Business Analytics for Managers - Taking Business Intelligence Beyond reporting

Business Analytics for Managers- Taking Business Intelligence Beyond reporting

All the way from strategy to data sources, this book will give you guidance on how to work with data warehouse information.

Read more at www.ba-support.com

Sample chapter:http://support.sas.com/publishing/pubcat/chaps/62842.pdf

http://support.sas.com/publishing/pubcat/chaps/62842.pdf

Introduction

Purpose of the PowerPoint… These slides can be used for teaching: ”Business Analytics for Managers: Taking Business

Intelligence Beyond Reporting” We don’t imagine that the slides are covering everything, but they are selected based on the

criteria below:

These slides contain the essential models of the chapters In some cases exercises are suggested and examples included

Introduction

Purpose of the book…

A guide to fuel what we refer to as the analytical age The ability to make an information strategy An understanding of Business Analytics (BA) as a holistic information discipline An understanding of the ever increasing role of BA A reference to most used BI concepts, definitions and terms

Chapter Introduction

Content What is business Intelligence?

Defining the term

The difference between BI and analytics Defining more terms

What is an information system? Business Intelligence is much more than a technical disciplin

What is Business Intelligence

Two popular definitions are: ”Decision support” and ”The process of providing the right people the right information at the right time”

Which customers should we send reminders (Credit department) What advertising should we send to which customers (CRM) What type of employees have the highest absence (HR) Information about which products and customer segments are most profitable and

therefore deserves focus in the future (Marketing)

This book definition of BI is a combination of the two definitions:

”Delivering the right decision support to the right people at the right time”

Introduction

What is Business Intelligence

This book focuses on information from data warehouses, however the same decision could also be based on e.g. survey data, interviews with subject matter experts or external consultants.

In general however, it is not the source that is important, it is too which degree it enables the right persons to take the right decisions.

Good decisions are made; if the decision maker uses all the relevant input and analyzes this correctly at the time of the decision.

Business intelligence is typical humans taking actions based on IT system input. IT automation is typical when IT systems take actions based on IT system input.

Another definition could be: decision support used for business decisions.

Introduction

The difference between BI and Analytics

Types of information (BA vs. BI)… This book typically takes it focus on processes – that is the routines we do and how they

interlink. Processes typically evolves in steps over time

Horizontally when we manage it Vertically when we improve it The blue area represents a state

less than optimal Can also be seen as the information

we need before we run a new process vs. the information we useduring the process. Later in the bookwe will look at learning information,which is information we use after wehave learned from a process. E.g. amarketing campaign.

Introduction

Process Performance

What is an information system?

BI-department produce information, but that in itself is not value creating. Value is not created until people throughout the organization improve upon their decision making and business processes

BI should be seen as a part of an information system Business processes Technical solutions Human competencies

Introduction


A value creating information system is characterized by three elements:

1. Some specific business processes are optimized by improved decision making – compared to a situation without an information system

People must act differently and more efficient than they used to, before value is created by using information. You can say that people must improve upon their work processes, that means the way they act in daily work routines

2. The information system contains a technological element that collect, store and deliver information

It can be IT-based, but also paper, papyrus scrolls, yellow sticky notes or heads with good memories3. Human competencies form part of the information system to

Someone must be able to retrieve data and deliver it as information in, say a front-end system. Even more important, those who make the decisions, those who potentially should change their behavior based on the decision support, are people who must be able to grasp the decision support handed to them

Introduction

Jesper Thorlund


When you create a new information system the order is the same:

1. Identify how you want the business process to work – who should do what and when, in which order etc.

2. Then create an information system that can deliver the right decision support to the right people at the right time

3. Train users in how to work the new process, including how to retrieve and use the technical decision support system

Introduction

Chapter 1

Content The BA model

Presenting the central concept in this book

An example Make it more concrete

The BA modelChapter 1

Structure of the book The BA-model – from strategy

to data sourcesTHE BA MODEL

The BA modelChapter 1

Structure of the book

The models five phases each of which is allocated a separate chapter Ch. 2: The relationship between business strategies. The overall competitive position and strategy of the

company set requirements for the information to be delivered and used

Ch. 3: Based on the overall company strategy, requirements for information deliverables are specified at the functional level , so they are able to reach their objectives. Business processes are improved in this area.

Ch. 4: How data, information and knowledge is created by analysts.

Ch. 5: How information is stored over time in a data warehouse

Ch. 6: What sources systems typically deliver data to a data warehouse

An exampleChapter 1

Chapter 2 – Strategic level

Content Strategy and information

Strategy is about solving short term issues and gaining long term competencies. Competing in the information age means that you in the strategy creation process also must be aware of how information can help you.

The relationship between strategy and business intelligence Varying degrees of integrating between company strategy and the usage of information

Which information do we prioritize?

Strategy and informationChapter 2

Postulate: ”A company’s core competencies lie in the field of knowing how to handle internal processes and knowing what customers want now and in the future”

These competencies include things the company is especially good at, and which can secure its survival, also an organization should be capable of evolving these competencies to meet the future requirements in the marketplace

The point is, that companies competes on continuous creation of knowledge and the ability to execute based on it

The relationship between strategy and business intelligence

Chapter 2

Separated: In these companies, data are not used for decision making on a strategic level. The BI-function only works on ad hoc basis. No prioritization in regard to what is relevant for the strategy.

Coordinated: The BI function performs monitoring of individual functions’ achievement of by using Dash boards etc. BI is use reactively to monitor the execution of strategy.

Dialogue: A learning loop is facilitated when the BI function is reporting on business targets. Analyses create learning from the differences between targets and actual. BI is used proactively to improve operational processes when executingthe company strategy.

Holistic: Information is being treated as a strategic asset, which can be used to determine company strategy. The organization constantly evaluates howit can compete on information when strategies are created.


Chapter 2

Coordinated

The BI function performs monitoring of individual functions’ achievement by using Dash boards etc.

Here the BI function is a reactive element, solely employed in connection with the monitoring of whether the defined targets of the strategy are achieved (performance management)


Chapter 2

Dialogue

A learning loop is facilitated when the BI function is reporting on business targets.

Analyzing the differences between targets and actual create learning

BI is used proactively to improve upon operational processes when executing the company strategy.


Chapter 2

Holistic

Information is being treated as a strategic asset, which can be used to determine company strategy. How information, in combination with strategies, can give them a competitive advantage

Competing on Analytics (Davenport) describes how a company can use information as a strategic asset/resource

Which information do we prioritize?Chapter 2

The strategy part of the book is primarily inspired by Treacy & Wiersema’s article: Treacy, M. & Wiersema, F. (1993) Customer Intimacy and other Value Disciplines, Harvard Business Review, Jan/Feb

The article explains that companies compete on the dimensions:1. Process Excellence (efficient in relation to production and delivery services, and which always

focuses on optimizing internal processes)2. Customer Intimacy (strong customer relations, that is, about being able to establish a psychological

connection to customers)3. Product innovation (strong in the field of product innovation and being a leading supplier of ’state

of the art’ products) If a company masters one of the three disciplines and matches its competition on the two others it

can become a market leader

Which information do we prioritize?Chapter 2

Discuss which information for the following companies would be expected to focus on when making a company strategy: Dell – low cost web distribution of products Apple – innovative and customer oriented Siemens – world leading wind mills Hennes & Mauritz, Boss and Wal-Mart clothing stores.

Chapter 3 – Functional level

Content

The relationship between this chapter and previous chapters Keeping the BA-model in mind

Defining lead- and lag information

The information wheel

Establishing new business processes If we start up a process from nothing, we have no data on it

Optimizing existing business processes If optimizes and existing process, we have data to work with also

Which processes should we start with? Depending on the overall company strategy, what internal information

systems must be expected a natural focus for the company

The relationship between this chapter and previous chapter

Chapter 3

The previous chapter focused on how information is used at the overall strategic level

At the functional level, we identify how to get from having some overall objectives for a department / function to being able to specify the information requirements.

The example contains 3 function / business processes

Defining lead- and lag informationChapter 3

Lead information relates to the future and is used to optimize or start up new processes

What to do tomorrow? Turn left!

Lag information relates to the pastand is used to monitor processes

How did it go?

You’ll need both

Not competing information

Together they constitute an information wheel

GPS and lead - and lag information

The information wheelChapter 3

Lead informationWhat to do tomorrow

Lag informationHow did it go?Process

Question: Can you create lead information out of lag information?

Establishing new business processesChapter 3

The Rockart model explains, how to identify information related to strategy and objectives, this is called developing an information strategy

The department/function is given some objectives. To reach these objectives an operational strategy is developed. The critical success factors are the elements of the plan that must have a successful outcome, if the plan as a whole is to succeed. Thisalso defines what the Business Intelligencedepartment has to deliver

Lead and lag information Lead information is information or knowledge that is

necessary for even beginning our new business activities.

Lag information let us monitor if we reach the strategic target


An example, page 64.


Exercise

Create 2 information wheels with lead and lag information for Human Resources (HR) in a IT consulting company

Assumption: overall strategy at company level: we want to increase the market share from 15% to 25 % Assumption: objective for HR: Hire 20 new good sales managers

Your task as a HR manager Make your own local / operational

strategy Identify 2 critical success factors Identify lead and lag information

for each critical success factor Develop and present your information

wheels Process

Optimizing existing business processesChapter 3

The model focuses more on the lag information by collecting and analyzing it to understand correlations to be able to improve processes in the future.

The model uses lag information to create lead information (maybe learning loops)

Process

Optimizing existing business processesChapter 3

Exercise

Optimizing two existing business processes of your own choice (perhaps you local canteen)

Identify lag information to create lead information

Create learning loops

Present your information wheels

Process

Which processes should we start with?Chapter 3

Correlation between strategy and operational processes with significant analytical potential lead us to the fact that some analytical disciplines are more relevant for some businesses than for others.

Exercise Which analytical disciplines do you think the companies below should focus on?

Dell – low cost web distribution of products Hennes & Mauritz, Boss and Wal-Mart clothing stores. Apple – innovative and customer oriented Siemens – world leader in wind mills

Chapter 4

Content Data, information and knowledge Analysts role in the BA model Requirements the analyst must meet How to select the analytical method (The three questions) Reporting Statistical testing Data mining

This chapters focus in on what links the technical part of BI and BA together with the commercial part of the organization. The focal point here is therefore the analyst and his or hers toolbox

Data, information and knowledgeChapter 4

Exercise

Discuss the difference between data, information and knowledge – Are these concepts the same thing?

Data, information and knowledgeChapter 4

The definitions in the book

Data is defined as the carrier of information. An example of a piece of data could be ‘‘bread’’ or ‘‘10.95.’’ Data is often too specific to be useful to us as decision support. Data rests in data warehouses and typically describes a transaction, action, status etc.

Information is data that is aggregated to a level where it makes sense for decision support in the shape of, for instance, reports, KPIs, alerts, tables, or lists. An example of information could be that the sales of bread in the last three months have been respectively $18,000, $23,000, and $19,000.

Knowledge is information that has been analyzed and/or interpreted. This means that the BI department, as an example, offers some suggestions regarding why bread sales have fluctuated in the last three months. Reasons could be seasonal fluctuations, campaigns, new distributions conditions, or competitors’ initiatives. It is therefore not a question of handing the user a table, but instead of supplementing this table with a report or a presentation. This means, of course, that when the BI department delivers knowledge, it is not a result of an automated process, as in connection with report generation, but rather a process that requires analysts with quantitative methods and business insight.

Requirements the analyst must meetChapter 4

The core skills of an business analyst

Business competencies First of all, the analyst must understand the business process he or she is supporting and how the

delivered information or the delivered knowledge can make a value-adding difference at a strategic level

Tool kit must be in order (method competencies) Reporting Statistical tests (to show any correlations that might be present in the tables.) Data mining (spot pattern or correlation in data)

Technical understanding (data competencies) If, for instance, an analyst needs new data in connection with a task, it’s no good if he or she needs

several days to figure out how the Structured Query Language (SQL) works, what the different categories mean, or whether value-added tax is included in the figures.

Analysts spend about 80% of their time retrieving and presenting data, so we also have to place some clear demands on the analysts’ competencies in connection with data processing.

How to select the analytical methodChapter 4

Three question model

The aim is to present a model that can be used in the dialogue between management who wants information and the analyst who must deliver it: Question 1: Determine with the process owner whether the quantitative analytical competencies, or

the data manager and report developer competencies are required. Question 2: Determine whether hypothesis-driven analytics, or data driven analytics can be expected

to render the best decision support. Question 3: Determine whether the data-driven method has the objective of examining the

correlation between one given dependent variable and a large number of other variables, or whether the objective is to identify different kinds of structures in data.

ReportingChapter 4

Three question model

Reporting (by using descriptive statistics) presents information and the individual viewer or business user will be the person to interpret and transfer this information into knowledge.

Reporting is typically based on a part of the available data and is cross tabulated (e.g. sales per week per region)

ReportingChapter 4

Types of reporting

Ad hoc reports (one time only) Manually updated reports (normally used in connection with projects and therefore have a limited

lifetime) Automated Reports: On Demand (standard reports) Automated Reports: Event Driven

(relevant information presents itself to the individual user at the right time)

Reports should be internally aligned (one version of the truth)

StatisticsChapter 4

When working with hypothesis-driven methods, we use statistical tests to examine the relationship between some variables in, let’s say, gender and age. We must have some initial idea of the relationship

The result of the test will be a number between 0 and 1, describing the risks of being wrong, if we conclude based on the data material that there is a relationship between gender and lifetime. The rule is then that if the value we find is under 0.05, that is, 5%, then the likelihood of our being wrong is so insignificant that we will conclude that there is a relationship.

Since the 5% also means that if we make 20 random test between variables which has nothing to do with each other, will in average 1 time (in 5% of the cases) find a significant relationship which is a false true. For the same reason you must before you test make a sanity check in regard to whether it theoretical possible that there is a relationship between the two variables.

Data miningChapter 4

Where statistics is a hypothesis driven process with the aim of creating knowledge. Data mining is more of a data driven process with the aim of finding actionable patterns in the data.

This means that we scan the data for patterns and we evaluate the quality of the decision support the process generates via testing the model on an unknown data set. In statistics we evaluate the quality of the decision support via monitor the level of significance and screen for whether it is a relevant test in the first place.

Data mining is typically used to give decision support on questions like: Which of our engines will break down when and why? Which customers will leave us when and why? Which customers will buy what and when? Which customers have high credit risk? What is the price of our products next year?

Exploratory techniques Chapter 4

Four typical examples In BI, we typically see four types of explorative analyses. These are methods for data reduction, cluster

analysis, cross-sell models, and up-sell models.

Data reduction - We take all the information in a large number of variables and condense it into a smaller number of variables.

The cluster analysis also simplifies data structures by reducing a large number of rows of individual customers to a smaller number of segments. For this exact reason, the two methods are often used in combination with questionnaires, where data reduction identifies the few dimensions that are of great significance, and the cluster analysis then divides the respondents into homogenous groups or segments.

Cross-sell models are also known as basket analysis models. These models will show which products people typically buy together

Up-sell models are not looking at what’s in the shopping basket once; instead, they are looking at the contents of the shopping basket over time

Closter analyticsChapter 4

Figure: PixelMap displays cluster regions - Note high-income clusters on the East side of Manhattan's Central Park, and low-income clusters on the West end of Brooklyn)


• Cross-sell or Die: Get More Sales in the iPhone App StoreFigure: After three weeks of strong sales in the App Store, sales began to decline for Company X's first application. When the second application was released, it included a strong call-to-action to purchase the first application. As the graph shows, strong sales of the second application, along with solid cross-sell conversion, reversed declining sales of the first application. Cross-selling remains among the most effective marketing tools.


A typical data mining process

Step 1: Development of different models

Step 2: Selecting the best model based on criteria such as : The model can be interpreted The model’s prediction power on an ”unknown”

dataset

Step 3: Score a data set – Implement model (production)


Example

Question to be answered: “Is there a correlation between the inquiries from corporate customers and whether they canceled their subscriptions shortly after?”

Based on the model, an automated electronic service was generated that scanned the data warehouse of the call center every five minutes. If any ‘‘critical’’ calls were found in the log readings from conversations with customers—which could be that they had called in for a good deal combined with the fact that their contract was up—then the person who was responsible for this customer would automatically receive an email. With reference to the reporting section of this chapter, this is essentially an event driven report which is generated based on a data mining algorithm.

Base table :Canceled subscription Customer name Contract up InquiriesYes YMCA yes Get discountNo Maersk Shipping yes Good deal on new phonesYes General motors no Get discount


About data mining projects

A data mining project often takes several weeks to carry out, partly because we are often talking about large volumes of data (both rows and columns) as input for the models.

The modelling fase of the project only takes up a fraction of the total time it takes to establish a new data mining process

After the data mining process is established and automated, it can be performed in a matter of hours next time.

Choose appropriate analytical method for information wheels

Chapter 4

Exercise

HR wants' to optimize their future hiring process by using analytics. As analyst you shall help them choose appropriate analytical methodologies for their information wheels

HR wants to compare the new sales managers performance with the ’old’ sales managers performance – What’s the method?

HR wants to identify a profile of a successful sales manager – What’s the method? HR wants to identify if there is a correlation between salary and sales results – What’s the method? HR wants to identify a good 10 year carrier path for the new sales managers – What’s the method? HR wants to know the average sales manager’s historical sales result – What’s the method? Which of the above questions/answers are lead information and which are lag information? Which of the above questions/answers belong to the information domain and which belong to the

knowledge domain?

Chapter 5

Content Why a Data Warehouse?

Architecture and processes in a Data Warehouse

How should you access your data?

BI Portal: Functions and examples

Exercise in Data Warehouse

This chapter will look into what a data warehouse or data repository is, and how it extract, transforms and loads data to other systems and front ends

Why a Data Warehouse?Chapter 5

To increases the usability and availability of source data

To ensure consistency and valid data definitions across business areas and countries (this principle is called one version of the truth).

To hold documentation of metadata centrally upon collection of data

To avoid information islands

To create a historical data foundation

To avoid overloading of source systems with daily reporting and analysis

Architecture and processes in a data warehouse

Chapter 5

Data floats from source systems to the BI Portal

How should you access your data?Chapter 5

How does users get data out of a data warehouse.

BI Portal: Functions and examplesChapter 5

Exercise

Explain what information the front-end displays and how business people can use them to improve upon decision making and processes?


Exercise



Exercise



Exercise


ExercisesChapter 5

Some of the most common terms in data warehousing

What are dimensions and how can you use them? Examples? What are metrics/facts? Examples? Why have a metadata repository? What does an ETL job do? Examples? What is a OLAP cube? What is a data mart? It is often said in data warehouse projects that you should: ”Think big, start small and deliver fast” - Why is

that? What is SQL and how can you use it? How do business people normally access BI information? What does ’data quality’ mean and why is it important? What is an ODS and why have it?

How many did you get right?

Chapter 6

Content Some examples of data-generating source systems

What data source do we prioritize?

How to store data in the best way

How to combine data in the best way

This chapter is about the different data that often can be found in an organization. You will learn about which types of decision support it can be used for and principle for how the data should be stored.

Some examples of data-generating source systems

Chapter 6

- and how this data can be useful

Billing systems. These systems print bills to named customers. By analyzing this data, we can carry out behavior-based segmentations, value-based segmentations, etc.

Reminder systems. These systems send out reminders to customers who do not settle their bills on time. By analyzing this data, we can carry out credit scoring and treat our customers based on their payment records.

Debt collection systems. These systems send statuses on cases that have been transferred to external debt collectors. This data provides the information about which customers we do not wish to have any further dealings with, and which should therefore be removed from customer relationship management (CRM) campaigns, until a settlement is reached.

CRM systems. These systems contain history about customer calls and conversations. This is key information about customers, which can provide input for analyses of complaint behavior and thus what the organization must do better. It can also provide information about which customers draw considerably on service resources and therefore represent less value. It is input for the optimization of customer management processes (see ‘‘Optimizing Existing Business Processes’’ in Chapter 3). It’s used in connection with analyses of which customers have left and why.


Chapter 6


Product and consumption information. This information can tell us something about which products and services are sold out over time. If we can put a name to individual customers, this information will closely resemble billing information, only without amounts. Even if we are unable to put a name to this information, it will still be valuable for multi purchase analyses, as explained in ‘‘The Product and Innovation Perspective’’ in Chapter 2.

Customer information. These are names, addresses, entry time, any cancellations, special contracts, segmentations, and so forth. This is basic information about our customers, for which we want to collect all market information. This point was explained in the customer relations perspective in Chapter 3.

Business information. This is information such as industry codes, number of employees, or accounting figures. It is identical to customer information for companies operating in the business-to-business (B2B) market. This information can be purchased from a large number of data suppliers, such as Dun & Bradstreet. It is often used to set up sales calls.

Accumulation of KPIs. These are used for monitoring processes in the present, but can later be used for the optimization of processes, since they reveal the correlations between activities and the resulting financial performance.


Chapter 6


Campaign history. Specifically, who received which campaigns when? This is essential information for marketing functions, since this information enables follow-up on the efficiency of marketing initiatives. If our campaigns are targeted toward named customers, and we subsequently are able to see which customers change behavior after a given campaign, we are able to monitor our campaigns closely. If our campaigns are launched via mass media, we can measure effect and generate learning through statistical forecasting models. If this information is aggregated over more campaigns, we will learn which campaign elements are critical and we will learn about overall market development as well.

Web logs. This is information about user behavior on the company’s Web site. It can be used as a starting point to disclose the number of visitors and their way of navigating around the Web site. If the user is also logged in or accepts cookies, we can begin to analyze the development of the use of the Web site. If the customer has bought something from us, it constitutes CRM information in line with billing information.


Chapter 6


Questionnaire analyses performed over time. If we have named users, this will be CRM information that our customers may also expect us to act on. Questionnaire surveys can be a two-edged sword, however; if we ask our customers for feedback on our service functions, for instance, they will give us just that, expecting us to then adjust our services to their needs.

Production information. This kind of information can be used to optimize production processes, stock control, procurement, and so on. It is central to production companies competing on operational excellence, as described in Chapter 2. & Accumulation of KPIs. These are used for monitoring processes in the present, but can later be used for the optimization of processes, since they reveal the correlations between activities and the resulting financial performance.

Data mining results. These results, which may be segmentations, added sales models, or loyalty segmentation, provide history when placed in a data warehouse. Just as with KPIs, this information can be used to create learning about causal relations across several campaigns and thus highlight market mechanisms in a broader context.


Chapter 6


Human resources information about employees, their competencies, salaries, history, and so on. This information is to be used for the optimization of the people side of the organization. It can also be used to disclose who has many absences due to illness and why. Which employees are proven difficult to retain? Which employees can be associated with success as evaluated by their managers? This information is generally highly underrated in large organizations, and public enterprises in particular, which we will substantiate by pointing out that all organizations have this information and that the scarce resource for many organizations is their employees. Similarly, hour registration information can be considered human resources-related information. When hour registration information (consumption of resources) is combined with output information from, for instance, the enterprise resource planning (ERP) system, we can develop a number of productivity key performance indicators (KPIs).

Information from ERP systems. This information includes accounting management systems in which entries are made about the organization’s financial transactions for the use of accounting formats. It can be related to KPI information, if we want to disclose correlations between initiatives, and whether results were as expected.

What data source do we prioritize?Chapter 6

- depends on what you want to use the data for

I takes a long time to make a data warehouse, often you will therefore include some data sources at the time

The same with data mining projects, time limitations might make us discard some data sources

How to store data in the best wayChapter 6


Depending on what you want to use the data for, it can be stored more or less optimally

How to combine data in the best wayChapter 6


Through merging data sources we can gain synergies’

We can merge internet behavior and customer info and get improved decision support on the different behavior across age groups, gender etc.

ExercisesChapter 6

It is a well known fact that men save more for retirement than women – A new law is passed in Denmark in 2009 that allow men to keep their pension 100% in case of a divorce.

As a business intelligence analyst you are asked by the banks pension department (sales) to develop a questionnaire to create source data for lead information for a campaign targeting women with insufficient pension schemes.

In accordance to their information wheels, the pension department needs to know how many women are in the target group and a profile of the potential customers for the campaign. Also sales asks you to develop lag information to measure the success of the campaign.

Task: Develop the questionnaire for the lead information and the lag information

Chapter 7

Content What is a BICC

The relationship with the technical part of the organization

Competencies in a BICC

Organizing your BICC

Ambitions with a BICC

This chapter will discuss what a Business Intelligence Competence Centre is and why it is important to have this business unit to govern a cross functional process.

Organizing your BICCChapter 7

If you place your BICC unit – preferably close to the strategy department – it will be more likely to have a direct impact on strategy

Holistic: Information is being treated as a strategic resource, which can be used to determine company strategy. The organization constantly evaluates how it can compete on information when creating strategies.

If you make virtual business unit close to where the analysts are used, you are likely to increase the performance

What is a BICCChapter 7

A BICC is a business unit mastering analytical, business and IT-competencies

A BICC can give BI and BA the needed critical mass to become successful

A BICC is the owner and developer of the information wheels in an organization

The relationship with the technical part of the organization

Chapter 7

First it uncovers what the organizational wide information need is – which is what you information strategy must be able to deliver on

This include how the data and information must be transported around in the organization – the information architecture

Finally you identify the technology that can make all this happen considering costs and alternative solutions – the technology strategy

Competencies in a BICCChapter 7

The three knowledge domains will together create the needed synergies to make BI and BA successful

Organizing your BICCChapter 7

If you place your BICC unit – preferably close to the strategy department – it will be more likely to have a direct impact on strategy

Holistic: Information is being treated as a strategic resource, which can be used to determine company strategy. The organization constantly evaluates how it can compete on information when creating strategies.

If you make virtual business unit close to where the analysts are used, you are likely to increase the performance

Ambitions with a BICCChapter 7

Where are you and where do you want to be? Improved performance – move horizontally Improved strategic impact – move vertically

Chapter 8

Content Strategic project or not

Estimating the value of a project

Qualitative description of a project

Project seen as a part of a greater whole

This chapter will give you an idea about how you can priorities which BI, BA or other project to focus on first

Strategic project or notChapter 8

Putting all the projects into a cost-benefit matrix might make it easier for you to prioritize

Large projects must always be supported by strategic arguments

If the project is not strategic, it should be prioritized based on a cost-benefit analysis

Estimating the value of a projectChapter 8

Elements in the costs benefit analysis One off costs – implementation costs Additional maintenance costs Increased value for the users or price for customers Improved resource utilization- what you will save from using the new solution

See also the SIPOC example next slide


By comparing the process in a SIPOC model before and after you will be able to put value on the changes


Alternatively you can make a qualitative description

Useful if there is uncertainty about the financial consequence and a need to link to the organizational strategy


Chapter 8

Try to see the projectas a part of a greater whole

What is you strategic objective vs. where areyou today(maturity analysis)

Can you make the leap(readiness analysis)


Chapter 8

When to use which way to prioritize a project

Chapter 9

BI in the future Decision support will be

centered around the individualperson as a mean of optimizing the overall performance of the process they are a part of.

Chapter 9

BI in the future We define BI as delivering the right decision support to the right people at the right time.

We believe that the quality of the decision support will improve Suggested solutions rather than issue flagging

People will receive the information individually in a way and form that is customized to the individuals need Based on what you prefer, you will be informed

The information will be given when relevant to the user forecasting will increased the lead time for critical

decisions

Documents

Business Analytics for Managers - Taking Business Intelligence Beyond reporting