of the evaluation stages and modifications to the model, we believe these 8

dimensions (5 organisational, 3 technical) cover the necessary dimensions to determine BA

maturity. Additionally, we believe there to be interrelationships between the two factors, as it can

24

be assumed that technical capabilities, for example, could influence organisational aspects of

BA, and vice versa.

Figure 1 - BAMM dimensions created from previous Maturity Models by Davenport & Harris (2017), Cosic et al. (2015) and Tavallaei et al. (2015), also presented in Table 1.

The dimensions are compiled into our BAMM, presented in Figure 2 below.

Figure 2 - First version of the Business Analytics Maturity Model (BAMM)

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We define each dimension as follows:

Management Support: The top-down support of managers and executives in the organisation.

Leadership attributes and change management are the main factors affecting management

support.

Employee Skills: This refers to the competencies existing in lower-levels of the organisation.

Technological investments are knowledge-intensive and require employee competencies or

education on technical and analytical abilities.

Enterprise Decision-making: This refers to the organisational hierarchy and identifies where

decision rights are placed in the organisation. Decentralisation and centralisation have varying

effects of how well an organisation adapts to changes and takes quick decisions.

Strategy: This refers to the degree of BA initiatives being aligned with the overall business

model and goals of the organisation. Business goals may change over time and data analysis

must be kept in line with the strategy to ensure a high maturity.

Culture: The organisational norms, values, and behaviours. These are naturally formed over time

and for instance affects the acceptance and adoption of new technologies, as well as the ability

for employees to rely on data-driven decision-making instead of intuition.

Data: The information that is collected for analysis and to reveal insights. This also includes

determining the relevancy of the data, making sure that the collected data is going to be useful

for analysis. The dimension also covers ensuring data quality and data security & privacy issues.

Analytical Tools: This refers to the BA technology used for data analysis. The dimension covers

how well the analytical tool integrates with other software systems in the organisation, the

conversion of data into information through generation of reports, the visualisation of data

reports, and the ability to make adjustments to the tool to suit the organisation's goals and

objectives.

Infrastructure: This covers the technology’s hardware, as well as the necessary physical

components of the computer which is needed to run the software, for instance, servers and

cables.

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4. Method This chapter presents the thesis methodology and justifies its chosen methods in relation to its purpose. An operationalised framework is introduced and potential limitations to the thesis are also identified and discussed.

4.1 Research Design This thesis takes a deductive approach in order to develop and validate the Business Analytics

Maturity Model. The phenomenon of Business Analytics in the specific context of this thesis is

unexplored, which calls for a case study with an exploratory approach (Saunders et al., 2012).

Consequently, to fit the purpose of our study, qualitative data collection methods have been

deemed suitable (ibid). Although there have been previous BA maturity models have been

developed, the research has been of mainly conceptual character and there has been negligence

towards thoroughly evaluating the model’s dimensions before publishing the finished model. The

benefits of using a case study design to conduct this study were therefore two-fold. Firstly, data

collections were able to provide a validation of the model and give valuable feedback for the

maturity framework. Secondly, the final maturity model presented in this study has been

thoroughly evaluated through deep insights provided by the chosen method and case study

design. Choosing the e-commerce industry in Sweden as a study object filled an identified gap in

previous maturity model assessment literature. Past research in data analytic maturity assessment

has had a broad focus, and very few studies have been directed towards a specific industry.

A deductive approach often requires quantitative data collection through hypothesis testing.

However, the validation of our BAMM required qualitative interviews with open questions. This

enabled in-depth responses, meaning we could compile an elaborate theoretical framework

(Bryman & Bell, 2011). An alternative strategy to conduct this study’s evaluation stages could be

through quantitative evaluations of the BAMM. A quantitative data collection method could

establish relationships between the BAMM’s dimensions and another chosen dependent variable

(such as financial performance or Return On Investment for analytical capabilities). However, a

common critique towards maturity models is their oversimplicity, and as the causality between

27

our model’s different dimensions and impact on the chosen dependent variable would have been

difficult to establish, we identified a need for in-depth qualitative evaluations of our BAMM. The

final version of the BAMM can then be used in future research for quantitative testing of analytic

maturity, in order to establish the model’s maturity levels.

4.2 Data Collection According to Becker et al.’s (2009) sixth requirement for developing a maturity model, the

model needs to be “evaluated and tested for comprehensiveness, consistency, and problem

adequacy” (see chapter 3.2). Data collection for this thesis was conducted through two

sequential evaluation stages of our BAMM, which tested the relevance of the model’s

dimensions and resulted in continuous modifications to the model during the evaluation stages.

The first evaluation stage was conducted amongst Business Analytics experts, in which we

presented our first version of the BAMM and asked for their general perceptions on data

analytics usage in organisations. The insights we received gave cause to a slight modification to

our BAMM as we entered the second evaluation stage. The second stage involved another

evaluation of the model but from the e-commerce sector. Using our own developed

operationalised framework of interview questions, which we created using a combination of

previous literature and insights from the first evaluation stage, we interviewed Swedish

e-commerce firms on the industry-specific matters of using BA. This stage resulted in further

model adjustments and the presentation of a final version of the BAMM. As our chosen

two-stage evaluation method allowed for continuous adjustments to the model, the BAMM went

from a broad- and general model, to a narrow- and specific model suitable to assess BA within

e-commerce.

A majority of our respondents were based in Stockholm which enabled us to conduct

face-to-face interviews. These interviews are preferred as they enable for facial expression which

can be evaluated in the response (Bryman & Bell, 2011). When face-to-face was not possible,

telephone interviews were conducted. The following sections explain the two evaluation stages

and how data collection was conducted.

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4.2.1 Evaluation - Business Analytics experts (stage 1)

The first evaluation stage captured characteristics surrounding Business Analytics in general. We

presented our BAMM to four Business Analytics experts and asked open-ended questions in

order to objectively capture their input.

The BA experts are all working within the field of data analytics and have knowledge and

experiences of the necessary requirements to succeed in data analysis, as well as the obstacles

which commonly impedes data analysis. The experts could, therefore, contribute with insights as

we presented them our first version of the BAMM. The experts were selected as respondents by

purposive sampling through our personal network of IT-professionals. This sampling method is

suitable for conducting qualitative studies, as it allows the researchers to interview the most

relevant respondents which can fulfil the study’s purpose (Yin, 2011).

The interviews were of exploratory character. This is justified by the fact that the aim of the

interviews was to get input on the characteristics and relevance of each dimension of our first

version of the BAMM. Therefore, we created a simple operationalisation of the model’s

dimensions and used it for the interviews. The BA experts were not made aware of the model’s

dimensions prior to the interview. By doing this, we hoped to receive more honest responses. It

was not until the start of the interview that the concept of maturity models was explained (if

needed), along with the presentation of the first version of our BAMM. The interview questions

were asked with an open-ended approach and respondents were asked to provide input into the

design (e.g. selected dimensions, the importance of specific dimensions) of our model.

Interviews Position Firm Interview length

Respondent #1 Director of BI & Analytics IT Consulting firm 40min

Respondent #2 Director of Business Development (in analytics consulting firm)

Analytics consulting firm 40min

Respondent #3 Director of Data & Analytics Consulting firm 60min

Respondent #4 Consultant within digital strategy Marketing Consulting firm 45min

Table 3. Summary of stage 1 interviews

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After the interviews had been conducted and transcribed, the transcriptions were analysed and

compared to find similarities which were of significance towards the relevance of the model’s

dimensions. The analysis firstly explored if all the model’s dimension were collectively

exhaustive and if any of the dimensions were either overlapping or insignificant. Secondly, the

analysis gave insight into how the dimensions should be operationalised for the second

evaluation stage. The results of the first evaluation stage led us to make adjustments and present

a modified version of the BAMM. The modified version of the model was then used for the

second stage.

4.2.2 Evaluation - E-commerce firms (stage 2)

The second evaluation stage gave us insights towards the industry-specific matters of using BA.

Interviews were conducted with six Swedish e-commerce firms.

When sampling for the second stage, we contacted several Swedish e-commerce firms

explaining the purpose of our thesis and the role of the respondent we aimed to interview.

To manage the variation of e-commerce firms we selected organisations depending on their

turnover and number of employees. The requirements were at least 20 employees and turnover of

a minimum of 15 million SEK. It was decided that any smaller firms may not be conducting

enough data analysis (if any) to provide contributions to our study. The participating firms had a

range of 30-250 employees, and roughly 20 million to 1 billion SEK in turnover. With this wide

range of selected firms, we increase the generalisability of our maturity model. The selection of

respondents within each e-commerce firm occurred through snowball sampling. We were able to

steer the selection of respondents in each firm by requesting to interview the employee in charge

of Business Analytics (or similar position) in the organisation. Examples of common roles

amongst the chosen respondents were “Data Analyst” and “Data Scientist”.

The interviews were conducted using an interview guide of operationalised statements. The

operationalised statements were focused on the dimensions of the BAMM and tested how

relevant the model’s dimensions were with regards to the e-commerce industry. The

operationalised statements were created using a combination of previous literature together with

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new insights received from the first evaluation stage. The statements from previous literature

were first taken from the same studies which the BAMM was created from (Davenport & Harris,

2017; Cosic et al., 2015; Tavallaei et al., 2015). As the BAMM received modification after the

first evaluation, the operationalised statements were then complemented with new statements

generated from our BA experts. By doing this, we ensured the interview guide suited our

modified BAMM as we entered the second evaluation stage. The updated operationalised

framework of statements is presented in appendix 9.2. The statements were re-written into

semi-structured questions to serve as an interview guide during data collection (see appendix

9.3). A semi-structured approach enabled for follow-up questions on certain interesting matters

which could be of use to the study (Saunders et al., 2012). Respondents answered questions

surrounding each model dimension and to what degree they believed it was important for their

firm’s Business Analytic maturity.

Interviews Position Firm Interview length

Respondent #5 Customer Service Analyst Furniture 35min

Respondent #6 Data Analyst Fashion 35min

Respondent #7 Marketing Director Various consumer goods 40min

Respondent #8 Data Analyst Various consumer goods 40min

Respondent #9 Data Scientist Electronics 40min

Respondent #10 Data Analyst Various consumer goods 35min

Table 4. Summary of stage 2 interviews

The data from the second evaluation stage was transcribed and analysed in the same manner as

the first stage. We present results and create discussions on the dimensions of the BAMM. In

order to increase the practical relevance of our study, the analysis of data from the second stage

provides specific examples from the experiences of e-commerce firms. By doing this, our results

are useful for other firms striving towards increasing their BA maturity. The results and

discussions section further provided the model with new insights as it concludes in another

model adjustment and the presentation of a final model version, leaning towards its application

on the e-commerce industry.

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4.3 Operationalisation of BAMM Dimensions As mentioned, the first evaluation stage was conducted through open-ended questions regarding

the dimensions of the BAMM. The objective was to identify BA experts’ input on the

characteristics of each dimension. These interviews consisted of open-ended questions. After the

interviews, we compared our results with the operationalisation of the previous academic models

by Davenport & Harris (2017), Cosic et al. (2015), and Tavallaei et al. (2015). This framework

of statements for assessing maturity within each dimension is found in appendix 9.1.

Prior to the second evaluation stage, the operationalised framework of statements was modified

in accordance with the result from the first stage. Within each dimension, some statements were

added, removed, or altered to better suit the model’s application to Business Analytics. All

statements within each dimension cover the important characteristics of the dimension. For

instance, privacy had been identified as a cause for concern amongst organisations. Therefore, a

statement regarding privacy was added to the Data dimension. The updated operationalised

framework can be examined in appendix 9.2. By rewriting statements into semi-structured

questions, the framework was turned into a thorough interview guide ahead of the second

evaluation stage (the interview guide is found in appendix 9.3).

4.4 Limitations to Research Design The interviews create some issues in need of addressing. Firstly, subjective opinions among all

respondents may run a risk of receiving biased results as respondents believe their view to be the

only accurate response (Saunders et al., 2012). By selecting a wide range of respondents from

different organisations, this potential issue is minimised. Secondly, we identify the risk of a

“hidden agenda” among respondents, especially during data collection from BA experts. The

experts may well be using maturity frameworks in their daily work of selling IT-consulting

services. Promoting another agenda can cause untruthful answers, a risk referred to as respondent

bias (Silverman, 2001). By recording and thoroughly transcribing their interviews, combined

with keeping a critical mindset when interpreting their answers, we were able to minimise this

risk. In addition, we chose to give all respondents and their firms anonymity for two reasons.

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Firstly to reduce the risk of promotional efforts (stage 1) and secondly to inhibit that any

revealing information can be traced back to a respondent’s firm (stage 2) (Sekaran & Bougie,

2010).

A limitation of using qualitative research within a specific context is the generalisability of the

results (Bryman & Bell, 2011). However, as the first evaluation stage holds a broader analytics

perspective, the results could be of value for similar BA studies in other contexts, as well as

generalised to other industries. The second stage presents industry-specific results, which is

believed to have a low generalisability to other sectors than e-commerce. Another potential issue

regarding the generalisability of our results is the fact that interviewed e-commerce firms are

competing in different markets; fashion, healthcare products, and tech products. However, the

evaluation stages revealed that the differences between firms and their specific market were

fairly small, regarding their data analysis. All the firms are structured in similar ways and

conduct the same kinds of BA. Additionally, choosing to focus our study on a specific market

within the e-commerce industry would decrease the practical relevance of our thesis, as the

results would only be applicable to that specific market. Furthermore, the choice to include firms

from different e-commerce markets widened the selection of firms to interview, increasing our

chances of obtaining firms to interview.

The validity of this study may be questioned as we raise the issue of whether the selected

dimensions of our BAMM actually causes a high degree of BA maturity or not. However, as we

base our model on previous research, discussed in the literature section, and as we carefully

categorise the dimensions and definitions used in this research, we are able to maintain a high

validity of our model. Furthermore, as previously discussed, the concept of maturity models has

historically been criticized for its oversimplification of the process of measuring the value of

organisational resources. The validity of maturity models can therefore again be questioned, as

the causality between a high maturity and a dependent variable (i.e. excellent operational or

financial performance) is often not established. However, we highlight in our literature review

that research has found several important benefits and competitive advantages that can be

achieved through a high analytic maturity (see section 3.1.1). This justifies the chosen topic of

maturity models as a research area and demonstrates an internal validity in our research.

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5. Evaluation by BA experts (Stage 1) This chapter presents and discusses key findings from the first data collection stage. The chapter is divided into the organisational and technical factors affecting BA maturity. The discussions conclude in a modification of the BAMM, presented in the last section of this chapter.

All respondents addressed a general confusion amongst organisations regarding the various

concepts used to define analytics. As stated in the theory section 2.2, the thesis has chosen to use

the term Business Analytics to define a wide range of analytic capabilities. This strategy of using

an umbrella term facilitated the interviews. While discussing analytics, respondents mentioned

various buzzwords including: “Business Intelligence”, “statistics”, “predictive modelling”,

“Artificial Intelligence”, “Big Data”, and “Machine Learning”. These dispersed terms can find

some common ground when relating them to the three different analytical elements described in

section 2.2; descriptive, predictive and prescriptive analytics. For instance, “Business

Intelligence” and “statistics” are regarded as descriptive analytics, while “Artificial Intelligence”

and “Machine Learning” is included in predictive or prescriptive analytics (depending on its use

in the specific case).

The BA experts were confident that, if properly conducted, analytics provide organisations with

competitive advantages. Specific to highly competitive markets, respondents believed analytics a

necessity for the survival of the organisation. One respondent mentioned how some analytical

firms, i.e. Amazon, are able to gain such large competitive advantages from their data gathering

and analysis, that competitors are unable to keep up (Respondent 3). These findings seem to

align with theories of the relationship between analytics and competitive advantage

(Ransbotham, 2015; Raguseo, 2018). This further justifies the argument that the thesis’ results

can be of use for other organisations aspiring to gain competitive advantages by developing their

own analytical skills, while also legitimising the theoretical standpoint of the Resource-Based

view of this thesis.

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5.1 Organisational Factors All respondents identified organisational factors to be the main obstacle towards achieving a

high BA maturity. In general, managers see the value of analytics and are aware of their firm’s

need for a digital strategy. However, top management (managers, executives and board

members) seldom have the required commitment to implement a digital mindset into the

organisation. Vidgen et al. (2017) support this, as their research identifies the lack of a clear

analytics strategy as the main challenge for managers striving to create value from Business

Analytics. This obstacle is dominantly caused by the fact that top management is too comfortable

in their seats and do not have the commitment to drive the needed organisational change

(Respondent 3). Additionally, top management may not have the necessary knowledge or

experience of working with analytics, which can lead them to neglect it entirely. These findings

align with McAfee & Brynjolfsson’s (2012) research regarding how “reluctance to change” is a

common inhibitor for adapting analytic technologies. Correspondingly, respondents highlighted

business leaders’ lack of change management as a vital factor for not achieving BA maturity

(Respondents 1, 2, 3). Respondent 3 stated: “It takes time to transform older organisations and

many business leaders are not fulfilling their role of driving the organisation towards exploiting

data and conducting analysis”.

Respondents believed firms should strive towards full integration of analytics into their existing

decision-making processes, and not simply add analytics as a separate detached function

(Respondents 1, 2). A challenge for ‘old’ organisations trying to implement new technologies is

that their processes and systems have already been set up for a very long time. Adding a new

layer of analytics on top of their existing processes could prove ineffective, as the firm’s

analytics and IT-departments are not working close enough to other departments. ‘New’ firms

are often, as respondents stated, “born digital”. These firms have a major advantage towards their

older competitors as analytics is naturally integrated into their decision-making processes, which

explains their oftentimes quick initial growth in the market. Respondent 3 summarised the issue

for an older organisation: “They are at risk of becoming disrupted by newcomers that have fully

35

integrated data analysis in their organisational processes”. ‘New’ firms support systems are

built using modern programming language and on scalable solutions that automatically gather

data. Furthermore, while our respondents believed that organisational hierarchy stills play a role

in decision-making, partly confirming McAfee & Brynjolfsson’s (2012) theory about

Highest-Paid’s Person’s Opinion, they did not believe it to be a vital factor in the strive for a

data-driven organisation.

One respondent provided insights from experiences with a market leading firm within analytics,

regarding their data-driven decision-making processes (Respondent 4). Management only gives

approval of an employee’s new initiative if it can be supported by data. In other words, the

employee needs to be able to present data insights that confirm his or her suggestion. If the

suggestion cannot be validated by data with a high level of significance, the new initiative is

disregarded. However, another respondent stated that organisations can sometimes struggle to

fully trust what the data is indicating (Respondent 1). For instance, an employee suggests a new

idea but it turns out that it is not supported by data insights. Nevertheless, the idea still sounds

good to management and it still gets brought up for a decision. For some reason, firms disregard

real data insights, preferring to trust their own intuition instead. Respondent 1 explains: “When

data supports an argument or idea of a manager, the data is used to ‘build their case’. However,

when data is not in favour of an argument or idea, the data is instead disregarded and the

manager chooses to trust their instinct”.

Respondents gave several reasons why analytics is not as widely adopted as it possibly could be.

Firstly, using analytical tools is to some degree a mental barrier for employees (Respondents 1,

4). Employees may be reluctant to conduct the data analysis by themselves and instead chose to

rely on the analytics department (or similar IT-support function) to run the analysis for them.

Secondly, employees may not be given the necessary time and training to learn how to analyse

data (Respondents 2, 3, 4). Thirdly, firms may not see the value in recruiting new competent staff

with a digital mindset (Respondents 1, 3). Instead, they expect their existing staff to adopt and

use new technologies, even though they do not provide the necessary time and training. This

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turns out to be a specific advantage given to new and growing firms, as they have the ability to

recruit competent staff with skills in new technologies, instead of educating existing staff.

Respondent 3 gave an example: “Relatively new firms have the ability to adapt to digital

transformations more rapidly, as they have recruited employees with a digital mindset already”.

In conclusion, organisations do not seem to establish the prerequisites for their employees to

fully use analytical technologies. In literature, Marshall et al. (2015) have previously highlighted

how market leaders within analytics strive towards integrating analysis to all roles, providing all

employees with the knowledge and skills to achieve the benefits of data.

In terms of developing a digital strategy for capturing and analysing data, firms struggle to have

one master database (Respondents 1, 3). Instead, firms have several databases stored in various

departments which creates the problem of figuring out which database contains the most accurate

data. Respondents refer to this as master data strategies, in which an organisation should strive

for one common database for all departments. Respondent 1 explained: “A big issue for

managing data is that there can arise several similar version of it, firms need to form a strategy

to ensure the data is telling the same truth”. There needs to be a clear master data strategy, with

analytical capabilities set up in accordingly. Besides this, organisations need to establish

cross-functional teams. This implies setting up a group of employees which coordinates the

various departments. A respondent recalls the concept of “squads” where employees with

various competencies are put together, including at least one person with analytical skills

(Respondent 4). With highly diverse teams, analytical competence is brought closer to each

business department and the organisation can utilise its data analysis. Marshall et al. (2015) have

identified a similar structure amongst successful analytic firms, where teams consisting of

employees with multiple competencies are distributed across the whole firm. An ineffective (but

common) way of working with analytics, in contrast to the previous example above, is to have

one centralised analytic department that provides support. Although it becomes much easier to

establish a master data strategy, the centralised analytic department will find it difficult to

provide customised support across the firm (Respondents 1, 3).

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Another issue firms are experiencing is the lack of cooperation between departments

(Respondents 2, 3, 4). Cooperation and internal knowledge sharing are believed to be a question

of organisational cultural obstacles to achieving BA maturity, according to respondents.

Respondent 2: “Departments may not realise that sharing insights from analytics can help other

parts of the firms”. Respondent 3 on the same issue: “The lack of cooperation across

departments leads to a lot of double-work being done”. As discussed above, cross-functional

teams could perhaps improve internal knowledge sharing. Increased internal collaboration

between employees and departments have been emphasised by McAfee & Brynjolfsson (2012)

as a requirement for creating a data-driven organisation. Furthermore, a data-driven

organisational culture is in general difficult to establish, as the constant demand for data support

in decision-making can make firms slow and inflexible. This gives an indication that BAMM’s

Culture dimension could be hard to assess, as respondents identified that organisational culture

influences all the other organisational dimensions of the BAMM. The ‘right’ culture is believed

to be a prerequisite to creating a data-driven organisation.

5.2 Technical Factors Research has identified how the quality of information and data is positively related to higher

analytics maturity (Popovic et al., 2012). It is evident that organisations have moved away from

finding sufficient amounts of data, towards finding the relevant data. In the past, data would be

extracted from several sources and gathered into “data lakes” for analysis. However, due to

technological advancements, the amounts of data became overwhelming and the task of sorting

and extracting the right data to generate insightful reports became a challenge. Today, successful

data-driven firms have a different approach. They start with the end in mind, determining which

data insight they need and then collecting only the relevant data needed to generate the insight.

Additionally, respondent 4 stated: “Continuous feedback-loops are required to consistently

gather new data. Some of the more successful analytic companies have succeeded with exactly

this”. Firms conducting data-driven decision-making are not specifically worried about which

data insight they base their decision-making on (Respondents 2, 3). There seems to be a general

consensus that all decisions based on data will lead to an accurate decision. The large amounts of

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available data combined with firms’ general trust in the data insights is a factor which leads to

successful data analysis.

Respondents confirmed our suspicion about security and privacy issues being a concern for

Business Analytics, as indicated in the theory section 2.2. An increasing governmental pressure,

proposing new laws such as GDPR, creates worry about which type of data firms are allowed to

collect and conduct analysis on. However, the laws are hard to fully understand and due to the

penalty fees involved with not complying, organisations prefer to stay on the safe side by not

collecting any data which could lead to a penalty. Respondent 2 elaborated on the confusion of

the GDPR law: “This lack of knowledge and unnecessary worry can inhibit an effective data

analysis”. In other words, organisations are choosing to disregard data they suspect could lead to

a fine, even though the data could have been considered legal. This causes them to miss out on

valuable data insights. Data analytics is still a relatively new phenomenon and authorities are

lagging behind in setting up clear regulatory laws for legal data collection and analysis.

Another interesting finding was the firms’ use of internal and external data. Internal data is data

available from the organisation’s own resources, for instance, internet traffic on their own

website to identify customers’ buying patterns. External data refers to macro-related insights

from the industry as a whole, for instance, consumer trends and shifts in demands. Respondents

mentioned the pitfall of firms taking strategic decisions solely based on their internal data

(Respondents 2, 3, 4). Although internal data may be in support of a decision, internal data needs

to be combined with external data in order to take the best decision. Respondent 3 gave an

example from a clothing brand. The internal data from their website indicated that their red skirt

is the most popular amongst customers in terms of colours, therefore they should invest in

promoting the red skirt. However, external data may indicate that the general consumer demand

for skirts is decreasing across the fashion industry. With this external insight, the firm may think

twice before promoting their red skirt. This combination of internal and external data is believed

to be vital in performing data-driven decision-making. When discussing the topic of data analysis

in this thesis, we always relate to quantitative data. However, qualitative data can serve as a

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useful complement to quantitative internal and external data. For instance, respondent 4

mentioned that a global leader within analytics regularly interviews their customers to fully

understand their needs.

There are plenty of analytical tools available. As respondent 2 states: “There are many suppliers,

and many of them are good”. Some of the most used tools mentioned by respondents were:

Google Analytics, Zendesk, and Microsoft Dynamics. A majority of tools have similar functions

which indicate that choosing the ‘right’ tool for data analysis is not a particularly important

factor. The analytical tools are also easy to use and understand. As data analytics becomes more

and more prominent in organisational decision-making, there is an understanding amongst

organisations that data analysis requires significant infrastructure in terms of hardware and

software. Respondents believed that firms’ data processing capabilities have increased due to

recent advancements in cloud computing. Firms today benefit from economies-of-scale, as

analytical tools have been made available at affordable prices.

5.3 Modifications to the BAMM Going forward in the evaluation of the BAMM, the organisational dimensions will not be

modified prior to the second evaluation stage. Although there are indications that the Culture

dimension overlaps the other organisational dimensions, the support for this is considered as too

weak to make any changes, as only one respondent stated clearly that the dimension possibly

overlaps other dimensions (Respondent 2). However, this dimension will be under scrutiny when

analysing the results of the second stage and as we suggest further model adjustments.

Focusing on the technical dimensions from the first evaluation stage, some respondent insights

were of particular interest for the further development of the BAMM. No respondents valued the

dimensions of Analytical Tools and Infrastructure as particularly important dimensions to

achieve a high level of BA maturity. This was due to two reasons. Firstly, the analytical tools

available on the market are fairly similar to each other and they are all user-friendly. Secondly,

recent developments in cloud computing have resulted in a majority of firms moving towards

working with data in cloud-based solutions. This means less and less data needs to be stored in

local databases, such as on-premise server halls. Therefore, the infrastructure of analytical

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capabilities is less of an issue than it used to be before cloud computing. For these reasons, we

have chosen to merge the dimensions of Analytical Tools and Infrastructure into one dimension

labelled Infrastructure & Tools. By merging the dimensions, both dimensions are assessed but

the influence of them on the overall level of analytic maturity is reduced (see Figure 3).

Infrastructure & Tools is defined as follows: “Infrastructure is the technology’s hardware and the

necessary physical components of the computer which is needed to run the software. Tools refer

to the BA technology used for data analysis. For instance, how well the tool integrates with other

software systems in the organisation, the conversion of data into information through generation

of reports, the visualisation of data reports, and the more complex functionality which conducts

predictive and prescriptive analysis”.

Figure 3 - Modified version of the Business Analytic Maturity Model (BAMM)

The modification to the BAMM also leads to smaller modifications to the model’s

operationalised framework of statements. Within each dimension, some statements were added,

removed, or altered to better suit the model’s application to business analytics. Regarding the

statements for the new dimension Infrastructure & Tools, we merged the most important

statements, identified by the respondents, from the previous dimensions Infrastructure and

Analytical Tools. The updated operationalised framework used for creating the interview guide

for evaluation stage 2 of this thesis is presented in appendix 9.2.

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6. Evaluation by E-commerce Firms (stage 2) The first section of this chapter presents the results of the second evaluation stage. The results are separated into each of the BAMM’s dimensions, as the interviews were conducted with a similar structure. Each dimension presents a table with key findings from each firm. The succeeding section creates discussions regarding the results and the model’s dimensions. The chapter concludes with the last modification of the BAMM and the presentation of a final model version.

6.1 Results

6.1.1 Management Support

Respondent 5 ➢ Management has previous experience in e-commerce firms so they understand the importance of data analytics.

➢ Managers encourage new data initiatives amongst employees, especially if new initiatives can find support in data.

Respondent 6

➢ All major strategic decisions by management are taken based on data support. ➢ Business analytic initiatives are only encouraged by management if employees can back

their initiative with data support.

Respondent 7

➢ Management recognises data analysis as important, but decisions do not necessarily have to be supported by data.

➢ Business analytic initiatives amongst employees are encouraged by management, but no pressure is put on employees to conduct data analysis.

Respondent 8

➢ There has been a growing emphasis on data analysis from management during recent years. ➢ Business analytic initiatives are welcomed by management and the firm have increased their

data initiatives in recent years.

Respondent 9

➢ Management has a digital mindset and data-based decision-making is common in the firm. ➢ Management does not need to encourage using BA, as data initiatives occur naturally by all

departments.

Respondent 10

➢ Management understands the importance of data, but do not encourage data analyses. ➢ Business analytic initiatives are given support from management, but so are initiatives not

based on data.

All respondents mentioned that their top management in some way prioritises analytics. In

general, managers of the firms promote analytic initiatives, regardless of where in the

organisation the initiatives are generated from. New ideas may not necessarily be generated from

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the data itself, but in a majority of the interviewed firms, management demand that data should

either validate the idea or at least able to provide an opportunity of measuring (following-up) the

outcome of the idea (Respondents 6, 7, 8, 9). A few respondents have seen an increased focus on

analytics during the last two or three years (Respondents 5, 7, 8). Respondent 7 exemplified this:

“We have recently gone through a large transformation, and the new management is investing

many resources into our analytic function”. A reason for this transformation is believed to be the

previous e-commerce experience of newly recruited managers. In addition, these respondents

believed that managers with previous e-commerce experience are more competent to

successfully drive digital change in the organisation.

6.1.2 Employee Skills

Respondent 5 ➢ Data analytical competence exists among employees. However, the analysis is basic and do not require advanced analytical skills.

➢ The firm does not offer employees Business Analytics education courses.

Respondent 6

➢ Various employees attain various analytical skills, depending on their role in the firm and the type of analysis their department conducts.

➢ Internal education sessions are organised for data analysis, for instance, Google Analytics. Employees are given extra time to get to know the analysis tools.

Respondent 7

➢ There is a demand for more competent employees within analytics. IT-department is the only department with skills in analytics.

➢ The firm has a “learn as you go” mentality, rather than offering special education sessions. Employees are free to learn more about analytics by trying the tools themselves.

Respondent 8

➢ Employees have the right competencies for data analysis, but the firm has too few employees to utilise its analytical skills to its desired extent.

➢ No Business Analytics courses are offered, but employees are encouraged to attend external education sessions if they want to.

Respondent 9

➢ There is sufficient data analytics competence amongst employees. There exists an analytics team working cross-departmentally, providing additional support to employees.

➢ The firm tries to spark a data analytics interest amongst its employees and encourages employees to take their own BA education initiatives.

Respondent 10

➢ There is a general lack of data analytic competence amongst employees. This hinders the firm from conducting its desired level of data analysis.

➢ Education sessions are not offered but are welcomed at employees’ initiative.

Firms identified a lack of competence in analytics among employees (Respondents 7, 8, 10).

They believed it to some degree be possible to learn analytics from colleagues, however, there is

a general demand for staff with IT and analytical skills. Two respondents stated that they had the

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right competencies amongst staff, but the employees were too few in numbers (Respondents 7,

9). All respondents believed there should be some employees with analytical skills in all

departments, as it brings BA closer to all business functions. In one firm, which we identified as

highly mature within analytics, there was at least one employee with analytical skills within each

of the firm’s departments (Respondent 6). This particular firm also has an established strategy

for continuous development and education of analytical skills among employees, as they

combine external courses, for example, Google Analytics, with internal workshops to enable

knowledge sharing in the organisations. The respondent commented on how to develop the right

competencies: “We try to mix internal and external courses, but overall, we believe that the key

is to give the users enough time to understand the tools and how to perform data analysis”.

6.1.3 Enterprise Decision-making

Respondent 5 ➢ Decentralised organisational structure. No data analytics department which provides support across departments.

➢ Many decisions are taken on intuition and do not have to be supported by data. However, there is a desire to move towards data-driven decision-making.

Respondent 6

➢ Fairly decentralised organisational structure. No specific data analytics department exists but employees in each department are perceived to be sufficiently competent.

➢ Almost all strategic decisions are required to be based on data.

Respondent 7

➢ Centralised organisational structure. IT-department attains data analytical skills and provides support across departments.

➢ All decisions are encouraged to be supported by data, but employees can still make decisions based on intuition.

Respondent 8

➢ Fairly decentralised organisational structure. There is no specific department providing data analytical support across departments. A decoupled Business Intelligence department exists.

➢ Data-driven decision-making has recently become a requirement in the firm, as they are aiming to rely more on their data analysis in the future.

Respondent 9

➢ A centralised organisational structure with a data analytical department providing support to the rest of the organisation.

➢ Decisions are required to be supported by data. This has become a standard in the firm.

Respondent 10

➢ Fairly decentralised organisational structure. No data analytics department. IT-department is available for any data-related queries.

➢ Many decisions are taken on intuition and do not have to be supported by data. Employees are free to try new initiatives if they believe it will lead to improvements.

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A majority of respondents perceived their firms to use a data-driven approach in their

decision-making (Respondents 6, 7, 8, 9). In general, respondents’ firms encourages and

promotes new ideas in how to exploit data for decision-making purposes. For instance, one firm

has constructed a procurement algorithm that assesses the upcoming need for products and

automatically proposes new orders from its suppliers. Furthermore, even though some firms

demand supporting data to validate a new idea, as previously mentioned, some respondents

stated that their firms’ decision-making processes did still depend on intuition and gut feeling

(Respondents 5, 10). A reason for this was due to the fast-moving competitive market, which

puts pressure on e-commerce firms to remain agile and take instant decisions without having to

wait for a data analysis report to reveal the suggested decision. However, respondent 5 brought

up a different reason: “Most decisions are based on gut feeling, and it is due to the previous

working processes that take time to change”.

6.1.4 Strategy

Respondent 5 ➢ Each department develops its own KPIs. There is no shared digital strategy in the firm. ➢ No need for a master data strategy, as there is so little data analysis conducted by each

department.

Respondent 6

➢ Departments have their own KPIs. All KPIs are displayed on screens around the office for the entire firm to read and follow.

➢ The firm has a clear master data strategy.

Respondent 7

➢ Each department works with its own KPIs. The entire firm attends weekly meetings where all KPIs are communicated to the rest of the organisation.

➢ There is somewhat of a master data strategy. IT-department grant employees access to data on a request basis.

Respondent 8

➢ Management tries to implement common KPIs which suit several departments. Transparency between departments in terms of strategies and goals is important.

➢ There is a clear master data strategy. Business Intelligence department handles all data and is responsible for data strategies.

Respondent 9

➢ Each department currently has its own KPIs. However, the firm is aiming to align departments’ goals and strategies to create a mutual KPIs.

➢ There is a clear master data strategy handled by the data analytics department.

Respondent 10

➢ Each department work with their own KPIs. There is little knowledge of what goals and strategies other departments have.

➢ There is no strategy for who is responsible for the available data.

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A major challenge in forming strategies for analytics is to build KPIs that are aligned with the

remaining goals of the organisation, but also built on the same databases. Almost all respondents

stated how various organisational departments have different KPIs which they work towards, but

aligning these with the overall objectives of the organisation can be challenging (Respondents 5,

6, 7, 9, 10). Respondent 8 exemplified how they have handled this issue: “We have become more

transparent among departments, now we know which KPIs the market department is using for

instance”. Additionally, a clear master data strategy is perceived as vital for succeeding with a

digital strategy. If employees have access to data from different sources, it may imply that

employees perform the same type of analysis but retain different results (Respondents 5, 7, 10).

Lastly, displaying the organisation’s KPIs on screens around the office, such as live dashboards,

has proven beneficial to one of the interviewed firms. As a result, employees can stay updated on

their own and their colleagues' progress, for instance, sales and marketing figures. Thus,

analytics becomes an integrated part of employees’ daily routines.

6.1.5 Culture

Respondent 5 ➢ The organisation is small in size, so employees naturally encourage each other to work-data driven and try new initiatives.

➢ There is no data report sharing between departments.

Respondent 6

➢ Employees are committed to each other and there is a general willingness to cooperate with each other.

➢ The organisation works in smaller teams where data reports and insights are shared with the rest of the firm.

Respondent 7

➢ Employees are encouraged to try new initiatives, even though the initiative is not in support by data. Due to a flat organisational hierarchy, it is easy to contact management and request their support.

➢ Weekly meetings ensure that all departments stay updated on each other and the firm in general. However, data reports or insights are rarely shared between departments.

Respondent 8

➢ The firm aims for improved cooperation between departments, as there is currently little internal communication conducted. Employees are willing to cooperate with each other.

➢ Few data reports and insights are shared. The firm perceives this as an issue and aims to improve its knowledge sharing in general.

Respondent 9

➢ Employees feel encouraged to try new data initiatives and receives support from the data analytics department.

➢ The data analytic department generates many reports which they share with the relevant departments.

Respondent 10

➢ There is a welcoming organisational culture to cooperate and try new ideas. ➢ No data reports or insights are shared internally in the firm.

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During the interviews, it became evident that the characteristics of the Culture dimension partly

overlapped other dimensions. More specifically, all respondents identified organisational culture

as a prerequisite for the maturity of the other organisational dimensions. Without the right

organisational culture, supporting digitalisation and emphasising data-driven decision-making,

the maturity of the remaining organisational dimensions are likely to be low. For instance, an

important characteristic of organisational culture is cross-departmental cooperation. Two

respondents stated that they are trying to set up cross-functional teams to enable this cooperation

and knowledge sharing (Respondents 6, 9). Interestingly, the four remaining respondents first

answered that their firm had an “open-minded culture” where employees are encouraged to

generate new and innovative ideas of how to develop the business. However, the respondents

later emphasised the importance of cooperation across departments and admitted they still have

room for improvements in this area. Respondent 5 mentioned: “We use Slack to communicate

between departments, but apart from that, little communication or sharing of insights occur”.

6.1.6 Data

Respondent 5 ➢ The available data is relevant and high in quality. However, the data is rarely questioned by its relevance or quality. Each department handles its own data and all employees have access to the data.

➢ Privacy is not an issue as the firm never analyses sensitive consumer data.

Respondent 6 ➢ Data is structured and of high relevance and quality. ➢ There is always a demand for more consumer data but the firm is reluctant to overstep any

privacy laws.

Respondent 7 ➢ There are sufficient amounts of data available to generate insightful reports. The amounts of data are even overwhelming at times.

➢ The firm is careful to comply with all privacy laws and regulations.

Respondent 8 ➢ The firm has some issues with the quality of their data. They want increased customer segmentation in their data analysis.

➢ New GDPR-law has created organisational changes to make the firm compliant with the law.

Respondent 9 ➢ Data is of high relevance and quality. The firm is satisfied with the amount of data available to them.

➢ There is an implemented business process which regularly runs checks to ensure no sensitive consumer data is stored, to reduce the firm's privacy concerns.

Respondent 10 ➢ Data is perceived as high quality, but not always relevant. Employees would like more data in order to improve their data analysis.

➢ Privacy is no issue, the firm rarely analyses sensitive consumer data.

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All respondents believed they have access to sufficient amounts of data, there were no cases in

which more data was in demand. In fact, respondents identified the overflow of data as an issue.

Too much data makes it difficult to sort and retract relevant insights, complicating the data

analysis. As the majority of available data is already perceived as high quality there is no need to

search and collect more data. Respondent 9 mentioned an issue related to the velocity of data:

“The gathering of data takes a long time. Usually, the data is a day old, which complicates the

analysis”. Furthermore, BA experts from the first evaluation stage emphasised that firms’ should

not only collect internal data but also collect external- and qualitative data from other sources.

Interestingly, interviews revealed that few firms actually collect external- or qualitative data.

Instead, they prefer to focus on the data generated from their own sources, their internal data.

Only one respondent confirmed that their firm base their analysis on a combination of data from

internal and external sources (Respondent 5).

Surprisingly, few data reports and insights are shared internally between departments. Each

department is responsible for its own data analysis with little consideration for how some of their

data reports can be of interest for other departments in the firm. A reluctance to share reports

internally leads to “information silos” where departments work independently, becoming

detached from each other. Information silos decrease the efficiency of the firm as, in some cases,

detached departments can waste time creating almost identical reports.

There is a large consideration for the privacy concerns of data. Firms are nervous to overstep any

boundaries set by governmental laws, as the fine for disobedience of the GDPR-law has made

firms nervous about collecting customer data (Respondent 6, 7, 9). There is also a general

confusion over the law and as to which type of data is legal and not.

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6.1.7 Infrastructure & Tools

Respondent 5 ➢ No adjustments are made to the data analysis tool. The firm develops KPIs according to data reports automatically generated from their tool.

➢ The firm uses descriptive elements of analysis, but no predictive or prescriptive elements. ➢ The data analysis tool is not integrated with the firm’s other software systems.

Respondent 6 ➢ The firm makes adjustments to the data analysis tool to suit their goals and KPIs. ➢ The firm uses descriptive, predictive, and prescriptive elements of analysis. Big Data and

Artificial Intelligence solutions were mentioned. ➢ The data analysis tool is to some extent integrated with their other software systems. The

firm aims for higher future integration.

Respondent 7 ➢ Few adjustments are made to the data analysis tool. Adjustments require assistance from IT-department.

➢ The firm uses descriptive elements of analysis, but no predictive or prescriptive elements. ➢ The firm is reliant on very few software systems, which are well-integrated with each other.

Respondent 8 ➢ The firm is able to make adjustments to the data analysis tool to suit their set goals and KPIs.

➢ The firm uses descriptive elements of analysis. They are in the initial stages of predictive and prescriptive analysis, currently working on an Artificial Intelligence solution.

➢ The data analysis tool is not well-integrated with their other software systems.

Respondent 9 ➢ The firm adapts its data analysis tool to fit their goals and KPIs. ➢ The firm uses descriptive, predictive, and prescriptive elements of analysis. Machine

Learning and Artificial Intelligence solutions increase their prescriptive analysis in particular.

➢ Improvements can be made to integrate all their software systems. There is a demand for competent IT-staff to enable this integration.

Respondent 10 ➢ The firm rarely makes any changes/modifications to its data analytical tool. ➢ The firm uses descriptive elements of analysis, but no predictive or prescriptive elements. ➢ Few software systems are used by the firm. The systems are detached from each other,

working independently.

The analytical tools and cloud-based solutions (for storing and collecting data) used by the firms

are to a great extent perceived as sufficient enough (Respondents 5, 7, 8, 9). There was no

identified demand amongst respondents for new or more advanced analytical tools. In some

cases, firms adapt their goals towards whichever data analysis is provided by the tool

(Respondents 5, 10). In other cases, firms modify their tools to suit their set goals and the data

insights they demand (Respondents 6, 7, 8, 9). Making adjustments to the tools require a certain

level of competence amongst staff, and this level of competence varied across firms. Another

factor which varied across our respondents’ firms were the use of predictive analysis. All firms

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use descriptive analytics (data giving insights into the past, such as standard reporting, see

section 2.2). However, only a few firms use predictive analytics (data which identifies future

patterns and trends, see section 2.2) (Respondents 6, 9). The firms using predictive analysis are

utilizing more advanced technologies such as Big Data and Artificial Intelligence. Respondent 6

explained how to use predictive analytics: “We have tools that can predict the optimum level of

our warehouses, and always tell our procurement function how much of each product that we

need to buy”. Lastly, there is a general lack of integration between the analytical tools and other

software systems in the firms. In most cases, firms are using the software separately, but

respondents identified a desire towards integrating their systems (Respondents 5, 6, 8, 10).

However, firms lack the necessary time and resources to achieve this.

6.2 Discussions on BA Maturity in E-commerce Even though the e-commerce firms evidently had varying levels of BA maturity, there was to

some degree a collective digital mindset already implemented in all firms, as they all realise the

value of working data-driven. This can be explained by the fact that several of the firms are what

we have previously called ‘new’, meaning they are “born digital”, which our BA experts

identified as advantageous for achieving a high BA maturity. All firms had established some

type of strategy for becoming a data-driven organisation, which gives an indication of how

mature the Swedish e-commerce market is in general. However, there were still significant

differences between the firms regarding their use of BA, which gave us indications for what is

perceived as low and high analytic maturity.

All respondents mentioned how their firm strive towards becoming a data-driven organisation.

Davenport & Harris (2007) describes two approaches to achieving a high analytic maturity (see

section 3.3.1). Respondent 8 recalls how their firm previously only invested in small analytics

projects. However, as the firm replaced their top management with managers who were more

committed towards analytics, the firm started initiating larger analytics projects. Using

terminology from Davenport & Harris (2007) research, the firm switched approaches, from a

“prove-it detour” to a “full steam ahead” approach. The new approach is likely to infer a faster

way to a high BA maturity of this specific organisation (ibid).

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This thesis’ initial theory that organisational dimensions are of higher importance than technical

dimensions seems to find support from the interviews. Results discussions identified greater BA

obstacles in the discussions surrounding the organisational dimensions, rather than the technical.

From our results, we are able to extract examples of what constitutes a highly mature

organisation within BA. A mature organisation successfully integrates analytics and employee

analytic competence seamlessly in multiple processes and departments of the organisation. The

organisational structure is a vital factor. A mature organisation ensures that there are analytic

competencies amongst employees in teams and departments, while also providing the time and

resources for employees to perform analysis. There need to be employees with analytic

competencies and previous data experiences placed in all business departments, combined with

an additional smaller department specialising in analytics, serving to support the rest of the

departments. Davenport & Harris (2007) especially highlighted the importance of in-house

analytical competencies, which we believe is aligned with the results of our study. Spreading out

analytic competences over the organisation (rather than clustering competences in one

specialised data analytic department) increases the analytic maturity of the entire firm. This is

also dependent on the degree of centralisation in the organisation. The interviewed firms had

varying levels of centralisation and it became evident that firms with a greater degree of

decentralisation are able to achieve a higher level of maturity. In a decentralised organisation,

data-driven decisions can be taken by employees closer to the business function. This enables

faster and more accurate decisions.

In order to increase analytic competence amongst employees in the organisation to achieve a

higher BA maturity, firms can either offer internal education sessions for existing employees or

recruit new staff with analytic experience. As a majority of our interviewed firms were aiming to

expand and grow, there were no issues in hiring new employees specialised in analytics.

However, respondents also mentioned that offering education sessions in analytics to existing

employees was effective. In fact, by increasing employees’ curiosity of data analytics, employees

become internally motivated to learn more about the technology, which eventually increases the

analytic competencies in the organisation.

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Creating algorithms that perform predictive analytics is another way to increase BA maturity, as

analytics becomes integrated into various business functions. In turn, these developed internal

resources will provide the firm with competitive advantages (Barney, 1991). As recently

mentioned, one firm revealed how predictive analytics had significantly streamlined its

procurement function. It is therefore evident that integration of algorithms for predictive

analytics can add additional value for the firm and enhance both operational-, as well as financial

performance. As discussed in section 2.2, we define Business Analytics as an umbrella term

including several concepts of analytic capabilities, such as “Business Intelligence” and “Big

Data”. Interviews revealed that the specific type of analytic element turned out to be of greater

importance than we initially expected when assessing Business Analytics maturity. Although

respondents all use analytics in various ways, most only use the descriptive elements of BA.

Only a few firms used more complex analytic capabilities with predictive and prescriptive

elements. Our BA experts stated that predictive and prescriptive analytics is often labelled as

“machine learning”, “artificial intelligence” or “advanced analytics”, and these types of analytic

capabilities are far harder to implement than descriptive analytics (e.g. Google Analytics).

However, predictive and prescriptive capabilities also generate more insightful data analysis and

can provide organisations with sustained competitive advantages, i.e a higher BA maturity

(Ransbotham et al., 2015; Wamba et al., 2015). This is supported by the respondent’s firm using

the procurement algorithm. Regarding the generalisability of this finding, using more complex

analytic capabilities to achieve a higher maturity may not be exclusive to the e-commerce sector,

as firms from other industries may also benefit from predictive and prescriptive elements. To

conclude, it is evident that the specific type of analytic element used by the firm has a larger

effect on its overall BA maturity than initially expected. This gives cause for another

modification to our BAMM.

As suspected, a digital strategy is vital for BA maturity. However, to achieve high maturity, the

digital strategy must be embraced and incorporated throughout the organisation. In other words,

the strategy must not be decoupled or solely applied to the analytic department (if there exists

one). All organisational departments must be made aware of the firm’s aim to work data-driven.

Furthermore, data-based KPIs should be applied to each department if possible.

52

Although the organisational dimensions prove to have a more significant role, interviews

presented several noteworthy insights into the technical dimensions of BA maturity. As BA

experts previously indicated, the combination of internal and external data for analysis is

important. By excluding external data, firms risk an ineffective data analysis. For instance, solely

focusing on internal data can cause firms to miss out on new market opportunities. By collecting

external data, firms keep an eye on market trends and competitors. To ensure a high BA

maturity, firms must find ways to combine their internal and external data insights, as well as a

compliment with qualitative data. Additionally, firms strive to improve customer relationships by

collecting customer data to understand their consumer behaviour across multiple channels. This

can be achieved with a master data strategy (Shankar, 2011). Firms must also make sure that

their data is of high quality and accessible to the entire organisation. A master data strategy

includes clear directives of how the firm’s databases are configured, how to access the most

accurate and updated data set, and who has access to the data. An unclear master data strategy

leads to confusion regarding the ‘right’ data, which, in turn, obstruct the firm’s analytics

capabilities. Furthermore, all respondents believed that they gather sufficient amounts of data.

However, as BA experts highlighted, businesses need to be in a constant strive for new sources

of data to make informed decisions. These results indicate that e-commerce firms may not

question their available data and do not examine new potential sources of data.

Both data evaluation stages revealed the Culture dimension to be of specific interest as we

further develop our BAMM. While interviewing respondents surrounding the organisational

dimensions, we noticed how respondents kept relating to descriptions of organisational culture.

Hence, it became evident that the cultural aspects of a firm influences all other organisational

dimensions of BA maturity, a typical interdimensional relationship. For instance, high maturity

in the Management Support dimension is only achieved through management’s encouragement

of analytic initiatives, a characteristic which is strongly associated with the existing

organisational culture of the firm. This implies that Culture acts as an enabler for the other

organisational dimensions. The case of General Electric’s digital transformation also provides

real-life evidence for this (Ferguson, 2014). A probable cause for why we initially perceived our

interviewed e-commerce firms as well-developed within BA could be that the firms are relatively

53

‘new’ and small, and have therefore not inherited an old culture. The issue of inheriting culture

can be defined as ‘organisational inertia’ and is one of the main obstacles to adopting new digital

ways of working (Radovic, 2008).

There are also indications of an interdimensional relationship between Strategy and Data. For

instance, BA experts previously highlighted the importance of forming explicit strategies for

how organisations handle data and data access. Respondents emphasised how their firms have

become aware of privacy risks. However, they are struggling with implementing master data

strategies across the organisation, which in turn has an impact on how they handle their data. A

third interdimensional relationship worth mentioning is how Employee Skills could impact the

maturity of Infrastructure & Tools. Employees competent in analytics are able to extract more

valuable insights from the analytical tools, as they have a better understanding of how to adjust

the tools to fit the firm’s goals and objectives. Firm’s ability to adjust the analytical tools is an

indication of high maturity. Although we have identified these signs of interdimensional

relationships, they are judged as too vague to have an impact on the design of the final BAMM.

6.3 Final Modifications to the BAMM To further adapt the BAMM towards its application on the e-commerce industry, we propose

another modification where the Culture dimension become integrated into all other

organisational dimensions. As discussed, the aspects of organisational culture in e-commerce

organisations seem to be integrated into all other aspects of organisational dimensions of

Business Analytics, as it acts as an enabler for the dimensions. Respondents consistently

mentioned organisational culture when answering questions regarding all other organisational

dimensions. As a result, there is no cause to assess Culture as equally important as the other

dimensions. It is vital to emphasise that the Culture dimension is not removed from the BA

assessment. In contrast, the dimension is so essential that it affects all other organisational

dimensions and should therefore not be regarded as an equal. This change implicates smaller

changes in some of the statements in the final operationalised framework. The specific

statements which previously regarded the Culture are distributed over the other organisational

dimensions statements, see appendix 9.4.

54

In its current state, the BAMM does not take into consideration as to which type of analytic

element is used by the firm; descriptive, prescriptive or predictive. E-commerce firms revealed

that most firms are only using descriptive elements and only a few firms use the more advanced

elements like prescriptive or predictive. In support of this, BA experts proposed that there is a

significant difference between analytical elements, as it affects the overall BA maturity of the

firm. Solely relying on descriptive analytic elements indicates a lower BA maturity level, while

predictive and prescriptive elements indicate a higher BA maturity (Raguseo, 2018; Ransbotham

et al., 2015; Wamba et al., 2015). Due to this, we deemed it appropriate to add these findings into

our BAMM by adding a new technical dimension called Analytical Elements. This thesis defines

Analytical Elements as: “The type of data analysis capability used by the firm, assessing whether

data analysis reveals present insights or predicts future insights. The type of analytical elements

indicates the complexity of the data analysis in the firm. The analytical elements are descriptive,

prescriptive or predictive”. The dimension will assess to what degree a firm is using predictive

elements of analytics, and to what degree the firm has implemented algorithms that automatically

perform analytical tasks. To empirically assess this dimension, we altered statements in our

operationalised framework in accordance with the BAMM modification, making sure the

Analytical Elements is weighed equally important as the other dimensions.

Figure 4 - Final version of the Business Analytic Maturity Model (BAMM)

55

7. Conclusions With the use of Becker et al.’s (2009) 8 requirements for developing a maturity model, this thesis

has created a Business Analytics Maturity Model (BAMM) and adjusted it towards an

application on the e-commerce industry. The initial intent of the thesis was to some degree defy

previous analytics maturity models, which tended to have a larger focus on the technical factors

of BA maturity rather than organisational factors. This was due to the fact that a majority of

previous models are created by IT-consulting firms or other professional vendors. These models

could be considered biased, as the creators have an underlying intent of selling their IT-services.

In this thesis we argue against this previous negligence of the organisational factors to achieving

BA maturity by creating our own model with an academic focus, emphasising the organisational

factors. Therefore, we fill an identified gap of research. The foundation of our BAMM stems

from the Resource-Based View and is built on the few academic studies available within

analytics. The model has undergone two evaluation stages to increase its validity. First, amongst

four BA experts with experience of analytics. Second, amongst data analysts from six Swedish

e-commerce firms. The evaluation stages have led to several modifications to the BAMM’s

dimensions. Continuous modifications have enabled the BAMM to go from a broad- and general

model, to a narrow- and specific model with a focus on both BA and e-commerce. The thesis has

therefore answered the research question of what is a suitable Business Analytics Maturity

Model for analysing organisations analytical capabilities and data usage within Swedish

e-commerce firms.

We can confidently confirm our initial assumption that organisational factors are more important

than previous research suggests. Our results indicate that organisational factors should be in

greater focus for maturity models assessing Business Analytics within organisations in

e-commerce. This implies that firms striving to achieve a high BA maturity level needs to

primarily direct their efforts towards the organisational factors presented in this thesis. In

addition to this confirmation of our initial assumption, the thesis’ creation of the BAMM has

generated several other key findings which are worthy of highlighting, discussed in the following

sections. These findings had an effect on the results of the thesis and partly contributed to the

final design of the BAMM.

56

Firstly, analytical tools have less effect on BA maturity than initially expected. Our research

revealed that the type of analytical tool used for data analysis is not significant when assessing

BA maturity in a firm. This insight was generated from interviews with BA experts. As Business

Analytics has seen increased popularity in recent years, the various available analytical tools

have also increased. Additionally, the tools available are well-developed in terms of functionality

and user-friendliness. Analytical tools (such as Google Analytics and Qliksense) have improved

up to the point that there are little differences between the tools. Consequently, which specific

analytical tool a firm chooses to conduct its data analysis is insignificant to assess its BA

maturity.

Secondly, Culture was initially one of BAMM’s organisational dimensions in early creation

stages of the model. However, interviews revealed that organisational culture is so crucial in

achieving BA maturity, that it even acts as an enabler for the other organisational dimensions.

This was evident from both our data collection stages. Without an organisational culture which

supports data-driven decision-making, firms will achieve low maturity in the rest of the

organisational dimensions. This finding implied that the Culture dimension was overlapping all

other organisational dimensions, which meant it should not be assessed equally to the

dimensions. Therefore, it was deemed appropriate to integrate the dimension into the other

organisational dimensions. The interview questions and statements used to assess organisational

culture are distributed to other suitable organisational dimensions, making sure culture is still

part of the maturity assessment.

Interviews with Swedish e-commerce firms revealed that industry overall seems to be relatively

mature within BA. For instance, all firms had allocated staff who specifically worked with data

analysis, generating data reports to support their decision-making. However, interviews also

revealed that the BA maturity of a firm is to a great extent dependent on the type of analytical

element being used. Descriptive elements (insights into the past, such as standard reporting) are

less advanced elements of data analytics in comparison to predictive or prescriptive (insights into

the possible future). All interviewed firms use descriptive elements of analytics but only a few

57

use predictive or prescriptive elements. The type of analytical element has a greater impact on

overall BA maturity than we initially expected, and therefore it needed to be included in the

model. Consequently, Analytical Elements was added as a new technical dimension to the

BAMM. In summary, e-commerce firms all hold a certain level of maturity as they use

descriptive elements, but the maturity between the firms varies with regards to their predictive or

prescriptive capabilities.

Comparing BA maturity between an e-commerce firm and a traditional brick and mortar retailer

would be misleading. However, we believe e-commerce firms can be compared to each other,

regardless of the market. We state this because our interviews revealed that all e-commerce firms

have similar organisational structures, and their data usage does not depend on their market. In

addition to this, we do not, however, believe this thesis’ BAMM can be directly generalised to

other industries outside e-commerce. After our second evaluation stage amongst e-commerce

firms, we had to make several (e-commerce specific) modifications to the BAMM which had not

previously been identified. This indicates that the use of Business Analytics analytics varies

across industries, which is why we condemn to using our BAMM on other industries besides

e-commerce.

In its current state, the BAMM holds a descriptive purpose, as it can be used as a diagnostic tool

to determine a firm’s current maturity (Becker et al., 2009). To give the model a comparative

purpose, the model needs to be quantified to establish each maturity level. In other words, the

reliability of the scoring scale needs to be validated on a large number of assessments with

several firms (Comuzzi & Patel, 2016). By doing this, the model provides the ability to

benchmark results across the industry to find and compare best practice (Poeppelbuss &

Roeglinger, 2012). The next section 7.1 explains this process further.

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7.1 Suggestions for Further Research During the process of creating the BAMM, we discovered how crucial it is to adjust the maturity

model towards its specific application area. The model’s dimensions need to be thoroughly

validated to ensure they are suitable to accurately assess the intended maturity. The final version

of the thesis’ BAMM has been adapted to the specific context of Business Analytics within

e-commerce and is from here on ready for quantitative data gathering. The suggested next step of

research from this thesis is, therefore, to apply the BAMM on a sufficient amount of e-commerce

firms to form an industry benchmark towards what is regarded as high and low maturity. From

there, the model can be used by other e-commerce firms to benchmark their own BA maturity in

comparison with their competitors.

IT-consulting firms who have created similar maturity models have named their different

maturity levels with buzzwords such as “Visionary” or “Symphony”. We believe this only

creates confusions. Regarding the naming of the levels of the BAMM, we suggest the levels to

simply be labelled 1-5. Furthermore, the maturity score for each dimension should be kept

separate and not merged into one average number for all seven dimensions. As of now, the

created BAMM with its supplementing survey (see appendix 9.4) is ready to be used by

organisations that want to test their own BA maturity. As the survey is based on individual

perceptions of the firm’s capabilities, we recommend the survey to be completed by multiple

employees working with analytics within the e-commerce firm, as it will increase the accuracy of

the results.

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9. Appendix 9.1 Operationalised Framework based on Previous Academic Models

Organisational factors

Management Support

● Senior managers view analytics as a strategic initiative at your organisation.

(Davenport & Harris, 2017)

● Management encourages and promotes business analytics initiatives at your organisation.

(Davenport & Harris, 2017)

● There is constructive communication between the organisation managers and the staff which is helpful and supportive.

(Tavallaei et al., 2015)

● Managers have a high competence within change management and are able to drive organisational change to improve analytical capabilities.

(Cosic et al., 2015)

Employee Skills

● Your organisation has acquired the necessary human resources (e.g. data analysts, data scientists, and/or quantitative managers) for business analytics.

(Cosic et al., 2015)

Enterprise Decision- making

● A centralized group exists with primary responsibility for business analytics at your organisation.

(Cosic et al., 2015; Davenport & Harris, 2017)

● The decision-making of the organisation is not influenced by the hierarchy of the organisation.

(Tavallei et al., 2015)

● Business analytics is integrated into business process improvement and reengineering at your organisation.

(Davenport & Harris, 2017)

Strategy ● Analytics efforts are coordinated across various business functions in your organisation.

(Davenport & Harris, 2017)

● Your organisation routinely uses analytics to develop competitive advantages.

(Davenport & Harris, 2017)

● Your organisation’s strategic focuses are grounded in analytics. (Davenport & Harris, 2017)

● IT supports and cooperates in business analytics efforts in your organisation.

(Davenport & Harris, 2017)

Culture ● Business functional areas embrace business analytics in your organisation.

(Davenport & Harris, 2017)

● Knowledge & data sharing is embedded in the organisational culture (Davenport & Harris, 2017)

● The organisation promotes entrepreneurship and innovative ideas on how to exploit data.

(Cosic et al., 2015)

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Technical factors

Data ● Relevant and high-quality data are made available for business analytics at your organisation.

(Davenport & Harris, 2017)

● Data & reports are shared internally so multiple departments can make use of the data

(Davenport & Harris, 2017)

● The organisation does consider data privacy concerns. (Davenport & Harris, 2017)

Analytical Tools

● Your organisation has enhanced its products/services through analytical tools and practices.

(Davenport & Harris, 2017)

● A wide range of fit-for-purpose business analytics technologies has been pervasively adopted at your organisation.

(Cosic et al., 2015)

● Technology presents a clear visualisation of data and analysis. “Visualisation tools”

(Cosic et al., 2015)

● The organisation use predictive and prescriptive elements of analytics (Davenport & Harris, 2017)

Infrastructure ● Your organisation has acquired the necessary technological resources (e.g. data warehouses and analytical tools) for business analytics.

(Cosic et al., 2015)

● Is there available infrastructure to scale up data analysis in the organisation?

(Davenport & Harris, 2017)

● The data analysis technology is well integrated with other systems which support its use.

(Tavallaei et al., 2015)

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9.2 Operationalised Framework (prior to stage 2)

Organisational factors

Management Support

● Management encourages and promotes business analytics initiatives at the organisation.

(Davenport & Harris, 2017), Respondent #1, 2, 3

● There is constructive communication between the organisation managers and the staff which is helpful and supportive.

(Tavallaei et al., 2015) & Respondent #1

● Managers have a high competence within change management and are able to drive organisational change to improve analytical capabilities.

(Cosic et al., 2015) & Respondent #1, 4

Employee Skills

● The organisation has acquired the necessary human resources (e.g. data analysts, data scientists, and/or quantitative managers) for business analytics.

(Cosic et al., 2015)

● Learning and education opportunities available for updating digital skills in the organisation

Respondent #1, 2

● The organisation continuously recruits new competencies within analytics by a demand to meet new technological innovations

Respondent #3

Enterprise Decision- making

● The business analytics department within the organisation provides support across the organisation.

(Cosic et al., 2015; Davenport & Harris, 2017), Respondent #2, #3

● Major strategic decisions are only taken if supported by data.

Respondent #4

● The organisational promotes unconventional and new ideas on how to exploit data.

(Cosic et al., 2015) & (Tavallaei et al., 2015)

● Business analytics is integrated into business process improvement and reengineering at the organisation.

(Davenport & Harris, 2017), Respondent #1

Strategy ● Analytics efforts are coordinated and cooperated across various business functions in the organisation.

(Davenport & Harris, 2017), Respondent #1, 2, 3

● Your organisation’s strategic focuses are grounded in analytics.

(Davenport & Harris, 2017), Respondent #2, 4

● The ROI of the organisation’s business analytics initiatives is measured.

Respondent #1, 2, 3

● KPIs and organisational objectives/goals are clearly set out to be achieved through analytics

Respondent #2, 4,

● A master data governance strategy is well established across the organisation

Respondent #1, 3

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Culture ● Employees are given the necessary prerequisites to embrace business analytics

(Davenport & Harris, 2017), Respondent #1

● Willingness to cooperate and experiment is a cultural norm in the organisation

Respondent #2, 4

● Knowledge & data sharing is embedded in the organisational culture

(Davenport & Harris, 2017) & Respondent #2, 3, 4

Technical factors

Data ● Relevant and high-quality data is made available for business analytics at your organisation.

(Davenport & Harris, 2017), Respondent #1, #3

● Data & reports are made accessible for all employees to make use of.

(Davenport & Harris, 2017), Respondent #1, 2, 3, 4

● The organisation strives towards collecting data from various sources (external, qualitative etc.)

Respondent #3, 4

● The organisation does consider data privacy concerns. (Davenport & Harris, 2017) & Respondent #1, 2

Infrastructure & Tools

● The analytical tools are fit-for-purpose towards its business needs

(Cosic et al., 2015) & Respondent #1, 2

● The organisation use predictive and prescriptive elements of analytics

(Davenport & Harris, 2017), Respondent #1

● Your organisation has acquired the necessary technological resources (e.g. data warehouses and analytical tools) for business analytics.

(Cosic et al., 2015), Respondent #1, 2

● The analytical tools are well integrated with other systems which support its use.

(Tavallaei et al., 2015), Respondent #1, 3, 4

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9.3 Interview Guide (stage 2) General information & introduction

● The respondent was assured full anonymity for themselves and their organisation.

● The respondent was asked to tell us about their role and responsibility at the organisation.

● The interviewers explained the aim of the study and the structure of the interview.

Organisational factors

(Follow-up question, if needed) = To ensure the interview ran smoothly, these questions were only asked if required. In most cases, the dimensions were covered using only 1-2 questions.

Management Support

1. Does management encourage and promote business analytics initiatives?

2. Is there constructive communication between your organisation’s managers and the staff which is helpful and supportive?

3. Follow-up question: Do managers have a high competence within change management and are able to drive organisational change to improve analytical capabilities?

Employee Skills 4. Has your organisation acquired the necessary human resources (e.g. data analysts, data scientists, and/or quantitative managers) for business analytics?

5. Are learning and education opportunities available for staff in order to update digital skills?

6. Follow-up question: Is your firm continuously recruiting new competencies within analytics to meet new technological innovations?

Enterprise Decision- making

7. Is the business analytics department, if there exists one, providing support across the whole organisation?

8. Are major strategic decisions only taken if it can be supported by data?

9. Follow-up question: Is your organisation promoting unconventional and new ideas in how to exploit data?

10. Follow-up question: Is business analytics integrated into business process improvement and reengineering at your organisation?

Strategy 11. Is your organisation’s strategic focus grounded in analytics?

12. Are KPIs and organisational objectives/goals clearly set out to be achieved through analytics?

13. Is there a well-established master data governance strategy across your organisation?

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14. Follow-up question: Are analytics efforts are coordinated and cooperated across various business functions in your organisation?

15. Follow-up question: Is the ROI of the organisation’s business analytics initiatives measured?

Culture 16. Is a willingness to cooperate and experiment a cultural norm in your organisation?

17. Is knowledge & data sharing embedded in your organisational culture?

18. Follow-up question: Are employees given the necessary prerequisites to embrace business analytics?

Technical factors

Data 19. Is relevant and high-quality data made available for business analytics at your organisation?

20. Are data and reports made accessible for all employees to make use of?

21. Does your organisation strive towards collecting data from various sources (external, qualitative etc.)?

22. Follow-up question: Is the organisation considering data privacy concerns? To what degree?

Infrastructure & Tools

23. Are the analytical tools fit-for-purpose towards its business needs?

24. Does your organisation use predictive and prescriptive elements of analytics?

25. Has your organisation acquired the necessary technological resources (e.g. data warehouses and analytical tools) for business analytics?

26. Follow-up question: Are the analytical tools well integrated with other systems which support its use?

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9.4 Survey Business Analytics Maturity within E-commerce

Organisational factors To what extent do you agree? (1=Not at all, 5=Fully agree, N=Not sure)

Management Support

● Management encourages and promotes business analytics initiatives at the organisation.

● Sharing of analytical knowledge is encouraged at the organisation.

● There is constructive communication between the organisation managers and the staff which is helpful and supportive.

● Managers have a high competence within change management and are able to drive organisational change to improve analytical capabilities.

● Employees are given the necessary prerequisites to embrace business analytics.

Employee Skills

● The organisation has acquired the necessary human resources (e.g. data analysts, data scientists, and/or quantitative managers) for business analytics.

● Learning and education opportunities are available for updating digital skills in the organisation.

● The organisation continuously recruits new competencies within analytics to meet new technological innovations.

Enterprise Decision- making

● The business analytics department within the organisation provides support across the organisation.

● Major strategic decisions are only taken if it can be supported by data.

● The organisation promotes unconventional and new ideas on how to exploit data.

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● Business analytics is integrated into business process improvement and reengineering at the organisation.

Strategy ● Analytics efforts are coordinated and cooperated across various business functions in the organisation.

● The organisation’s strategic focuses are grounded in analytics.

● The ROI of the organisation’s business analytics initiatives is measured.

● KPIs and organisational objectives/goals are clearly set out to be achieved through analytics,

● A master data governance strategy is well established across the organisation.

Technical factors To what extent do you agree? (1=Not at all, 5=Fully agree, N=Not sure)

Data ● Relevant and high-quality data is made available for business analytics at the organisation.

● Data and reports are made accessible for all employees to make use of.

● The organisation strives towards collecting data from various sources (external, qualitative etc).

● The organisation does consider data privacy concerns.

Infrastructure & Tools

● The analytical tools are fit-for-purpose towards its business needs.

● The organisation has acquired the necessary technological resources (e.g. data warehouses and analytical tools) for business analytics.

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● The analytical tools are well integrated with other systems which support its use.

Analytical Elements

● The organisation use predictive and prescriptive elements of analytics.

● The organisation has implemented algorithms that automatically perform analytical tasks.

9.5 List of Tables and Figures

Table 1: BA Maturity Models for academic research

Table 2: BA Maturity Models by professional vendors and IT-consulting firms

Table 3: Summary of stage 1 interviews

Table 4: Summary of stage 2 interviews

Figure 1: BAMM dimensions created from previous Maturity Models by Davenport & Harris

(2017), Cosic et al. (2015) and Tavallaei et al. (2015)

Figure 2: First version of the Business Analytics Maturity Model (BAMM)

Figure 3: Modified version of the Business Analytic Maturity Model (BAMM).

Figure 4: Final version of the Business Analytic Maturity Model (BAMM).

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