The Value of Good Data - A Quality Perspective
A Framework for Discussion
Tony O'Brien
1
, Arun Sukumar
1
and Markus Helfert
2
1
Sheffield Business School, Sheffield Hallam University, Howard Street, Sheffield, U.K.
2
Department of Computing, Dublin City University, Dublin, Ireland
Keywords: Data Quality, IT Governance, Data Assurance, IT Management, Cost Benefits, Information Value
Management.
Abstract: This study has highlighted the benefits and value of quality information and the direct consequences
associated with low quality data. This paper also describes a number of taxonomies which may be used to
classify costs relating to both the consequences of low quality data and the costs of improving and assuring
on-going data quality. The study then provides practical examples of data quality improvement initiatives
undertaken within two large organisations. Finally a data governance model is proposed centring on three
inter-related fundamental elements namely: People, Processes and Data, where any attempt to improve the
quality of data within any organisation must be focussed around these three essential elements.
1 INTRODUCTION
Many researchers within the context of management
planning and information systems have identified
the importance of data (Davenport, 1998; Davenport
et al., 2001; Galliers and Newall, 2001; Davenport
and Harris 2002; Newall, Huang, Galliers and Pan
2002; Davenport, Harris and Cantrell 2004). From
this research a realisation has grown that
organisations that are able to collect, analyse and act
on data in a strategic manner, are in a position to
gain a competitive advantage within their industries,
leading in some cases to domination in these areas
(Davenport, 2006). This form of information
management known as ‘analytics’ stresses that
successful organisations are those that take action
from their information to inform their strategic
decision making Davenport (1998); Davenport et al.,
(2001); Davenport (2006); Davenport and Harris
(2007) Davenport (2009), establishing along the way
a ‘fact-based culture’ (Harris 2005a; Harris 2005b;
Harris 2007).
If this ever expanding focus on ‘intelligent’
business intelligence and management information is
so crucial to organisational strategy, then the
requirement to have quality data becomes even more
paramount in manufacturing planning (Gustavsson
and Wanstrom (2009: 326) as well as information
systems (Davenport, Harris and Cantrell 2004: 23;
Stenmark 2004: 1; Economist Intelligence Unit
2006: 2, 16; Foley and Helfert 2010: 477;
Davenport, Harris and Morison 2010: 1). Over the
last two decades data quality has been identified as a
major concern for many enterprises Redman (1995);
English (1998); Redman (1998); English (1999);
Loshin (2001); Redman (2001); Eckerson (2002);
Redman (2002); Redman (2004); English (2009),
none more so than those operating enterprise
resource planning and information systems (Deloitte
1999).
A report from The Data Warehouse Institute
estimated that data quality problems costs US
business $600 billion a year (5% of the American
GDP) in postage, printing and staff overhead costs
alone, whilst the majority of the senior managers in
those companies affected remained unaware
(Eckerson, 2002: 3). More recently English (2009:
4-15) outlined a catalogue of corporate disasters
emanating from poor quality business information
amounting to ‘One and a Quarter Trillion Dollars’
(English 2009: 15). During 2009 a survey of 193
organisations sponsored by Pitney Bowes, 39% of
which had revenues in excess of US $1 billion,
reported that a third of the respondents rated their
data quality as poor at best, whilst only 4% reported
it as excellent (Information Difference, 2009: 4).
A further survey found that less than one third of
organisations regularly monitor data quality (Hayter
555
O’Brien T., Sukumar A. and Helfert M..
The Value of Good Data - A Quality Perspective - A Framework for Discussion.
DOI: 10.5220/0004616805550562
In Proceedings of the 15th International Conference on Enterprise Information Systems (IVM-2013), pages 555-562
ISBN: 978-989-8565-60-0
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
2010: 22), whilst a Gartner report stated that
“Through 2011, 75% of organisations will
experience significantly reduced revenue growth
potential and increased costs due to the failure to
introduce data quality assurance” (Fisher 2009: 6).
Strong parallels have been within the research
literature between the concept of a planning and
information system and that of a manufacturing
system (Strong et al., 1997:104; Wang, 1998: 59 and
latterly Pham Thi and Helfert, 2007: 6). The
principle elements are highlighted below within
what may be termed an Information Process Model
see Table 1 to compare and contrast the various
elements:
The ‘Manufacturing’ or ‘Factory’ analogy is a
useful model in that it takes a conceptual over-view
of both generic manufacturing and information
systems to identify ways in which established
quality principles may be applied to the input and
process elements ensuring that information products
in the form of outputs conform to the requirements
of their relevant customers. Strong, Lee and Wang
(1997:104) identified three key roles within a data
manufacturing system:
Data Producers: Generate data
Data Custodians: Manage, store and process data
Data Consumers Use data and information
Within this context, however, one needs to be aware
that the end products from manufacturing and
information systems have differing implications,
with the information production process viewed as
potentially a more complex process than its physical
equivalent (Pham Thi and Helfert 2007: 6). The
outputs from a factory are unique one-off products
which can be consumed only once, whether they are
finished goods or components requiring further
work. The overall effects of poor manufacturing are
somewhat limited, normally requiring a scrap and
re-work operation. Some longer-term detrimental
implications may occur including customer
dissatisfaction or product contamination, but even
these will normally be relatively localised and time-
constrained. Output in the form of data or
information products can be consumed in an infinite
number of ways and be re-cycled continually. Poor
data can act like a virus infiltrating all aspects of an
enterprise’s operations, re-occurring again and
again, or lay hidden undetected within sub-systems
in perpetuity. Data may also be used in ways for
which it was not created or intended, causing
potential misalignment, errors or misinterpretations,
resulting in potentially dangerous or catastrophic
decision making (Senge, 1992: 7; Orna, 2005: 44,
144-150; Mutch, 2008: 53).
2 RESEARCH APPROACH
Our research process to develop the proposed
framework can be seen as elements of Design
Science-oriented research process (Braun et al.
2005; Hevner et al. 2004). In this paper we scoped
the problem and based our findings on reviewing
relevant literature and feedback from case studies.
The literature was collected primary from journals
and prominent book contributions related to design
science. The literature review was complemented by
a series of discussion-type focus group meetings
with domain experts sharing experiences and
challenges. This approach attempted to generate
discussion and interaction to confirm our
framework. The use of a design science oriented
research approach in this environment provided the
study with a considerable degree of richness. From
the outset certain important notions and impressions
emerged from the discussions and the analysis and
these were subsequently developed as key findings.
In the following we present first findings from our
literature review and conceptual framework.
3 BENEFITS AND VALUE
OF DATA QUALITY
The second element to be considered when
evaluating data quality is its impact and value to the
business. The aspect of business value in relation to
IS has been discussed in numerous papers. For
instance, Gustafsson et al. (2009) have presented a
comprehensive model that aims to explain the
business impact with three generic elements: IT,
organizational impact, and business value. This
model serves as background model for data quality
impact. Other related frameworks have been
presented in the literature aiming to refine this
generic model (Borek et. al., 2011).
The model is supported by strong evidence that
data quality has a considerable effect on decision-
making in organizations. This section will therefore
focus on the data quality value in decision-making.
For instance, Keller and Staelin (1987) indicate that
increasing information quantity impairs decision
effectiveness and, in contrast, increasing data quality
improves decision effectiveness. Jung et al.
conducted a study to explore the impact of
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Table 1: Information Processing Model.
Generic
Process
Manufacturing
System
Generic Information
System
ERP Environment
Input Raw materials Raw data
People- Processes-
Data
Process/
Operations
Production
line
Information system ERP Database
Output
Physical
products
Information products Information-People
representational data quality (which comprises the
data quality dimensions interpretability, easy to
understand, and concise and consistent
representation) on decision effectiveness in a
laboratory experiment with two tasks with different
levels of complexity (Jung and Olfman, 2005).
Furthermore, Ge and Helfert (2008) show that the
improvement of data quality in the intrinsic category
(e.g. accuracy) and the contextual category (e.g.
completeness) can enhance decision quality.
Further studies elaborate this theme. English
(1999) divides data quality costs into three main
characteristics, costs caused by low data quality,
assessment and inspection costs incurred to verify if
processes are performing properly and process
improvement and prevention costs. Loshin (2001)
focusses upon the effects of low quality data in
terms of its impact over time in relation to the
traditional levels of organisational decision making.
Shorter term operational impacts covering aspects of
a system for processing information together with
the costs of maintaining such operational systems
involving elements of detection, correction, reworks
and ultimate prevention. Medium term tactical
aspects which attempt to anticipate issues and
problems and finally long term planning where the
impact of poor data quality can delay important
strategic decisions resulting in lost opportunities and
overall poor strategic decision making (Loshin
2001).
Haug et al., (2011) followed up the research of
Ge and Helfert (2007), who identified three major
components relating to this area: (1) information
quality assessment, (2) information quality
management, and (3) contextual information quality
as shown in Table 2.
Following on from the themes of English (1999)
and Loshin (2001) the Eppler and Helfert model
dissects data quality costs into two major
classifications relating to those costs incurred as a
result of low quality data and the consequential costs
of improving or assuring ongoing data quality. Each
classification then consists of subordinate categories
relating to the direct and indirect costs of poor data
and the prevention, detention and repair costs
associated with data quality improvement processes
as shown in Table 3.
Each subordinate category is then further
subdivided into six quality costs element and seven
cost improvement elements. In order to investigate
the business value of data quality, we follow IS/IT
business value studies that show how IS/IT impacts
on business processes and/or decision-making. A
business process can be defined “a specific ordering
of work activities across time and place, with a
beginning, an end, and clearly identified inputs and
outputs: a structure for action” (Davenport, 1993).
Porter and Millar argue that activities that create
value consist of a physical and an information-
processing component and each value activity uses
information (Porter and Millar, 1985).
In their integrative model of IS/IT business
value, Mooney et al. (1996) propose a process
framework for assessing the IS/IT business value.
They present a typology of processes that subdivides
business processes into operational and management
processes and argue that IS/IT creates business value
as it has automational, informational, and
transformational effects on the processes. Similarly,
Melville et al. (2004) see business processes and
business process performance as the key steps that
link organizational resources to organizational
performance. Data can be seen as an important
organizational asset as well as resource. Its quality is
directly related to business value and organizational
performance. In addition to measuring the effect on
business processes, organizational performance has
always been of consideration to IS/IT researchers
and practitioners, resulting in a plethora of
performance related contributions. Earlier
approaches focused, for example, on the economic
value of information systems (Van Wegen and De
Hoog, 1996). They were more recently detailed to
frameworks for assigning the impact of IS/IT to
businesses (Mooney et al., 1996; Melville et al.,
2004). These IS/IT oriented frameworks have
resulted in an abundance of recommendations,
frameworks and approaches for performance
measurement systems (Folan and Browne, 2005).
TheValueofGoodData-AQualityPerspective-AFrameworkforDiscussion
557
Table 2: “Classification of information quality problems identified in literature”. Source: Ge and Helfert (2007). Adapted by
(Haug et al., 2011).
Data Perspective User Perspective
Context-
independent
Spelling error
Missing data
Duplicate data
Incorrect value
Inconsistent data format
Outdated data
Incomplete data format
Syntax violation
Unique value violation
Violation of integrity constraints
Text formatting
The information is inaccessible
The information is insecure
The information is hardly retrievable
The information is difficult to aggregate
Errors in the information transformation
Context-
dependent
Violation of domain constraint
Violation of organization’s business rules
Violation of company and government
regulations
Violation of constraints provided by the
database administrator
The information is not based on fact
The information is of doubtful credibility
The information presents an impartial view The
information is irrelevant to the work
The information consists of inconsistent meanings
The information is incomplete
The information is compactly represented
The information is hard to manipulate
The information is hard to understand
Table 3: “A data quality cost taxonomy”. Source: Eppler and Helfert (2004). Adapted by (Haug et al., 2011).
Data quality
costs
Costs caused by low data
quality
Direct costs
Verification costs
Re-entry costs
Compensation costs
Indirect costs
Costs based on lower reputation
Costs based on wrong decisions or actions
Sunk investment costs
Costs of improving or
assuring data quality
Prevention
costs
Training costs
Monitoring costs
Standard development and deployment costs
Detection
costs
Analysis costs
Reporting costs
Repair costs
Re
p
air
p
lannin
g
costs
Repair implementation costs
It has been recognized that there are two
perspectives on value: objective and perceived
value, which results in different data quality and
value measures and value perceptions for particular
stakeholders (Fehrenbacher and Helfert, 2012). To
evaluate the value of data quality and develop
suitable indicators, we suggest combining the work
on business processes and decision quality with the
work on performance indicators, developing a
framework for analyzing business value of data
quality. This is illustrated in Figure 1. The value
propositions of data quality are manifold. It ranges
from generating direct business value by providing
information of high quality, reducing complexity,
ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems
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improving customer loyalty, improving operational
performance, reducing costs and so on. Due to its
critical importance in enterprises, data quality affects
many areas of organizational performance and may
deliver business value simultaneously to
stakeholders.
4 CASE EXAMPLES
This article has provided illustrations from the
literature to highlight examples of the costs of poor
data quality and consequential benefits of related
improvement programmes. A further example of the
effects of such an initiative may be seen from a
practical data quality improvement programme
allied to an academic study carried out in
collaboration with an industry partner. This
organisation, a multi-business manufacturing
enterprise operating across sixty three factories and
offices within the United Kingdom, initiated a data
quality improvement programme in 2006 and over
the subsequent five years the quality of its overall
data as measured by a weighted KPI index showed
an overall improvement of 59%.
Looking at the cost taxonomy, whilst there was
no detailed analysis undertaken as to the detailed
financial effects of the underlying data quality
problems, the improvement initiative was
undertaken by existing staff using existing resources,
applying quality principles which evolved during the
overall process. These were basically ‘sunk costs’ in
that there were little or no marginal incremental
costs incurred as a direct consequence of the overall
initiative. Whilst it could be argued that such
resources could have been applied in other areas of
the business, the overall effects upon the business
mean that data was identified as a major
organisational resource and asset.
During the period the overall operating results
improved by 37%, with a 52% improvement in
operational order efficiency across purchasing,
manufacturing and sales/despatches. In addition the
underlying problems in processing supplier invoices
and successfully resolving customer invoice issues
improved by 72% and 53% respectively.
Whilst the links between the data quality
initiative and improved financial position could be
somewhat tenuous, it was widely acknowledged
within the organisation that the operational
improvements were a direct result of the
programme.
A similar study conducted more recently on a
large quasi-public sector organisation has again
highlighted the costs of poor data quality. The
organisation, used to be one of the largest public
sector organisation has recently been privatised and
has faced numerous problems relating to data quality
whilst providing its services. The study conducted in
the form of focus groups, highlighted a number of
key themes relating to data quality. The main themes
identified are given as follows,
Firstly, in the discussion among the cross section
of the work force, it was noted that data and
information governance were of low priority.
Employees’ awareness of data governance issues
and the associated responsibilities were low; the
communication channels that are used to highlight
Figure 1: Framework for analyzing business value of data quality.
TheValueofGoodData-AQualityPerspective-AFrameworkforDiscussion
559
and promote data quality issues are either non-
existent or clogged. Secondly, there was an absence
of any formal mechanism or a procedure to report
data problems. Employees who worked with the
Master Data Systems were not aware of any formal
procedures or mechanisms through which they can
report or correct faulty or incorrect data. One
attendant stated that “When I send payments to
subxxxxx, I am not even sure that the branch is still
open, I may actually be sending payments to the
wrong person or to a wrong branch”. Thirdly, the
organisation did not have formal structure in terms
of data stewardship or governance, data management
was done on an ad-hoc basis by senior managers and
specific roles and responsibilities relating to data
quality management were either absent or under-
developed. Fourthly, a more common theme
identified related to the use of local and informal
controls to manage data. Examples included the use
of local spreadsheets, storing mission critical data in
local drives, users writing their own macros to
automate some actions etc. These issues though
provide convenience and expedite the transactions in
local areas can often lead to information security
risks and compliance issues. Lastly, among the
discussions it was noted that the middle level
managers were not aware of ISO standards or best
practices associated with information security
management, They were aware of the need to
employ and use the current best practices available
within the information security management domain
but the knowledge to get further relevant
information or how to implement a organisation
wide data management program was lacking.
One of the positive aspects of the discussion was
that the senior management were aware of the data
quality issues and the pressures of compliance, they
are highly supportive in improving the current
practices and procedures but present organisational
culture and remains of public sector heritage is
making their task harder and less efficient. The
organisation is still developing key metrics or the
parameters which can identify the cost of the poor
data and poor data decisions
5 CONCEPTUAL FRAMEWORK
Fundamental to this study is the identification of
three conceptual elements seen to be key to any data
quality programme namely: People, Processes and
Data. This has been developed further to form the
basis of the conceptual framework (Figure 2)
detailed below. This research indicates that there are
a myriad of methods and solutions to improve data
quality in both the areas of transactional and master
data at various levels embracing both process and
people, with varying consequences and degrees of
success. Nicholaou (2004:44) identified that lack of
people training and failure to recognise the effects of
an ERP system on current business processes are the
most important culprits in problematical
implementations. Whilst all such initiatives have
enormous merit in themselves, they will not generate
long-term success or influence unless they can be
embedded. This study takes note of these theories
and practices that can improve and create quality
data, but focuses upon identifying how an
organisation may be able to create an environment
where data quality improvement initiatives may be
sustained. In this it accepts that there must be a
climate where such improvements should be sought-
after, generated, supported and implemented with
adequate resources
.
The conceptual framework depicted in Figure 2
above sets Data Quality firmly within the overall
context of Data Governance as part of an enterprise-
wide data strategy and acting as a route map through
the whole research. The initial triple inter-linked
framework developed from an intensive review of
the literature comprises the ’Data’ elements of
master data management, together with operational
and transactional data; ‘Process’ review and
improvement initiatives running in tandem with the
necessary system housekeeping procedures; together
with the ‘People’ elements of education and training,
personal development aligned with accessibility in
the form of Assistive Technology (hardware and
software techniques developed in order to assist
visually or physically disabled persons gain access
to information technology within the working
environment). During the research for this study it
became apparent that any enduring improvement is
predicated on making lasting changes to both
processes and individuals’ behaviour and to bring
about this, there has to be cultural and organisational
change mainly through the interaction of leadership
and management at all levels. The framework also
identifies how the process of producing quality
information derived from quality raw data has
parallels with a generic product manufacturing
process as discussed above. This useful analogy
between a production process and an information
system also has strong roots in the literature (Strong,
Lee and Wang 1997:104; Wang 1998: 59).
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Figure 2: Conceptual Framework
6 CONCLUDING REMARKS
The relationship between theory and practice,
research and action to attempt to enhance theoretical
knowledge whilst providing practical solutions as
expounded by Van de Ven and Johnson (2006) and
Van de Ven (2007) has been the overriding ambition
of this study. It can therefore be argued that this
research has made a contribution to theory whilst
also assisted in bringing about lasting fundamental
practical data quality improvements with real life
organisations
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