Organising Accountabilities for Data Quality Management A Data Governance Case Study
ABSTRACT Enterprises need corporate data quality management (DQM) that combines business-driven and technical perspectives to respond to strategic and operational challenges that demand high-quality corporate data. Hitherto, companies have assigned accountabilities for DQM mostly to IT departments. They have thereby ignored the organisational issues that are critical to the success of DQM. With data governance, however, companies implement corporate-wide accountabilities for DQM that encompass professionals from business and IT. This study examines a large organisation that has adopted an ad-hoc data governance model to manage its data. It was found that its DQM efforts were hampered mainly by the lack of clear roles and responsibilities and the lack of mandate to carry out data quality improvement initiatives. In order to promote effective DQM, this research identifies a data governance structure with the emphasis on collaboration between business and IT to support organisations.
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ABSTRACT: An organization depends on quality information for effective operations and decision-making. However, fundamental questions still remain as to how quality should be defined and the specific criteria that should be used to evaluate information quality. Previous work adopted either an intuitive, empirical, or theoretical approach to address this problem; however, we believe that an integrated research approach is required to ensure both rigour and scope. This paper presents an information quality framework based on semiotic theory, the linguistic theory of sign-based communication, to describe the form-, meaning-, and use-related aspects of information. This provides a sound theoretical basis both for defining quality categories, previously defined in an ad-hoc manner, based on these different information aspects and for integrating the different research approaches required to derive quality criteria for each category. The goal of our work is to provide an approach to defining information quality that is both theoretically grounded and practical that can serve as a basis for further research in data quality assessment and decision support.01/2005;
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ABSTRACT: The dominant market for enterprise resource planning (ERP) vendors has traditionally been the largest of multinational corporations. Until recently, most vendors (SAP, PeopleSoft, Oracle, etc.) have promoted a “one size fits all” solution built on “industry best practices.” This approach forced organizations to either conform to the “best practices” and configurations suggested by vendors and implementation consultants or embark on extremely costly reconfiguration of their ERP package. The study reviews the concepts of control, coordination, and their trade-offs plus Bartlett and Ghoshal’s topology of firm strategy. Human resource issues are introduced as examples of organization elements that may or may not conform to the enterprise design structure within coordination and control. Finally, the concepts of control and coordination and the Bartlett and Ghoshal topology are combined to create a firm strategic orientation which is then matched to an ideal ERP configuration or enterprise information architecture.Business Process Management Journal 07/2001; 7(3):205-215.
- The Academy of Management Review 10/1989; 14(4):532-550. · 6.17 Impact Factor
Organising Accountabilities for Data Quality Management
– A Data Governance Case Study
Kristin Weber+, Lai Kuan Cheong*, Boris Otto+, Vanessa Chang*
+Institute of Information Management
University of St. Gallen
9000 St. Gallen, Switzerland
*School of Information Systems, Curtin Business School
Curtin University of Technology
Kent Street, Bentley,
Western Australia 6102, Australia
Abstract: Enterprises need corporate data quality management (DQM) that
combines business-driven and technical perspectives to respond to strategic and
operational challenges that demand high-quality corporate data. Hitherto,
companies have assigned accountabilities for DQM mostly to IT departments.
They have thereby ignored the organisational issues that are critical to the success
of DQM. With data governance, however, companies implement corporate-wide
accountabilities for DQM that encompass professionals from business and IT. This
study examines a large organisation that has adopted an ad-hoc data governance
model to manage its data. It was found that its DQM efforts were hampered mainly
by the lack of clear roles and responsibilities and the lack of mandate to carry out
data quality improvement initiatives. In order to promote effective DQM, this
research identifies a data governance structure with the emphasis on collaboration
between business and IT to support organisations.
Companies are forced to continuously adapt their business models. Global presence
requires harmonised business processes across different continents, customers ask for
individualised products, and service offerings must be industrialised [cf. KÖ06]. These
factors certainly impact the business process architecture and the IT strategy of
organisations. Ultimately, however, data of high quality is a prerequisite for fulfilling
those changing business requirements and for achieving enterprise agility objectives
[NL06a]. In addition to such strategic factors, some operational domains directly rely on
high-quality corporate data, such as business networking [Ve00; Ma04; Te04], customer
management [ZG03; RC05; CM06], decision-making and business intelligence [SZW03;
PS05], and regulatory compliance [Fr06].
Data quality management (DQM) focuses on the collection, organisation, storage,
processing, and presentation of high-quality data. In addition, there are organisational
issues that must be addressed, such as maintaining sponsorship, managing change and
expectation, defining accountabilities, enforcing mandates, and handling political issues
[Wa98a; En99; Ep06; Th06]. Despite those organisational aspects, responsibility for
improving data quality and managing corporate data is often assigned to IT departments
[Fr06]. Also, many companies try to cope with data quality (DQ) issues by simply
implementing data management or data warehouse systems. Surveys on data
warehousing failures reveal that organisational rather than technical issues are more
critical to their success [WFA04].
Successful DQ programs identify the organisational processes behind DQ [BN07]. In
order to address both organisational and IT perspectives an integrated approach to DQM
is required. With data governance, companies implement corporate-wide accountabilities
for DQM that encompass professionals from both business and IT. Data governance
defines roles and assigns accountabilities for decision areas to these roles. It establishes
organisation-wide guidelines and standards for DQM and assures compliance with
corporate strategy and laws governing data.
There are only few academic resources about data governance. Apart from a few DQM
approaches dealing with accountabilities [Re96; En99], an elaborate analysis of the
interaction of roles and responsibilities, and the design of decision-making structures is
missing. For our research, we therefore incorporate data governance sources from
consultants, analysts and practitioners [e.g., Sw05; De06a; DL06a; MS06; NL06b; Ru06;
The conclusion from these sources is that data can be managed more effectively and
successfully through the adoption of a data governance structure. This paper outlines a
case study of an Australian utility company (“Water Inc.”1) to determine the justification
for formal data governance. Our main contribution is to demonstrate how formal data
governance structures contribute to successful DQM based on this case study. The paper
exposes how the state of the art in data governance was adopted by the company in order
to build a virtual, boundary-spanning organisation for managing data quality effectively.
The remainder of the paper is structured as follows: Section 2 introduces related work on
data quality management and data governance. It provides an overview of formal data
governance structures. Section 3 outlines the research methodology and the case study
company “Water Inc.”. Section 4 describes the initial situation at “Water Inc.” and
details its data governance approach. Section 5 discusses the insights of the case study.
The last section, Section 6, summarises the paper and its contribution.
2.1 Data Quality Management and Data Governance
We refer to data quality management as quality-oriented data management, i.e., data
management focusing on collecting, organising, storing, processing, and presenting
high-quality data2. The importance of organisational issues, such as maintaining
sponsorship, managing change and expectation, defining accountabilities, enforcing
mandates, and handling political issues, result from conflicts between different
departments, lines of business, and legal entities in which DQM operates. DQM is
boundary-spanning and provides many stakeholders (e.g., CxOs, sales, controlling,
procurement, IT, business units, customers, public authorities) with high-quality
corporate data by balancing their different interests (e.g., company-wide requirements,
laws and regulations vs. local and regional differences). Because of these particularities
of DQM, large multi-business companies are likely to have difficulties with
institutionalising DQM, i.e., defining accountabilities, assigning people accountable for
DQM within the organisational structure, and enforcing DQM mandates throughout the
Data governance – as part of DQM – addresses these particular issues within corporate
structures. Data governance specifies the framework for decision rights and
accountabilities to encourage desirable behaviour in the use of data3. Hence, data
governance encompasses two aspects: first, establishing accountabilities for DQM; and
second, defining corporate-wide guidelines and standards for DQM. This paper focuses
on the first aspect, because this aspect of data governance causes difficulties in most
1 Name of the company changed by the authors.
2 The term data is often distinguished from information by referring to data as “raw” or simple facts and to
information as data put in a context or data that has been processed [HLW99; PS05]. In line with most data or
information quality publications, we use the terms data and information interchangeably throughout the paper.
3 In the absence of academic definitions of data governance, this definition was adapted from the IT
governance definition of [We04].
companies. Little practical guidance and few case studies of successful implementations
exist. Furthermore, established accountabilities are the pre-requisite for the second data
governance aspect – defining and implementing guidelines and standards.
Academic research on data governance is in its infancy. DQM approaches, such as Total
Data Quality Management (TDQM) [Wa98b; Wa98a; HLW99], insufficiently address
the allocation of accountabilities. They mainly outline DQM activities and decision
areas. TDQM defines only one accountable role, the information product manager,
which ensures that relevant, high-quality information products are delivered to
information consumers. Few DQM approaches, such as Total Quality data Management
(TQdM) by [En99] and DQ for the information age by Redman [Re96] deal with more
than one accountable organisational position or role, roles related to several
organisational levels, and their tasks and responsibilities.
However, an elaborate analysis of the interaction of roles and responsibilities, and the
design of decision-making structures is missing. Hence, companies might find it difficult
to establish and maintain organisational structures designed to assure and sustain high-
quality data throughout the enterprise. Findings of a recent survey among data
management professionals indicate that data governance is rarely adopted [Ru06]. Only
8% of respondents had deployed a data governance initiative, 17% were in the design or
2.2 Organising Data Quality Management
Hierarchical organisational structures fail to support corporate DQ in situations where
data is used across organisational boundaries [Th06, 25], such as ensuring regulatory
compliance or supporting global process harmonisation. Therefore, data governance
establishes a “virtual organisation”. It defines roles4 and their responsibilities for DQM
across organisational boundaries. It establishes committees5 to make important unbiased
DQM-related decisions to achieve the best result for the whole organisation.
For identifying the roles and organisational bodies involved in DQM, Thomas [Th06,
81ff] distinguishes three kinds of data governance approaches. In Governance via
Management managers execute decision-rights and set data-related rules. The
governance organisation is the existing organisational structure. Responsibilities for
DQM are not formalised. Governance via Stewardship is a more formal approach that
provides separate roles and responsibilities. The governance organisation is made up or a
hierarchy of data stewards and data owners. Data stewards typically define, create, or use
data and may formulate rules. The Governance via Governance approach clearly
distinguishes between governors (make rules and resolve issues) and data stewards
(work with data, ensure compliance with rules, and raise issues). Governance bodies
complement hierarchical management structures to address boundary-spanning DQM
4 A role bundles different business tasks that are carried out by a single person (employee) or an organisational
unit as well as their area of responsibility and competencies.
5 A committee is a politically-neutral organisational body that unites stakeholders from different parts of the
Data governance surveys [Ru06], case studies [De06b; DL06b], and reports and books
from analysts and consultants [Sw05; DL06a; MS06; NL06b] follow that governance via
governance approach. These sources distinguish between three and five organisational
roles building the data governance organisation. Dyché and Levy [DL06a] and English
[En99] describe more specialised roles – they distinguish twelve and nineteen roles
The analysis of these sources results in a set of four typical roles and one committee –
the data quality board. Table 1 describes the roles and the committee. It provides a short
description, the level and part of the organisation to which the roles typically belong, and
the alternative names found in the sources. Names in brackets only partly match with
either the description or organisational assignment.
Role Description Organisational
Executive or senior
CEO, CFO, CIO)
funding, advocacy and
oversight for DQM
Decides for corporate-
wide standards and
Strategic information steward
[En99], executive level [NL06b],
executive sponsor [MS06],
(executive council) [De06b]
Business information stewardship
team [En99], data governance
council [DL06a; MS06], data
governance committee [Ru06],
GRCS board [De06b], trustee
council [DL06b], (legislative level)
Master data coordinator [Sw05],
director of data management
[DL06a], chief steward [MS06],
corporate steward [Ru06], lead
stewards [De06b], (data czar)
Information professionals [Re96],
business information steward
[En99], business data steward
[DL06a], subject area steward
[NL06b], master data lead [Sw05],
domain steward [Ru06], business
steward [MS06], subject matter
Database steward & information
architecture steward [En99],
technical steward [MS06], source
system data steward [DL06a]
by chief steward,
business unit and
IT leaders as well
as data stewards
Puts the board’s
decisions into practice,
enforces the adoption of
standards, helps establish
DQ metrics and targets
Details the corporate-
wide DQ standards and
policies for his or her
area of responsibility
from a business
business unit or
data element definitions
and formats, profiles
source system details and
data flows between
Table 1: Set of Data Quality Roles
The actual number of roles may vary from company to company. However, the roles
presented build a balanced and useful set when focusing on the strategic notion of
corporate DQM. The data governance organisation is virtual, i.e., it does not create a
new organisational structure; it aligns and coordinates existing roles and cooperates with
established committees and roles, such as IT governance boards, central purchasing
committees, data architects or data security officers.
Against this background, this paper describes a case study of an Australian utility
company. Several incidents that took place related to the lack of maintenance on network
asset were the main business driver for starting a DQM initiative. Data for network
assets need to be of high quality for the maintenance program to be planned efficiently.
This paper outlines the process of implementing a data governance organisation within
the company and how it benefited from a formal data governance (via governance)
approach in its DQM initiative.
3 Research Methodology
3.1 Case Study Research
We adopted a case study research method which is particularly suitable for
understanding phenomena within their organisational context [BGM87; Ei89; Yi02].
Case study research allows researchers to carry out their studies in a natural setting, learn
about the actual process of managing data quality and generate theories from practice.
This also allows researchers to answer the ‘how’ and the ‘what’ questions in order to
understand the nature and complexity of the processes taking place. This is an
appropriate research method for an area that had few previous researches [BGM87]. As
outlined above, this is particularly true for data governance. Furthermore, data
governance is an emerging topic that needs further development and research [WO07].
The chosen utility company (“Water Inc.”) manages data from various disparate systems
or sources, and the data involves different line of business units from multiple users and
stakeholders. The current user base is more than 100 users and data quality is the priority
concern for the organisation. The data collected from interviews with the IT Managers
and Data Managers were primarily qualitative in nature. The interviews were recorded
and later transcribed and analysed. The interview questions were designed to ascertain
the IT view of data-related issues and problems and the business view of data-related
issues and problems within the organisation. In addition, the study also investigated the
resolution and mediation methods that the organisation employed; and the deployment of
such methods. Additional information was also gathered from reviewing data
management documentation of the organisation. The documentation review provided
insight into the style of data quality management, methodology used for developing and
enhancing application systems, and the interaction between IT and business.
3.2 Water Inc.
Water Inc. is a large utility organisation in Australia with complex data integration
issues. This organisation provides essential services to approximately 890,000 industrial,
commercial, and residential customers. Its main mission is to provide reliable supply by
maintaining the network and to restore the supply in the event when the supply was
interrupted. It is also responsible for building new network to meet the demands of
existing and future customers.
It employs more than 1,850 core staff with an asset base of nearly AU$3 billion. It
operates in a regulated market to ensure quality of service and the accessibility of its
network to all service retailer and supplier. Recently, Water Inc. was split into four
different business entities (regional and metropolitan retail business, generation business
and network/infrastructure (asset) management). Through this restructure, it had been
charged with AU$2.23 million of investment to increase the network reliability by 25%
over the next four years.
Water Inc. is obliged to regularly report to a government regulatory authority. The
government regulatory authority requires maintenance plans and proof that the company
had performed its duties in maintaining public safety, ensuring reliable supply, and
efficient management of its network infrastructure. The building and maintenance of the
network infrastructure requires the cooperation of several divisions: the asset
management division (setting the strategy for managing network assets), field services
(packaging of inspection and maintenance program), and works delivery (planning and
construction of new infrastructure).
A few years ago, several incidents related to the lack of maintenance on network asset
affected public safety and reliability of supply. This resulted in an inspection of the
conditions of the network assets. Data for these assets need to be current and accurate so
that a maintenance program can be planned efficiently. With this maintenance program,
the condition of the assets can be accurately determined and aging assets can be targeted
for replacement ensuring the reliability of the service network.
Water Inc. is also in the process of migrating its asset related data from its legacy asset
management systems to Ellipse, an Enterprise Resources Planning (ERP) system and is
also planning to replace its custom-built Geographical Information System (GIS) with an
off-the-shelf GIS. The successful migration of these legacy systems depends largely
upon the quality of the asset and spatial data. For example, in order to locate assets the
XY coordinates and GPS references must be accurate; in order to identify the number of
customers affected by an asset malfunction the connectivity of the network must be
accurate. Recently, a dedicated Data Management department was created to manage
asset data and its related information systems for the Asset Management division. This
department is responsible for ensuring data quality and providing strategic direction for
the asset related data initiatives. It consists of three teams: First, the Data Management
(DM) team is responsible for providing strategic and tactical direction on data related
issues, conducting quality assurance and data cleansing activities; second, the Data
Services team enters data for asset related application systems (distribution network) and
provides underground asset information to external parties conducting trenching
activities; and third, the GIS Strategist team is responsible for the strategic direction of
any spatial data related issues, such as positional accuracy and graphical representation
4 Data Governance at Water Inc.
This research focuses on the activities within the DM team at Water Inc. This team acts
as data stewards. It has knowledge of the business processes and an in depth knowledge
of the asset related information systems. Currently, it is facing difficulties with managing
asset related data as an enterprise asset. The DM team feels it is managing data in a
reactive and ad hoc manner, it has no direct access to its source database, it has
difficulties obtaining consensus on data related issues, its data improvement projects
were mostly overrun and over budget, and any attempt to set data-related standards had
not been taken seriously by other groups of the business and IT. In light of this, the DM
team started a data governance initiative and the following remediations were instigated
to address these issues:
Reactive data management. The DM team establishes a data quality strategy
to improve and maintain data quality. Based on the strategy, a work plan for
IT related project and resource management is set-up.
Asset data not easily accessible. The asset data is stored in several disparate
databases and maintained by different application systems.
Data improvement project mostly overrun and over budget. A dedicated
project manager is appointed to liaise between the business and IT and to
ensure that the data stakeholders’ expectation and communication are
managed effectively. There is still a lack of IT tools for data profiling and
Difficulty in setting data standards. Data standards are important for the
migration of data from legacy systems to the new ERP system [CS01]. The
DM team plans to purchase a metadata repository.
Difficulty in obtaining consensus on data related issues. Data users are
spread across the organisation, spanning different divisions. A data
custodianship policy was drawn up but was difficult to put into action as it
did not have a governance structure to enforce this policy.
This data governance model corresponds to the governance via stewardship approach
[Th06]. However, this form of governance is not working for the DM team due to the
following reasons: first, a lack of mandate from senior executives results in no power to
act; second, sometimes DQM projects have no priority as senior executives do not
understand their importance; and third, a lack of clear roles and responsibilities.
Obviously, there is no connection between those at the operational level who knows the
problem and those who have the power to make decisions but are removed from the
problem. In order to address the issue, the DM team put together a plan for the
governance via governance approach of data governance with executive support of the
Asset Management General Manager. It resulted in a data governance organisation based
on the following roles and responsibilities:
Data Governance Council. Membership of this council consists of
executives from various divisions who have an interest in the management
of asset data. They are responsible for endorsing policies, resolving cross
divisional issues, engaging the IT council at the strategic level, strategically
aligning business and IT initiatives, and reviewing budget submission for IT
and non IT related projects.
Data Custodian. Asset data is managed by the data custodian on behalf of
Water Inc. He or she is responsible and accountable for the quality of asset
data. The data custodian resolves issues raised in user group meetings. If
issues become political and impacts stakeholders from other divisions, they
are escalated to the DG council level. The data custodian is also responsible
for endorsing data management plan, endorsing data cleansing plan,
ensuring data is fit for purpose, converting strategic plans into tactical plans,
change management, and stakeholder management.
Data Steward. Data stewards have detail knowledge of the business process
and data requirements. At the same time they also have good IT knowledge
to be able to translate business requirements into technical requirements.
They are led by the data custodian and are responsible for carrying out the
tactical plans. They act on behalf of the data custodian in stakeholder
management, change management, asset related information systems
management and project management. They manage user group meetings,
train and educate data users.
User Groups. Data stakeholders from various divisions are invited to the
user group meetings. These key data stakeholders consist of people who
collect the data, process and report off the data. Technical IT staff is also
invited to these meetings so that their technical expertise is available during
the meeting. This is also a venue where urgent operational data issues can be
tabled. The data users are responsible for reporting any data related issues,
requesting functionality that would help them collect data more efficiently,
and specifying reporting requirements.
The data governance structure in Figure 1 shows the business engagement with IT at the
strategic, tactical and operational levels. This level of engagement ensures that IT and
business are kept informed and IT initiatives align with the business data governance
Figure 1: Data Governance Roles at Water Inc.
The data governance structure at Water Inc. provides a structured framework for
mitigating the risks of DQM. It is scalable to include other divisions as the data
governance efforts within the DM team mature. The data custodianship and user groups
structure can be adopted by other division with the data governance council acting as the
‘organisational glue’. Water Inc. had shown that business and IT need to work together
in order to manage corporate data effectively.
The roles and committee used in the data governance structure correspond on the whole
to the set described in Section 2.2. The data governance council corresponds to the data
quality board, the data custodian to the chief steward and the data stewards correspond to
the business data stewards. The Asset Management General Manager is the executive
sponsor of the data governance initiative. IT technical staff fills the role of the technical
data steward. Because of the operational nature of the user groups they have no
equivalent in a typical governance structure.
The DM team had provided insights into the difficulties in managing data as corporate
asset without proper authority. The following summarises the findings of this research:
The justification for formal data governance. This study had shown that
managing the data quality of enterprise data is not effective without a formal
data governance model. The reason for this is because of the lack of clear
roles and responsibilities among data stakeholders. Data governance also
assists business in engaging IT (vice versa) to manage corporate data
The process of setting up a formal data governance program. The first step
to setting up a formal data governance program is to determine a data
governance structure. The structure provides escalation authority and a
transparent decision-making process. Roles and responsibilities are defined
so that members within the data governance structure are held accountable
for their actions. Water Inc. had largely achieved this, however as this
structure was recently introduced the success of its implementation cannot
be determined in this study.
Ability to carry out actions as a result of a formal data governance
structure. Given the clear structure, the DM team is able to purchase a data
profiling and metadata repository tool. The data profiling tool will allow the
DM team to discover anomalies more efficiently. The metadata repository
tool captures information about data so that it can be accessed by the whole
organisation. Metrics measuring the quality of data had also been developed.
The publication of these metrics will help management to determine the
success of data improvement initiatives.
Simple data governance structure and framework. The DM team did not
want a cumbersome framework that could cause bottlenecks and delays in
existing and future projects. With a proper structure and framework, the DM
team is able to steer strategic projects to conform to and maintain good data
governance. Nevertheless, Water Inc. moved from the governance via
stewardship to the governance via governance approach.
6 Conclusion and Future Work
Companies need data quality management that combines business-driven and technical
perspectives to respond to strategic and operational challenges demanding high-quality
corporate data. Data governance specifies the framework for decision rights and
accountabilities as part of corporate-wide DQM.
This paper investigates whether effective data quality management can be achieved
without formal data governance. Some insights into data governance initiatives in a large
utility organisation were obtained from this study. This paper underscores the
importance of a data governance structure together with policies and procedures for
managing data effectively. A data governance framework also enables collaboration
from various levels of the organisation and it also provides the ability to align various
data related programs with corporate objectives. This paper highlights that data
governance provides a structured framework for mitigating the risks of data
This research has thrown up many questions in need of further investigation. Given that
Water Inc. had only recently introduced a data governance structure, a longitudinal study
would give a better indication of the benefits and success of the data governance
structure implementation. This case study was conducted in isolation. Future research
could investigate multiple case studies and compare the implementation of data
governance programs between organisations of different sizes.
With this paper we encourage IT and business to work together to achieve high quality
data. The case study had shown that IT has the technical know-how but business
sponsorship is important to give data quality programs visibility and direction.
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