Developing a data quality framework for asset management ... · Developing a data quality framework for asset management 101 ... organisational decisions together with the increasing

100 Int. J. Information Quality, Vol. 1, No. 1, 2007

Copyright © 2007 Inderscience Enterprises Ltd.

Developing a data quality framework for asset management in engineering organisations

Shien Lin*, Jing Gao, Andy Koronios and Vivek Chanana Strategic Information Management Lab, School of Computer and Information Science, University of South Australia, Mawson Lakes, SA 5095, Australia E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author

Abstract: Data Quality (DQ) is seen as critical to effective business decision-making. However, maintaining DQ is often acknowledged as problematic. Asset data is the key enabler in gaining control of assets. The quality asset data provides the foundation for effective Asset Management (AM). Researches have indicated that achieving AM DQ is the key challenge engineering organisations face today. This paper investigates the DQ issues emerging from the unique nature of engineering AM data. It presents an exploratory research of a large scale national-wide DQ survey on how Australian engineering organisations address DQ issues, and proposes an AM specific DQ framework.

Keywords: data quality; DQ; engineering asset management.

Reference to this paper should be made as follows: Lin, S., Gao, J., Koronios, A. and Chanana, V. (2007) ‘Developing a data quality framework for asset management in engineering organisations’, Int. J. Information Quality, Vol. 1, No. 1, pp.100–126.

Biographical notes: Shien Lin is a Computer Science Researcher currently working towards the completion of a PhD at the University of South Australia. He is currently involved with the research into the development of data quality framework for engineering asset management associated with the CRC for Integrated Engineering Asset Management (CIEAM) program in Australia. Prior to joining the University of South Australia, he obtained a Master Degree in Electronic Commerce and has had extensive experience working on various positions in the Information System and Finance sectors. He has published journal paper, book chapter and various proceedings in international conferences such as International Conference on Information Quality, Americas Conference on Information Systems.

Jing Gao received his PhD Degree from the University of South Australia. He is currently working as a Full Time Research Fellow/Lecturer for the School of Computer and Information Science at University of South Australia. He has also been working as a Professional Trainer/Consultant for many Australian leading organisations including the Defence Science and Technology Organisation (DSTO) of Australia, SA Water and etc. His research interest is about how to obtain an alternative view to appreciate complex social and technical problems.

Developing a data quality framework for asset management 101

Andy Koronios received his PhD Degree from the University of Queensland. He has extensive teaching experience both in the tertiary sector at undergraduate and postgraduate, MBA, DBA and PhD level as well as in the provision of executive industry seminars. He has numerous research publications in a diverse area such as multimedia and online learning systems, information security and data quality, electronic commerce, and web requirements engineering. His current research interests focus on data quality and the use of information in strategic decision making. He is currently a Professor and the Head of the School of Computer and Information Science at University of South Australia.

Vivek Chanana is a Senior Research Fellow with Strategic Information Laboratory at the School of Computer and Information Science, University of South Australia. He holds PhD in Computer Science, MTech. (IIT Delhi) and BEng. (Hons.) in Electronics Engineering. He has extensive experience spanning over two decades. Prior to his academic career, he worked in industry at senior technical positions. He has been involved with a number of projects in research, design and development of information systems. His current research interests include information and data quality, business rules, information retrieval and context based agile information access.

1 Introduction

Almost every process and activity in the organisations involves data. Levitin and Redman (1998) suggest that data provides the foundation for operational, tactical, and strategic decisions; further, they identify that, through the judicious use of data, managers are able to plan, organise and control an organisation’s resources to seek out new business opportunities, improvements to processes and the development of innovative products and services. As data becomes increasingly important in supporting organisational decisions together with the increasing global competition, regulatory compliance and corporate governance, which all demand that data be leveraged to help day-to-day business operations, modern organisations, both public and private, are now continually generating more data than at any other time.

More data, however, does not necessarily mean better information, or more informed business decisions. In fact, many are finding it difficult to use the data. It is estimated that more than 70% of generated data is never used (Koronios, 2006). Gartner Research (Desisto, 2004) found that bad data is worse than no data at all. There is strong evidence that most organisations have far more data than they possibly use; yet, at the same time, they do not have the data they really need (Levitin and Redman, 1998). Despite this apparent explosion in the generation of data it appears that, at the management level, executives are not confident that they have enough correct, reliable, consistent and timely data upon which to make decisions. Many say they are drowning in data and are starved of information.

Consequently, the quality of the data that managers use becomes critical. Poor-quality data, if not identified and corrected, often leads to decisions being made more on the basis of personal judgement rather than being data driven judgement (Koronios et al., 2005). Without quality data, organisations are running blind and make any decision a gamble (ARC Advisory Group, 2004). This can lead to disastrous economic impacts on the health of the company such as less effective strategic business decisions, an inability

102 S. Lin, J. Gao, A. Koronios and V. Chanana

to reengineer, mistrust between internal organisational units, increased costs, customer dissatisfaction, and loss of revenue (Wang and Strong, 1996). In some cases, it could also lead to catastrophic social consequences such as massive power failures, and industrial or aviation disasters.

Industry has recently put a strong emphasis onto the area of AM. In order for engineering organisations to generate revenue they need to utilise assets in an effective and efficient way. Often the success of an enterprise depends largely on its ability to utilise assets efficiently. Thus, AM has been regarded as an essential business process in many organisations, and is moving to the forefront of contributing to an organisation’s financial objectives.

Previous studies in AM suggest that a common, critical concern with engineering AM is the lack of quality data (Eerens, 2003; IPWEA, 2002). Recent research by US GAO (2004) clearly demonstrates that achieving DQ is the key challenge engineering organisations face today in successfully implementing effective engineering AM. Saunders (2004) indicated that although very large amounts of data are being generated from asset condition monitoring systems, little thought has been given to the quality of such generated data. Thus the quality of data from such systems may suffer from severe quality limitations.

As an important initiative proposed by the Australian federal government and the industry sector, studies were commenced in 2003 into the impact of the quality of data on AM organisations including the Australian Navy, state utilities, transportation and mining companies, and local governments. A number of research findings (Neely et al., 2006; Saunders, 2004; Koronios and Haider, 2003; Koronios et al., 2005; Lin et al., 2006) were published in various conferences and journals. In 2006, a large-scale nation-wide survey was conducted into DQ issues in engineering AM, with a sample size of 2000 and a response rate of over 23.9%. This is one of the largest nation-wide surveys of its kind, aimed as directly addressing DQ issues in engineering AM organisations in Australia. This paper discusses the development of the DQ framework through this survey and presents some of its findings. Data and information are often used synonymously. In practice, managers differentiate information from data intuitively, and describe information as data that has been processed. Unless specified otherwise, this paper will use data interchangeably with information.

2 Data Quality (DQ)

Through time, the field of DQ and data management has become an important and vibrant research domain. Numerous researchers have attempted to define DQ and to identify its dimensions (Kriebel, 1979; Ives et al., 1983; Wang, 1998; Fox et al., 1994; Wand and Wang, 1996; Wang and Strong, 1996; Shanks and Darke, 1998; Kahn et al., 2002). Traditionally, DQ has been described from the perspective of accuracy. However, many researches have indicated that DQ should be defined as beyond accuracy and is identified as encompassing multiple dimensions. Through literature, many authors have tried to explain the meaning of all relevant dimensions from several points of view (Strong, 1997; English, 1999; Ballou et al., 1998; Orr, 1998). Each of them has tried to identify a standard set of DQ dimensions valid for any data product; but as Huang et al. (1999) state, it is nearly impossible due to different nature of different data environment.


The four most frequently mentioned DQ dimensions in the literature are accuracy, completeness, timeliness and consistency (Liu and Chi, 2002; Naumann, 2002; Bouzeghoub and Peralta, 2004; Batini et al., 2004; Strong, 1997). Unfortunately, a set of data may be completely satisfactory on most dimensions but inadequate on a critical few. Furthermore, improving on one DQ dimension can impair another dimension. For example, it may be possible to improve the timeliness of data at the expense of accuracy (Ballou and Pazer, 1995). It may be complete at the cost of concise representation (Neely and Pardo, 2002). Moreover, different stakeholders in an organisation may have different DQ requirements and concerns (Giannoccaro et al., 1999). Data whose quality is appropriate for one purpose may not be sufficient for another (Neely and Pardo, 2002). The DQ dimensions considered appropriate for one decision may not be sufficient for other types of decisions. As a result, Wang and Strong (1996)’s widely-accepted definition of DQ “quality data are data that are fit for use by the data consumer” is adopted in this research.

In practice, maintaining the quality of data is often acknowledged as problematic. Examples of the many factors that can impede DQ are identified within various elements of the DQ literature. These include: inadequate management structures for ensuring complete, timely and accurate reporting of data; inadequate rules, training, and procedural guidelines for those involved in data collection; fragmentation and inconsistencies among the services associated with data collection; and the requirement for new management methods which utilise accurate and relevant data to support the dynamic management environment. Clearly, personnel management and organisational factors, as well as effective technological mechanisms, affect the ability to maintain DQ.

Due to the complexity of DQ problems, there is a need for a generic DQ framework to categorise the issues, and further provide guidance for exploring and understanding the DQ issues. With this respect, the framework (Table 1) developed by Wang et al. (1995) served this purpose.

Table 1 A framework for DQ research

Framework element Description Development of a corporate DQ policy Management

responsibilities Establishment of a DQ system Operating costs include prevention, appraisal, and failure costs Operation and

assurance costs Assurance costs relate to the demonstration and proof of quality as required by customers and management Definition of the dimensions of DQ and measurement of their values Analysis and design of the quality aspects of data products

Research and Development

Design of data manufacturing systems that incorporate DQ aspects Quality requirements in the procurement of raw data, components, and assemblies needed for the production of data products Quality verification of raw data, work-in-progress, and final data products

Production

Identification of non-conforming data items and specifications of corrective actions


Table 1 A framework for DQ research (continued)

Framework element Description Storage, identification, packaging, installation, delivery, and after-sales servicing of data products

Distribution

Quality documentation and records for data products

Employee awareness of issues related to DQ Motivation of employees to produce high-quality data products

Personnel management

Measurement of employee’s DQ achievement Legal Function Data product safety and liability

Source: Wang et al. (1995)

3 Engineering Asset Management (AM)

As the domain of this research is about DQ in engineering AM organisations, it is important to address the nature of engineering AM first. Engineering enterprises have a broad range of physical assets and each type deserves attention. Engineering assets like machine, production equipment, and fleets have a unique set of characteristics that dictate the need for a separate strategy. Engineering assets are expensive and demand constant accountability and care to extend their useful lifetime. They are complex and have lifecycles that must be managed through best practice methodologies and business processes. Their impact on the organisation is broad and involves many stakeholders who require visibility into asset information. Multiple stakeholders also introduce the need for synchronisation between engineering AM activities and other processes like production management and sales. Finally, engineering assets are expected to provide value over an extended period. Performance of the asset and the AM program must be periodically assessed, responsibly managed and continuously improved to reap maximum value. Table 2 summarises the nature of engineering AM.

Table 2 Engineering asset characteristics and management requirements

Engineering asset characteristics Asset Management requirements

Constant accountability – location, status, who uses, who controls, etc. Expensive Appropriate care of asset needed to get the most effective lifetime Process management across all significant lifecycle stages Complex Use of advanced physical AM methodologies and technologies Comprehensive view of asset information Effective visibility of asset information by multiple stakeholders

Broad impact on enterprise

Synchronisation of asset lifecycle management with other enterprise processes Asset performance and asset lifecycle performance management Extended lifetime Continuous improvement process

According to the British Standards Institute (2004), AM encompasses activities that are aimed at establishing the optimum way of managing assets to achieve a desired and sustained outcome. The objective of AM is to optimise the lifecycle value of the physical


assets by minimising the long term cost of owning, operating, maintaining, and replacing the asset, while ensuring the required level of reliable and uninterrupted delivery of quality service (Eerens, 2003; Spires, 1996; IPWEA, 2002). At its core, AM seeks to manage the facility’s asset from before it is operationally activated until long after it has been deactivated. This is because, in addition to managing the present and active asset, AM also addresses planning and historical requirements.

AM is process-oriented. The AM process itself is quite sophisticated involving a wide range of disciplines as well as the whole asset lifecycle that can span a long period of time (Steed, 1988). The lifecycle for a typical asset involves several interdependent stages including the identification of the need for an asset, the definition of the requirements, design, plan, acquisition, installation, operation, maintenance, rehabilitation and disposal. An overview is shown in Figure 1. At every stage of the process, AM also needs to collaborate and synchronise with other business processes, which is vital to the effective management of engineering assets (as shown in Figure 2). The cost and complexity of engineering assets demands considerable planning to identify appropriate solutions and evaluate investment opportunities. These same characteristics are reflected in the need for an extended acquisition process, a comprehensive request for proposal, and an equally comprehensive purchase agreement that addresses guarantees and warranties. Installation and placing in service of engineering assets is also complex and requires a proper set of processes to manage contractors. Once the asset is acquired, it must be tracked throughout its useful life. Finally, records must be made of its eventual disposition.

Figure 1 Asset lifecycle stages

Source: Snitkin (2003)

The AM process itself is data centric. The sophistication of the engineering AM process requires substantial information to be collected throughout all stages of a typical asset’s lifecycle. This information needs to be maintained for many years in order to identify long-term trends. The process also uses this information to plan and schedule asset maintenance, rehabilitation, and replacement activities. In order to manage and support the complicated AM process and its data requirements, specialised technology and systems are required. A variety of specialised technical, operational and administrative systems exist in AM. These not only manage, control and track the asset through its entire lifecycle, but also provide maintenance support throughout the lifecycle of the asset. Table 3 shows some of these technical systems. Considering the complexity and importance of AM, these systems are normally bought from multiple vendors and each is specialised to accomplish its task. Unfortunately, this leads to an extremely difficult integration job for the end-user.


Figure 2 Collaborative asset lifecycle management

Source: Snitkin (2003)

Table 3 Asset Management technical systems

Asset lifecycle stage Asset Management technical system Computer Aided Design (CAD) Asset creation Document Management System (DMS) Supervisory Control and Data Acquisition (SCADA) system Enterprise Asset Management (EAM) Plant Asset Management (PAM) Computerised Maintenance Management System (CMMS) Enterprise Asset Maintenance System Reliability Centred Maintenance (RCM) Total Productive Maintenance (TPM) Geographic Information System (GIS)

Asset operation and maintenance

Product Service Management (PSM) Reliability assessment system Turbo-machinery safety system Condition Monitoring System (CMS) Rotating machine vibration condition monitoring system Electrical motor testing system Root cause analysis system Asset capacity forecasting system Operational data historian

Asset operation and maintenance

Physical asset data warehouse system

Source: Developed by the authors


Engineering processes rely heavily on input of data and also produce a large amount of data. Engineering data itself is quite different to typical business-oriented data as illustrated in Table 4. It has unique data characteristics and complex data capture processes from a large variety of data sources. This large amount of data therefore can suffer from DQ problems. The nature of such DQ problems has not previously been investigated in Australian engineering-oriented organisations.

Table 4 Differences between engineering data and typical business data

Element Typical business environment Engineering Asset Management

Data environment

Transaction-driven, product-centric business data environment

Continuous data, process-centric open control system and manufacturing data environment

Self-descriptive Non self-descriptive Static Dynamic Intrinsic quality Intrinsic/extrinsic quality Discrete value with fewer or no constraints

Continuous value with constraints (e.g., within a range), precision value

Current Temporal Transactional data Time-series streaming data Often structured Often unstructured Easy to audit Difficult to be audited Can be cleansed using existing tools Difficult to be cleansed using existing

tools

Data characteristics

Similar data types Diversity of data types Data category Inventory data, customer data,

financial data, supplier data, transaction data etc.

Inventory data, condition data, performance data, criticality data, lifecycle data, valuation data, financial data, risk data, reliability data, technical data, physical data, GPS data etc. Disparate data sources Spatial data – plans/maps, drawings, photo Textual records – inspection sheets, payment schedules Attribute records – separate databases, maintenance/renewal records, fault/failure records, field books Real-time CMS/SCADA

Data sources Mainly transaction-based textual records from business activities

Other sources – existing/previous staff and contractors, photos

Often manually by data providers in fixed format

Electronically, involving sensors and technical systems such as SCADA system, condition monitoring system

Data capture

Data often entered by reasonably trained, dedicated personnel with proper relevant knowledge

Manually, involving field devices, field force, contractors, business rules


Table 4 Differences between engineering data and typical business data (continued)

Element Typical business environment Engineering Asset Management

Data collection environment is stable, well pre-organised

Data collected in a variety of formats

Requires to collect substantial data from many different parts of the organisation Data often entered by less/un trained, less dedicated personnel without proper relevant knowledge Data entry environment can be unstable, harsh, less pre-organised

Data capture

Data entry point is within the business

Data entry point can be far from the organisation site

Data to be kept in accordance with appropriate compliance requirements

Very large amount of data to be maintained for extended time for AM engineering and planning process

Data storage

Data stored on functional information systems

Data stored on various operational and administrative systems

Comprehensive Not comprehensive Process independent Process dependent

Data processing

Easy for data integration Complex to integrate data, need both vertical and horizontal data integration

Data to be shared only among relevant business systems

Data to be shared among various technical (e.g., design, operations, maintenance) and business systems

Data to be communicated to internal stakeholders

Data to be communicated to an array of stakeholders, business partners and contractors, subcontractors

Use general, common knowledge to interpret data

Need experts with professional knowledge to interpret data

Data usage and analysis

Easy for management use Difficult to translate asset data into meaningful management information


As discussed before, AM consists of a number of processes across the entire organisation. At each stage of the processes, all the resources required for the effective management of the processes such as people, technology, procedures etc need to be well considered. Thus, it is appropriate to use Mitroff and Linstone’s (1993) TOP model to explore the current DQ issues associated with AM practices. Mitroff and Linstone’s (1993) TOP model allows analysts to look at the problem context from either Technical or Organisational or Personal points of view:


• The technical perspective (T) sees organisations as hierarchical structures or networks of interrelationships between individuals, groups, organisations and systems.

• The organisational perspective (O) considers an organisation’s performance in terms of effectiveness and efficiencies. For example, leadership is one of the concerns.

• The personal perspective (P) focuses on the individual’s concerns. For example, the issues of job description and job security are main concerns in this perspective.

The aggregation of these perspectives will present a comprehensive structure for understanding the research problem in an organised manner.

4 Research methodology

Considering the importance of engineering data in AM and the uniqueness of AM data as compared to other business data, it becomes obvious that the quality asset-related data provides the foundation for effective AM in achieving the success of an asset-intensive organisation. Therefore, this research seeks to investigate the following research questions:

• What is the current state of DQ in Australian engineering organisations?

• What are the specific DQ requirements in AM?

• What are the emerging key factors for ensuring high quality of AM data?

In order to answer the above questions, a four-phase empirical study was designed.

• Phase 1: literature review and analysis

• Phase 2: pilot case study

• Phase 3: national DQ survey

• Phase 4: multiple case studies.

4.1 Phase 1 (literature review and analysis)

Phase 1 is an extensive literature review of the streams of research in generic DQ and engineering AM. The objective of the literature review is to propose a conceptual research framework for the study of DQ related issues in AM. As a starting point, the key DQ research framework (Wang et al., 1995) was used to extract a list of important issues for the study of DQ in engineering AM. This study was then based on Mitroff and Linstone’s (1993) TOP model to identify and categorise key DQ issues. This list of issues was then supplemented by other studies in DQ and AM research streams (English, 1999; Wang, 1998; Segev, 1996; Firth, 1996; Saraph et al., 1989; Bever, 2000; Xu et al., 2003; CIEAM, 2003; Sandberg, 1994; IPWEA, 2002; Eerens, 2003; Snitkin, 2003; Steed, 1988; US GAO, 2004; Woodhouse, 2003). The near-exhaustive list was analysed to develop the research framework (Figure 3) which provided the basis in guiding this research.


Figure 3 DQ research framework


4.2 Phase 2 (pilot case study)

The second phase uses the pilot case study approach to provide a broad picture of DQ issues in AM. There are two main reasons for using case studies for this phase. First, case studies are likely to be the most appropriate way for initial investigation of issues in a relatively new area. This investigation will eventually form the foundation for further research in DQ for engineering AM. Second, as per Yin (2003), case study research provides the advantage of presenting a holistic view of a process. The in-depth investigation allows the study of different aspects of a research topic and their relationship with one another. In essence, it enables researchers to view the total environment of the process under study.

The pilot case study was conducted in two different companies based in Adelaide. Of the two organisations, one is a government owned organisation and the other is a private company. These companies represent industries of water utility and general engineering service. The selection of companies was based on our ability to gain access to key AM stakeholders in the company. The objective of the pilot case study was to create a DQ research forum within industry partners and to discuss their specific DQ problems. The outcome from the pilot case study was used to refine and extend the issues from the literature review.

In DQ studies, four types of stakeholders have been identified: data collector, data custodian, data consumer, and data owner (Strong, 1997; Wang, 1998). In this study, stakeholders of DQ in AM are defined as follows:


• data collectors are those who create or collect asset data e.g., technician, data entry staff

• data custodians are those who design, develop, manage, and operate the AM information systems e.g., IT manager, data manager

• data consumers are those who use the asset information in their work activities e.g., maintenance engineer, senior manager

• data owners are those who own and responsible for managing the entire data in AM systems e.g., asset manager.

The key stakeholders were interviewed to elicit information for the pilot case study. Care was taken to ensure that the stakeholders interviewed were involved with data management and/or decision making associated with engineering AM in their respective companies. Open-ended questionnaires were used for the case study. The data from the cases was analysed using content analysis. Inferences were made based on responses of interviewees. Underlying themes were the major unit of analysis. The results of interview analysis were then validated by the interviewees.

4.3 Phase 3 (national DQ survey)

The third phase used a mail DQ survey to get responses on a variety of questions. To apply the process of triangulation it has been suggested that active data collection methods can often be supplemented with passive data collection methods (such as written or observational methods) to overcome limitations associated with each type (Davis and Cosenza, 1988). Thus, a mail DQ survey was designed to address the questions developed in the previous studies, in order to understand the general perceptions towards data management issues and further establish the extent of DQ maturity.

A multi-section questionnaire were mailed to 2000 large random samples of asset manager, data collector, data custodian and data user, in 1100 geographically dispersed engineering AM organisations in Australia (including 572 organisations in the public sector). The questionnaire provided a guideline in the beginning to ensure that respondents had a common understanding of the various sections and definitions. The questionnaire was pre-tested by initially mailing it to 15 companies. Changes were incorporated and the questionnaires were then mailed to the remaining companies. The survey population for the questionnaire was chosen from engineering AM organisations based in Australia. These organisations represent a variety of industries:

• utility (water, electricity, gas, oil)

• mining and resources

• transport (rail, airline, ship, automobile)

• defence

• local government.

The list of AM organisations was first generated by selecting the major participants from the databases of specific industry-related associations in Australia. This list was then matched with the database of Business Who’s Who of Australia to develop the survey


population list of Australian AM organisations. We believed that being the key participants or leaders in the major areas of engineering AM, these organisations would be potential candidates for having AM information systems. Once the data was collected, statistical tools and methods were used to analyse the data and report the results.

The results of this phase, along with the analysis of the literature and pilot case study, were used to develop an AM DQ framework and formulate specific research propositions and questions for the next phase. The AM DQ framework developed as shown in Figure 4 will form the foundation for further research in performing a data audit to identify the nature and volume of DQ problems, and in developing a specification of the functional requirements for AM data cleansing and enrichment software packages.

Figure 4 AM DQ framework



4.4 Phase 4 (multiple case studies)

After the DQ survey, multiple case studies were conducted in phase 4 as the methodology to further validate the proposed DQ framework and obtain in-depth understanding. Case study research is an acceptable research strategy in IS. Cavaye (1996) suggests that the term ‘case research’ is not a monolithic one: case study methods can be applied and used in many different ways and, as such, case research is open to much variation. It is further suggested that case research can be carried out taking a positivist or an interpretivist stance, can take a deductive or an inductive approach, can use qualitative and quantitative methods, and can investigate one or multiple cases.

The interview-based case studies were designed to explore the DQ issues emerging within the chosen organisations. Four cases for the study were carefully selected from the large participating industry partners in the Centre for Integrated Engineering Assets Management (CIEAM) due to research commitments and funding arrangements. Semi-structured interviews were conducted with key AM people, including asset managers, IT managers, maintenance engineers, external contractors, senior managers, and data managers.

Responses to our research questions from the interviewees were collated, stored, and analysed using qualitative data analysis software. This analysis allows us to explore the raw data, identify and code the common themes, and identify relationships between themes in a rigorous manner. In analysing the collected data, an extensive examination of the viewpoints of various stakeholders was conducted. The views and actions of various interviewees in terms of their organisational interests were also examined. Interpretive research requires sensitivity to possible differences in interpretations among the participants as are typically expressed in multiple narratives of the same sequence of events under study (Klein and Myers, 1999). Klein and Myers (1999) also suggest that interpretive research requires sensitivity to possible ‘biases’ and systematic ‘distortions’ in the narratives collected from participants. Thus, the validation of the DQ framework for AM was achieved.

5 Findings and discussions

The findings are based on the DQ survey responses and interviews with DQ stakeholders in the multiple case studies, and are presented respectively below. The DQ survey is the first national DQ survey performed in Australia, focusing specifically on the DQ issues of engineering AM organisations. The key findings from this survey show the different attitudes and perceptions towards DQ. More importantly, it shows that the emerging key DQ issues found in the various data roles.

In addition to the survey findings, a number of DQ issues were identified and discussed in the case study findings. Many of them are specific to the AM practice. Examples of these issues were presented using the TOP model. Overall, asset-intensive organisations should adopt an active approach and focus on these DQ issues in their efforts for effective AM.


5.1 Survey findings

5.1.1 Awareness of DQ importance

The majority of survey respondents recognise the important role that Data Quality is playing in achieving the success of an asset-intensive organisation.

DQ was considered to have a direct effect on AM operations (and therefore have a potential to enhance the organisation’s performance). Approximately 81% of data owners, and 88% of data consumers considered DQ as a critical or very important issue (Table 5), especially as a path towards developing cost saving strategies and improving current AM performance. The 72% of the data collectors, and 80% of the data custodians also felt that the DQ is a critical or very important consideration to the organisation. Overall, the majority (81%) of participants perceived that DQ is an important success determinant for their organisations. It also suggests that the data consumers have a much higher needs for quality data. Further analysis reveals that the respondents from the defence industry, or in the chief officer level, or from the large AM enterprises have the highest rate of DQ awareness.

Table 5 Awareness of DQ importance

Data stakeholder Critical (%) Very important (%)

Data owner 29 52 Data collector 20 52 Data custodian 29 51 Data consumer 39 49 Total 30 51

5.1.2 Confidence on organisational DQ practice

Despite the overwhelming DQ awareness, a gap exists among the different data roles’ confidence on the organisational DQ practice.

Data consumers and data owners in strategic level have more confidence on the quality data they use/own, while data collectors and data custodians in tactical/operational level have less confidence on their organisational DQ.

Majority of data consumers (89%) and data owners (85%) have DQ confidence and can rely on organisational data (sometimes or all the time) for making critical decisions. However, only 26% of data collectors have confidence that data in the organisational system is correct. Fifty-seven percent of data custodians even suggest that there are a lot of DQ problems in the enterprise information system. The different attitudes found between groups indicate that there may be a disconnection between the strategic level managers and the operational personnel.

Further analysis found that only 38% of data consumers from engineering AM area have DQ confidence compared to 50% of those from general/finance management function area. Data consumers in utility companies responded the highest confidence rate of 64% while data users in defence industry have the lowest confidence rate of only 25%.


5.1.3 Data collector

The rule for data collection and data entry is seldom adhered to in capturing asset data.

Sixty-two percent of data collectors collect asset data by physically inspecting the assets and writing down the details for later data entry. Only 2% responded using electronic transducers to record data automatically. In entering the collected data, 34% of data collectors reported that the data entry was taken place after they returned to the office from the fields, 51% say that data entry was done even several days later. Only 10% of data collectors enter the data immediately at the location where the data was captured. Majority of the data providers (55%) manually type into the system from paper records while 14% give the paper records to office data entry staff for manual data entry.

According to literature, the rule for asset data entry is that the quality of AM data improves the nearer the data entry is to the asset and its work. It is obvious that the rule was seldom adhered to in the current process of AM (as shown in the statistics analysis). Literature indicates most organisations have a growing need for real-time access to information across their business. Mobile computing makes data accessible to decision makers at decision time, regardless of their location. It also makes data entry possible immediately at the location where the data is captured. It is suggested that business increase its DQ, productivity, and efficiency by employing mobile solutions to collect and deliver vital information where it is needed in each step of the AM process.

Electronic sensors and transducers are used to capture data in asset condition monitoring systems. Sixty-two percent of data custodians indicated that DQ problems exist in the condition monitoring systems, and have impacted on their organisations. The study found that despite sensor calibration and integrity check are essential steps in AM for ensuring high quality condition data captured in the automatic data production system, these steps are often ignored in the process of asset maintenance and management. Therefore, the extent that data captured by sensors is reliable is questionable. Nevertheless, 60% of data collectors show confidence with the data captured by electronic sensors. The inconsistent perceptions towards data confidence between data collector and data custodian indicate that there may be a lack of effective communication among DQ stakeholders in AM.

Over half of data collectors were given guidelines to follow in the forms of standard form (58%) or work procedure (46%) with only 25% were given appropriate training for data collection work. Only 11% of data collectors responded that management reviews in relation to data collection performance were regularly conducted with almost 50% were seldom, rarely, or never reviewed. The statistics analysis also shows that large organisation or local government have regular management review done on data collection performance with the highest rate of 72%.

Literature shows that data collectors with why-knowledge about the data production process contribute to producing better quality data. Data collector’s knowing-why knowledge is the most critical prerequisite for high DQ across data production processes. It is found that data collectors need management feedback and support as well as effective communication among DQ stakeholders, to enhance their knowing-why knowledge about the data production process. Thus, high quality data can be produced.


5.1.4 Data custodian

Lack of specific DQ tools available for engineering Asset Management DQ improvement.

About four in five data custodians are aware of the importance of DQ to the success of their organisations. But there appears to be much room for DQ improvement – particularly in the areas of database design, data migration, information system, standard adoption, and specific AM DQ software implementation.

Overall, only 10% say they have total confidence the data in their system is accurate to the required level. The rest have DQ problems with various degrees of seriousness. In fact, three-fourths report a whopping four or more DQ problems experienced in their organisations – the most-significant being wrong data, noted by 72%. Other common data problems include missing fields, duplicate records, out-of-date, and violation of standard or rules (Table 6).

Table 6 DQ problems

Wrong data 72% Out-of-date 70% Missing fields 68% Validity with respect to related data 58% Violation of standard or rules 46% Duplicate records 43%

When it comes to ranking the significance of DQ dimension, most data custodians say accuracy is first priority, higher than timeliness (Table 7).

Table 7 First priority of DQ dimension

Accuracy 28% Timeliness 19% Completeness 14% Consistency 6% Integrity 3% Redundancy 3%

Only 11% of data custodians say they strongly agree the data in their organisations have been stored in a well-designed database. Among companies where database is not regarded as well-designed, three quarters report they neither use DQ software to manage their data asset, nor have DQ system implementation in their organisations. But, among companies where DQ software is used across the enterprise or DQ system is implemented, 89% and 78% respectively report having well-designed database. It stands to reason that a well-designed database is more likely to be associated with having DQ solution implementation.

While 45% of the organisations were using some kinds of DQ products, there were nearly half of the participants who have not used any DQ software. Many participants cited there was no relevant DQ software available for specific engineering AM system.


5.1.5 Data consumer

• Three quarters of data consumers have problems with the data in their systems.

• Lack of trust on data collectors in the fields.

Compared to other DQ stakeholder groups, data consumers (88%) have the highest awareness of DQ importance. The majority of data consumers in the area of finance and general management (93%) considered DQ as a critical or very important issue, while a lower 71% of the respondents from AM domain were aware the importance of DQ to the success of their organisations.

Overall, 89% of data consumers say they can access the data and information all the time or sometimes when required. However, only 52% of senior managers in the higher strategic level can access the data all the time, compared to the higher 66% of data users in tactical and operational level. A possible reason for the difference is that data consumers in higher organisational level require to access data from external sources more often than operational staff.

Despite the higher rate in DQ awareness and data access, 75% of data consumers indicate they have problems with the data in their systems. The 86% of data users in operational level have data problems, compared to the 71% in the senior level. Among the respondents in AM area, 84% have data problems, compared to the 75% from finance and general management area. This indicates DQ in AM has become an important business issue.

Data consumers in AM indicate some major issues for data management. They are overloaded with data, but still have insufficient data for what is required for AM. Despite a lot of data is collected that is not used, often the data that is needed is not collected. This has resulted in data overloaded with insufficient quality data. In addition, different asset groups use different specialised systems to manage the assets, and the lack of overall data architecture and management strategy have made the control of data in AM more complicated.

When there is a doubt with the data obtained, most data consumers make cross-check (80%), or even ask the field people (71%), or make assumptions based on their own experience (67%). However, it is important to point out from the respondents’ comments that many data consumers would capture the data personally, or check on site themselves, or conduct field checks themselves, or just work on their own data wherever possible. This implies data users have a lack of trust on the data collectors in the fields.

5.1.6 Data owner

• Despite high DQ awareness, higher level data owners appear not treating DQ problems with high priority. Therefore, only limited resources were allocated to solve DQ problems.

• Although data management plan exists, more actions are needed in implementing DQ solution.

• Senior management commitment and support are critical in the organisation’s DQ efforts.


The survey revealed that 81% of data owners considered DQ as a critical or very important issue. Despite having high awareness of the DQ problems associated with AM, it appears that data owners at strategic level do not treat the DQ problems with high priority. Therefore, only limited resources were allocated to solve the problems.

At tactical level, data owners such as asset managers were frustrated by the poor quality information stored in the company’s AM information system. It appears that they can not do too much about it due to the limited resource support. Therefore, they tend to rely on other information sources such as GIS for AM practices. They even suggested organisational DQ efforts should be linked with reward system in order to make staff responsible for the DQ problems they caused.

The majority of data owners (58%) indicated they had data management strategy in place within their organisation. However, it appears that there was a lack of action in the organisation to actually implement the required DQ improvement solution. Over 44% of asset owners have no plans to implement any data management solutions in near future. Considering the findings along with the earlier observation that 89% of participants either suffered major or minor impacts by poor quality data, it appears that there may still be many organisations which have not learnt lessons and taken any timely actions to prevent future negative impacts caused by poor DQ. It is also found that senior management commitment and support are critical in ensuring the success of the organisation’s DQ efforts.

5.2 Case study findings

A number of DQ issues specific to AM practice were identified in the case study findings and summarised using the TOP model as shown in Table 8.

Table 8 The AM DQ issues

TOP perspective Data Quality issues in Asset Management

Integration of disparate AM systems Lack of integration between business and technical systems Asset hierarchy Data accessibility Database synchronisation Data exchange and integration Merging of data records in databases

Technology

Sensor calibration and integrity check Organisational readiness Organisation Business process reengineering Education and training Communication and management feedback

People

Motivation


5.2.1 Integration of disparate AM systems

Most companies today purchase specialised systems from many suppliers. Disparate systems have often led to an extremely difficult integration job for the end-user. Some of these AM-related systems include: reliability assessment systems, turbo-machinery safety systems, rotating machine vibration condition monitoring systems, electrical motor testing systems, enterprise asset maintenance systems, asset capacity forecasting systems, and physical asset data warehouse systems. Normally these systems are bought from multiple vendors and each is specialised to accomplish its task. Such disparate systems have often led to an extremely difficult integration job for the end-user.

5.2.2 Lack of integration between business and technical systems

Organisations have frequently adopted systems for the management of financial, human resource, inventory and maintenance aspects of the business which are incompatible with technical systems such as Supervisory Control and Data Acquisition (SCADA) and condition monitoring systems. The various computer software programs written for condition monitoring and diagnostics of machines that are currently in use cannot easily exchange data or operate in plug-and-play fashion without an extensive integration effort. For example, the maintenance work event forms which contain the hours that a contract has worked cannot be directly input to the payroll system as an alternative to the traditional timesheets. This makes it difficult to integrate systems and provide a unified view of the condition of assets to users. Such disconnects make it extremely difficult to bring real-time information from the plant into business systems. This has resulted in significant DQ consequences and thus negatively affected data-driven decision-making, because most users are unable to translate the vast amounts of available asset data into meaningful management information to optimise their operation and control the total asset base.

5.2.3 Asset hierarchy

Infrastructure assets generally have a clear hierarchical relationship that breaks down from the asset type as a whole to large units (facilities), then to assets and their components. The objective of developing an asset hierarchy is to provide a suitable framework for assets, which segments an asset base into appropriate classifications. The intent of the asset hierarchy is to provide the business with the framework in which data are collected, information is reported, and decisions are made. In most cases (as found in one organisation) organisations work with an informal asset hierarchy. This often leads to data being collected to inappropriate levels, either creating situations where costs escalate with minimal increases in benefits, or insufficient information is available to make informed decisions.

5.2.4 Data accessibility

Designers and asset manufacturers represent the external source of asset data. As part of the asset acquisition process, all asset information required to own and operate the asset should be handed over to the user organisation at the commissioning of the asset, in a form that can be assimilated readily into the user organisation’s asset information systems. These asset data may include a fully-fledged technical information database


with GIS maps, technical specifications, and even video clips of the equipment and its operation.

The research has found that a data gap may exist between the maker and the user of asset equipment. The user organisation needs to populate the AM information system with data from the manufacturer – particularly the component structure and spare parts. These capabilities exist in manufacturers’ Product Data Management (PDM) and Product Lifecycle Management (PLM) systems. Unless arrangements or contract conditions are made, in many cases, the data is not passed on to the buyer in a usable electronic format. In some cases, the asset data handed over to the user organisation does not conform to the physical assets delivered. In other cases, updated asset data, particularly the component structure, may not always be passed on to the user organisation. Information such as job instructions, maintenance cycles and advisory notices is also available. However, without standards and interfaces to share this information across systems, it is often held offline either as paper documents or poorly linked electronic copies of instructions.

5.2.5 Database synchronisation

The capability of AM information systems can be enhanced through a link with GIS to provide the ability to access, use, display, and manage spatial data. The ability to effectively use spatial asset data is important for utilities with geographically dispersed utility networks. However, it was found that one of the most critical activities is to establish synchronisation between the two database environments. One asset manager indicated that there has been an issue existed for overcoming the synchronisation of asset register in a very common work management system with GIS in the company. Both automated and manual processes needed to be defined and implemented to maintain synchronisation between the GIS and EAM databases. Database triggers and stored procedures need to be defined to automate the attribute update process maintaining synchronisation between the GIS and AM databases. Workflows and business rules must be developed for GIS and AM data editing, to ensure synchronisation from both applications.

5.2.6 Data exchange and integration

AM enterprises typically have many disparate systems monitoring different assets and/or locations. These systems are normally bought from different vendors and often do not share data with the AM system(s) directly. Two organisations indicated there is a need for data exchange between AM systems for seamless access to information across heterogeneous systems and different departments within an engineering enterprise. The apparent lack of dialogue among vendors leads to incompatibilities among hardware, software and instrumentation. Moreover, the life cycle performance data of the various assets are kept in individual uncoordinated databases, which make inter-process, inter-functional analysis very difficult.

5.2.7 Merging of data records in databases

There is a need to design databases to accurately record asset condition. Facility managers in one organisation indicated that whilst comprehensive asset databases generally exist, not all of them have been designed to accurately record asset condition


data. For example, a plant records system may hold data on all items of operational plant in terms of age, location, and date last inspected/maintained, but it does not provide the means to hold data on the condition of the plant. Where a comprehensive asset database exists containing both asset details and record of defects, the database, however, does not permit the recording of successive inspection data and so the important function of developing generic aging curves is not achievable.

5.2.8 Sensor calibration and integrity check

Interviews with asset maintenance field workers indicated that data captured by intelligent sensors may not always be accurate. Data capturing devices typically used in condition monitoring are electronic sensors or transducers, which convert numerous types of mechanical behaviour into proportional electronic signals, usually voltage-sensitive signals, producing analogue signals which in turn are processed in a number of ways using various electronic instruments. As signals are generally very weak, a charge amplifier is connected to the sensor to minimise noise interference and prevent signal loss. The amplified analogue signal can then be sent via coaxial cables to filtering devices to remove or reduce noise, before being routed to a signal conditioner and/or Analogue-to-Digital converters for digital storage and analysis. To ensure the data received by the condition monitoring system conforms to the original signal data captured by sensors, integrity checks for signal transmission process and sensor calibration need to be performed and maintained. However, as the sensor calibration and integrity check are often neglected in asset maintenance as mentioned in the interviews with maintenance staff, the extent to which automatically captured data is correct was shown to be of concern.

5.2.9 Organisational readiness

Many companies that attempt to implement AM information systems run into difficulty because they are not ready for integration, and the various departments within the organisation have their own agendas and objectives that conflict with each other. Organisational readiness can be described as having the right people, focused on the right things, at the right time, with the right tools, performing the right work, with the right attitude, creating the right results. It is a reflection of the organisation’s culture. AM system implementation involves broad organisational transformation processes, with significant implications to the organisation’s AM model, organisation structure, management style and culture, and particularly, to its people.

An AM system implementation project within the AM organisation is expected to have a high acceptance of the system in areas that provide just as good or better functionality than the old system. However some functions and processes did not get the full appreciation the legacy systems once had. Through interviews with technicians and data collectors, it was found that field workers are frustrated with the need to use Maximo [an asset maintenance work order management system] and are losing confidence in it. One staff member said that, “… with Maximo there are so many problems, people are not interested”. Another interviewee said that,

“Maximo hasn’t solved the speed problem which you would have thought it would have … From day 1, workers were starting to acknowledge the good points of old systems compared to Maximo.”


5.2.10 Business process reengineering

Organisational fit and adaptation are important to the implementation of modern large-scale enterprise systems. Like enterprise resource planning systems, specialised AM systems are also built with a pre-determined business process methodology that requires a rigid business structure for it in order to work successfully. They are only as effective as the processes in which they operate. When AM organisations faced with promised technology and systems, realising the gain does not occur with technology implementation. It occurs when business processes are reengineered to successfully integrate with the new systems. The integration of technology with business processes sets the stage for operational transformation, enabling the desired efficiency gains to be realised. In other words, integrating technology and business processes are the first step. The next step is for people to change their behaviour to incorporate the newly integrated business processes and technology into their daily work habits. Widespread collaboration and strategic discussions are crucial to realising an organisation’s desired goals when planning a new technology deployment.

Companies that place faith in EAM systems often do so without reengineering their processes to fit the system requirements. Consequently, this often results in negative impacts on the effectiveness of both the AM system and AM practices. It is found that the business process for AM in the organisation was not modified to fit the AM system.

5.2.11 Education and training

Engineering assets are generally complex in nature and some training is required to operate and maintain them. The data entry operators should be trained. This would improve the accuracy and also speed of data entry. One field technician indicated in the interview, when the new state-wide AM system was first introduced, several field staff members were chosen to take a brief, 3-day training workshop and then were assigned to be trainers for the rest of the regional office. Due to the insufficient knowledge and skills of these trainers, the system implementation experienced tremendous problems. One manager mentioned that, “training has been provided, but a lot of attendants are old and hence can’t be bothered”. However, through the interviews with field workers, one said that “training is the same for everyone” and “most of us have very little training, we’re mostly self-taught”. It was found that the gap between current practices and capabilities, and those required to harness everybody’s best efforts, is wide in the organisation. On the education front alone, simple things like “awareness of the cost of downtime” and “how the data being collected is going to be used” can transform the motivation, performance and creativity of the asset operators/technicians.

Managing assets requires all aspects of training as well as appropriate documentation of the system. It was found that organisations tended to focus more on the ‘hardware’ part of the systems’ development process, putting less effort on the ‘soft’ part, that is, the training of how to operate and manage the system. People’s skills and abilities to use the system efficiently are very critical to ensure DQ in AM systems. If people do not have the skills and knowledge to control the system, then even a perfect system would not be able to produce high quality information. Lack of training can cause serious damage and have an adverse impact on information quality. Unfortunately, it is easy for organisations to find reasons/excuses for avoiding adequate training for the staff and management.


5.2.12 Communication and management feedback

Competitive asset intensive companies have reported that most of their asset improvements come from their workforce. Despite the fact that “people are our greatest asset”, evidence of the opposite was often found. People’s problems, people’s relationships, people’s aspirations and people’s personal agendas are seldom given the consideration appropriate to their importance in the successful implementation of an AM system. In fact, the problem needs to be stated more emphatically. Most system implementations neglect the people factor and, as a result, most systems ultimately fail to achieve the objectives upon which their original funding was justified.

To improve performance of people in any organisation, they should get feedback about the effectiveness of their performance. As the data recorder is not the data user, the data collection process could be significantly improved by motivating the data recorder with feedback on what has been done with the data. The positive feedback keeps operators motivated and they see a purpose in their continuing to do a good job. Negative feedback coupled with the information on what went wrong and where, would help operators correct their mistakes and know exactly where to concentrate to improve in future.

It appears through the interviews that the organisations continue to see the operators and technicians as skilled hands, rather than also having brains and being very sophisticated sensors. It was also found that field people within one organisation often generate the view that “year after year they filled out field data without feedback and a lot of them worked out that if they did nothing, nothing happens so why bother?”.

5.2.13 Motivation

The asset organisations are different from many businesses where separate personnel are engaged for data entry who are skilled and trained for their job. In engineering organisations, the task of data entry would normally be done by operators whose primary job is to operate and maintain assets and who are also expected to perform the job of entering data, observations and maintenance actions. The operator may see the data entry job as unnecessary and of low importance. It is important for the organisation to make them aware of the significance of entering correct and complete data. They should be informed about the far reaching consequence of good and bad data at the decision making stage; and how their job of entering data could have substantial impact on the work of other people and the organisation as a whole.

6 Conclusion

This paper included a proportion of survey findings. Nevertheless, these results suggest that while the organisations are concerned about the quality of data, there is a lack of scrutinised discussion on the various issues associated with DQ problems. More importantly, there is a disconnection between data custodians and data producers and high level data owners. Despite over half of engineering AM organisations in Australia have data management strategy in place, it appears that there was a lack of actions in most organisations to actually implement the required DQ improvement solutions. Over 44% of the AM organisations have no plans to implement any data management solutions in the near future.


Due to the lack of the relevant DQ software available for specific engineering AM system, the majority of participating organisations have not used any DQ tool in their AM practices. As engineering data itself is quite different to typical business-oriented data, there is a pressing need for the development of specific DQ solutions for engineering AM.

The survey findings are very different from those by PricewaterhouseCoopers which were mainly concerned with the strategic development of data management within financial institutions. Perhaps, the engineering AM organisations in Australia still adopt a reactive approach and only focus on the daily operations. A more comprehensive analysis will show the different attitudes and management strategies in relation to sizes of the organisations and the industries that they operate within. Further, these findings will be compared with the general DQ surveys.

This research provided a better understanding of DQ issues for AM and is assisting in identifying elements which will contribute towards the development of a DQ framework specific to engineering AM. This in turn will assist in providing useful advice for improving DQ in this area. As an increasing number of organisations are putting in resources in data management solutions, there is a growing need for suitable guidelines to help them develop appropriate strategies and employ appropriate tools. Perhaps this is why DQ research has become more critical.

Acknowledgement

This research was conducted through the Centre for Integrated Engineering Assets Management (CIEAM). The support of CIEAM partners is gratefully acknowledged.

References ARC Advisory Group (2004) Asset Information Management – A CALM Prerequisite,

White Paper, ARC Advisory Group, Dedham, USA. Ballou, D.P. and Pazer, H.L. (1995) ‘Designing information systems to optimize the

accuracy-timeliness tradeoff’, Information Systems Research, Vol. 6, No. 1, pp.51–72. Ballou, D.P., Wang, R.Y., Pazer, H.L. and Tayi, K.G. (1998) ‘Modeling information manufacturing

systems to determine information product quality’, Management Science, Vol. 44, No. 4, pp.463–485.

Batini, C., Catarci, T. and Scannapiceco, M. (2004) ‘A survey of data quality issues in cooperative information systems’, Tutorial Presented at the 23rd International Conference on Conceptual Modeling (ER2004), Shanghai, China.

Bever, K. (2000) ‘Understanding plant asset management systems’, Maintenance Technology, July–August, pp.20–25.

Bouzeghoub, M. and Peralta, V. (2004) ‘A framework for analysis of data freshness’, Proceedings of the First ACM International Workshop on Information Quality in Information Systems (IQIS 2004), Paris, France.

British Standards Institute (2004) PAS 55: Asset Management. Part 1: Specification for the Optimised Management of Physical Infrastructure Asset.

Cavaye, A.L.M. (1996) ‘Case study research: a multi-faceted research approach for IS’, Information System Journal, Vol. 6, pp.227–242.

CIEAM (2003) Business Plan V1.0, Centre for Integrated Engineering Asset Management, Brisbane, Australia.


Davis, D. and Cosenza, R.M. (1988) Business Research for Decision Making, 2nd ed., Boston, PWS-Kent.

Desisto, D. (2004) Bad Sales Data is Worse than No Data at All, Research Paper, Gartner Research, Stamford, USA.

Eerens, E. (2003) Business Driven Asset Management for Industrial and Infrastructure Assets, Le Clochard, Australia.

English, L.P. (1999) Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits, Willey and Sons, New York, USA.

Firth, C. (1996) ‘Data quality in practice: experience from the frontline’, The Proceedings of 1996 Conference of Information Quality, Cambridge, USA.

Fox, C., Levitin, A.V. and Redman, T.C. (1994) ‘The notion of data and its quality dimensions’, Information Processing and Management, Vol. 30, No. 1, pp.9–19.

Giannoccaro, A., Shanks, G. and Darke, P. (1999) ‘Stakeholder perceptions of data quality in a data warehouse environment’, Australian Computer Journal, Vol. 31, No. 4, pp.110–117.

Huang, K., Lee, Y. W. and Wang, R.Y. (1999) Quality Information and Knowledge, Prentice-Hall, Upper Saddle River, NJ.

IPWEA (2002) International Infrastructure Management Manual, Australia/New Zealand Edition, The Institute of Public Works Engineering, Australia.

Ives, B., Olson, M., and Baroudi, J.J. (1983) ‘The measurement of user information satisfaction’, Communications of the ACM, Vol. 26, No. 10, pp.785–793.

Kahn, B.K., Strong, D.M. and Wang, R.Y. (2002) ‘Information quality benchmarks: product and services performance’, Communications of the ACM, Vol. 45, No. 4, pp.184–192.

Klein, H.K. and Myers, M.D. (1999) ‘A set of principles for conducting and evaluating interpretive field studies in information systems’, MIS Quarterly, Vol. 23, No. 1, pp.67–93.

Koronios, A. (2006) Foreword of Challenges of Managing Information Quality in Service Organisations, Idea Group Inc., USA.

Koronios, A. and Haider, A. (2003) ‘Managing engineering assets: a knowledge based asset management methodology through information quality’, E-Business and Organisations in the 21th Century, pp.443–452.

Koronios, A., Lin, S. and Gao, J. (2005) ‘A data quality model for asset management in engineering organisations’, Proceedings of the 10th International Conference on Information Quality (ICIQ 2005), 4–6 November, Cambridge, MA, USA, pp.27–51.

Kriebel, C.H. (1979) ‘Evaluating the quality of information systems’, in Szysperski, N. and Grochla, E. (Eds.): Design and Implementation of Computer Based Information Systems, Sijthroff and Noordhoff, Germantown.

Levitin, A.V. and Redman, T.C. (1998) ‘Data as a resource: properties, implications and prescriptions’, Sloan Management Review, Vol. 40, No. 1, pp.89–101.

Lin, S., Gao, J. and Koronios, A. (2006) ‘Key data quality issues for enterprise asset management in engineering organisations’, International Journal of Electronic Business Management (IJEBM), Vol. 4, No. 1, pp.96–110.

Liu, L. and Chi, L. (2002) ‘Evolutionary data quality- a theory-specific view’, Proceedings of the 7th Intl. Conf. on Information Quality, Massachusetts Institute of Technology, Cambridge, MA, pp.292–304.

Mitroff, I.I. and Linstone, H.A. (1993) The Unbounded Mind: Breaking the Chains of Traditional Business Thinking, Oxford University Press, New York.

Naumann, F. (2002) Quality-Driven Query Answering for Integrated Information Systems, LNCS 2261, Springer-Verlag, Berlin, Germany.

Neely, P. and Pardo, T. (2002) Teaching Data Quality Concepts Through Case Studies, Center for Technology in Government, Albany.

Neely, P., Gao, J., Lin, S. and Koronios, A. (2006) ‘The deficiencies of current data quality tools in the realm of engineering asset management’, Proceedings of AMCIS, Acapulco, México.


Orr, K. (1998) ‘Data quality and system theory’, Communications of the ACM, Vol. 41, No. 2, pp.66–71.

Sandberg, U. (1994) ‘The coupling between process and product quality – the interplay between maintenance and quality in manufacturing’, Euromaintenance, Amsterdam.

Saraph, J.V., Benson, P.G. and Schroeder, R.G. (1989) ‘An instrument for measuring the critical factors of quality management’, Decision Sciences, Vol. 20, No. 4, pp.810–829.

Saunders, D. (2004) ‘Innovation in asset management – achieving a nexus of cost and capability’, UDT Pacific 2004, Hawaii, USA.

Segev, A. (1996) ‘On information quality and the WWW impact a position paper’, Proceedings of 1996 Conference of Information Quality, 25–26 October, Cambridge, MA, USA.

Shanks, G. and Darke, P. (1998) ‘Understanding data quality in a data warehouse’, Australian Computer Journal, Vol. 30, No. 4, pp.122–128.

Snitkin, S. (2003) ‘Collaborative asset lifecycle management vision and strategies’, Research Report, ARC Advisory Group, Dedham, USA.

Spires, C. (1996) ‘Asset and maintenance management – becoming a boardroom issue’, Managing Service Quality, Vol. 6, No. 3, pp.13–15.

Steed, J.C. (1988) ‘Aspects of how asset management can be influenced by modern condition monitoring and information management systems’, IEE Research Article, The Institution of Electrical Engineers, UK.

Strong, D.M. (1997) ‘IT process designs for improving information quality and reducing exception handling: a simulation experiment’, Information and Management, Vol. 31, pp.251–263.

United States General Accounting Office (US GAO) (2004) Water Infrastructure: Water Utility Asset Management, GAO-04-461, Washington DC.

Wand, Y. and Wang, R.Y. (1996) ‘Anchoring data quality dimensions in ontological foundations’, Communications of the ACM, Vol. 39, No. 11, pp.86–95.

Wang, R.Y. (1998) ‘A product perspective on total data quality management’, Communications of the ACM, Vol. 41, No. 2, pp.58–65.

Wang, R.Y. and Strong, D.M. (1996) ‘Beyond accuracy: what data quality means to data consumers’, Journal of Management Information Systems, Vol. 12, No. 4, pp.5–33.

Wang, R.Y., Storey, V.C., and Firth, C.P. (1995) ‘A framework for analysis of quality research’, IEEE Transactions on Knowledge and Data Engineering, Vol. 7, No. 4, pp.623–640.

Woodhouse, J. (2003) ‘Asset management: concepts and practices’, Research Article, The Woodhouse Partnership, Kingsclere, UK.

Xu, H., Nord, J.H., Nord, G.D. and Lin, B. (2003) ‘Key issues of accounting information quality management: Australian case studies’, Industrial Management and Data Systems, Vol. 103, No. 7, pp.461–470.

Yin, P.K. (2003) Case Study Research: Design and Methods, 3rd ed., Thousand Oaks, Sage, CA.

Documents

Developing a data quality framework for asset management ... · Developing a data quality framework for asset management 101 ... organisational decisions together with the increasing