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The costs of poor data quality

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Abstract

Purpose: The technological developments have implied that companies store increasingly more data. However, data quality maintenance work is often neglected, and poor quality business data constitute a significant cost factor for many companies. This paper argues that perfect data quality should not be the goal, but instead the data quality should be improved to only a certain level. The paper focuses on how to identify the optimal data quality level. Design/methodology/approach: The paper starts with a review of data quality literature. On this basis, the paper proposes a definition of the optimal data maintenance effort and a classification of costs inflicted by poor quality data. These propositions are investigated by a case study. Findings: The paper proposes: (1) a definition of the optimal data maintenance effort and (2) a classification of costs inflicted by poor quality data. A case study illustrates the usefulness of these propositions. Research limitations/implications: The paper provides definitions in relation to the costs of poor quality data and the data quality maintenance effort. Future research may build on these definitions. To further develop the contributions of the paper, more studies are needed. Practical implications: As illustrated by the case study, the definitions provided by this paper can be used for determining the right data maintenance effort and costs inflicted by poor quality data. In many companies, such insights may lead to significant savings. Originality/value: The paper provides a clarification of what are the costs of poor quality data and defines the relation to data quality maintenance effort. This represents an original contribution of value to future research and practice.
doi:10.3926/jiem.2011.v4n2.p168-193 JIEM, 2011 – 4(2): 168-193Online ISSN: 2013-0953
Print ISSN: 2013-8423
The costs of poor data quality
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A. Haug; F. Zachariassen; D. van Liempd
The costs of poor data quality
Anders Haug, Frederik Zachariassen, Dennis van Liempd
University of Southern Denmark (DENMARK)
adg@sam.sdu.dk; frz@sam.sdu.dk; dvl@sam.sdu.dk
Received August 2010
Accepted January 2011
Abstract:
Purpose:
The technological developments have implied that companies store
increasingly more data. However, data quality maintenance work is often neglected,
and poor quality business data constitute a significant cost factor for many
companies. This paper argues that perfect data quality should not be the goal, but
instead the data quality should be improved to only a certain level. The paper
focuses on how to identify the optimal data quality level.
Design/methodology/approach:
The paper starts with a review of data quality
literature. On this basis, the paper proposes a definition of the optimal data
maintenance effort and a classification of costs inflicted by poor quality data. These
propositions are investigated by a case study.
Findings:
The paper proposes: (1) a definition of the optimal data maintenance
effort and (2) a classification of costs inflicted by poor quality data. A case study
illustrates the usefulness of these propositions.
Research limitations/implications:
The paper provides definitions in relation to
the costs of poor quality data and the data quality maintenance effort. Future
research may build on these definitions. To further develop the contributions of
the paper, more studies are needed.
Practical implications:
As illustrated by the case study, the definitions provided
by this paper can be used for determining the right data maintenance effort and
doi:10.3926/jiem.2011.v4n2.p168-193 JIEM, 2011 – 4(2): 168-193Online ISSN: 2013-0953
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A. Haug; F. Zachariassen; D. van Liempd
costs inflicted by poor quality data. In many companies, such insights may lead to
significant savings.
Originality/value:
The paper provides a clarification of what are the costs of
poor quality data and defines the relation to data quality maintenance effort. This
represents an original contribution of value to future research and practice.
Keywords:
data quality, master data management, data quality costs
1 Introduction
Data are used in almost all the activities of companies and constitute the basis for
decisions on operational and strategic levels. Poor quality data can, therefore, have
significantly negative impacts on the efficiency of an organization, while high
quality data are often crucial to a company's success (Madnick et al., 2004; Haug
et al., 2009; Batini et al., 2009; Even & Shankaranarayanan, 2009). However,
several industry expert surveys indicate that data quality is an area, to which many
companies seem not to give sufficient attention or know how to deal with efficiently
(Marsh, 2005; Piprani & Ernst, 2008; Jing-hua et al., 2009).
Vayghan et al. (2007) classify the data that most enterprises deal with in three
categories: master data, transactional data, and historical data. Master data are
defined as the basic characteristics of business entities, i.e. customers, products,
employees, suppliers, etc. Thus, typically, master data are created once, used
many times and do not change frequently (Knolmayer & Röthlin, 2006).
Transaction data describe the relevant events in a company, i.e. orders, invoices,
payments, deliveries, storage records etc. Since transactions are based on master
data, erroneous master data can have significant costs, e.g. an incorrect priced
item may imply that money is lost. In this context Knolmayer and Röthlin (2006)
argue that capturing and processing master data are error-prone activities where
inappropriate information system architectures, insufficient coordination with
business processes, inadequate software implementations or inattentive user
behaviour may lead to disparate master data.
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In spite of the importance of having correct and adequate data in a company, there
seems to be a general agreement in literature that poor quality data is a problem
in many companies. In fact, much academic literature claims that poor quality
business data constitute a significant cost factor for many companies, which is
supported by findings from several surveys from industrial experts (Marsh, 2005).
On the other hand, Eppler and Helfert (2004) argue that although there is much
literature that claims that the costs of poor data quality are significant in many
companies, only very few studies demonstrate how to identify, categorize and
measure such costs (i.e. how to establish the causal links between poor data
quality and monetary effects). This is supported by Kim and Choi (2003) who
state: “There have been limited efforts to systematically understand the effects of
low quality data. The efforts have been directed to investigating the effects of data
errors on computer-based models such as neural networks, linear regression
models, rule-based systems, etc.” and “In practice, low quality data can bring
monetary damages to an organization in a variety of ways”. According to Kim
(2002), the types of damage that low quality data can cause depend on the nature
of data, the nature of the use of data, the types of responses (by the customers or
citizens) to the damages, etc.
As such, companies typically incur costs from two sides when speaking of master
data quality. Firstly, companies incur costs when cleaning and ensuring high
master data quality. Secondly, companies also incur costs for data that are not
cleaned as poor master data quality might lead to faulty managerial decision-
making. The purpose of this paper is to provide a better understanding of the
relationship between such costs. To help determine the optimal data quality
maintenance efforts, the paper provides: (1) a definition of the optimal data
maintenance effort; and (2) a classification of costs inflicted by poor quality data.
In this context the paper argues that there is a clear trade-off relationship between
these two cost types and that the task facing the companies in turn is to balance
this trade-off.
The remainder of the paper is organized as follows: First, literature on data quality
is discussed in Section 2. Next, Section 3 proposes a model to determine the
optimal data maintenance effort and a classification of different types of costs
inflicted by poor quality data. Section 4 presents a case study to illustrate the
application of the proposition. The paper ends with a conclusion in section 5.
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2 Data quality literature
Firstly, this section makes a clarification of the term 'data quality' and then
provides a fundamental understanding of the impacts of poor quality data. Finally,
the section discusses existing models of the relationship between data maintenance
effort and costs inflicted by poor quality data.
2.1 Data quality
To understand the concept of ‘data quality’, to begin with a distinction between
data, information and knowledge may be appropriate. Popular definitions of such
terms have been made by Davenport and Prusak (1998), who define data as
“discrete, objective facts about events” and information as data transformed by the
value-adding processes of contextualization, categorization, calculation, correction
and condensation. Similar definitions are provided by Newell et al. (2002), who
define data as “providing a record of signs and observations collected from various
sources” and information as when “data are presented in a particular way in
relation to a particular context of action”. In contrast to ‘data’ and ‘information’,
the meaning of ‘knowledge’ is much more debatable, which is a discussion often
relating to whether knowledge is perceived as being of an impersonal and static
nature or being personal and related to action (Newell et al., 2002). However, a
deeper discussion about the meaning of the meaning of ‘knowledge’ is beyond the
scope of this paper, which, as mentioned, focuses on data quality.
Data quality is often defined as 'fitness for use', i.e. an evaluation of to which
extent some data serve the purposes of the user (e.g. Lederman et al., 2003; Tayi
& Ballou, 1998; Watts & Shankaranarayanan, 2009). Another way to understand
the concept of data quality is by dividing it into subcategories and dimensions. An
often cited definition is provided by Ballou and Pazer (1985), who divide data
quality into four dimensions: accuracy, timeliness, completeness, and consistency.
They argue that the accuracy dimension is the easiest to evaluate as it is merely a
matter of analysing the difference between the correct value and the actual value
used. They also argue that the evaluation of timeliness can be carried out in a
similar unproblematic manner. As for the evaluation of the completeness of some
data, this can also be done relatively straight forward, as long as the focus is on
whether the data are complete or not in contrast to defining the level of
completeness, e.g. the percentage of data completeness. On the other hand, an
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evaluation of consistency is a little more complex, since this requires two or more
representation schemes in order to be able to make a comparison.
Another data quality classification is provided by Wand and Wang (1996). They
limit their focus to intrinsic data qualities, of which they define four intrinsic
dimensions: completeness, unambiguousness, meaningfulness and correctness.
Wand and Wang (1996) take as their basis a paper, which features a review of
cited data quality dimensions, i.e. the comprehensive literature review of Wang et
al. (1995). Wang et al. (1995) summarize the most often cited data quality
dimensions as shown in Table 1.
Accuracy
25
Flexibility
5
Sufficiency
3
Informativeness
2
Reliability
22
Precision
5
Usableness
3
Level of detail
2
Timeliness 19 Format 4 Usefulness 3 Quantitativeness 2
Relevance
16
Interpretability
4
Clarity
2
Scope
2
Completeness
15 Content 3
Comparability 2
Understandability
2
Currency
9
Efficiency
3
Conciseness
2
Consistency
8
Importance
3
Freedom from bias
2
Table 1.Cited data quality dimensions”. Source: Wang et al. (1995).
Wang and Strong (1996) propose a data quality classification which divides data
quality into four categories: intrinsic, contextual, representational, and
accessibility. For each category they define a set of dimensions, 18 in all. The
definition by Wang and Strong (1996) is discussed by Haug et al. (2009) who
argue that 'representational data quality' can be perceived as a form of
'accessibility data quality' instead of a category of its own. Thus, Haug et al. (2009)
define three data quality categories: intrinsic, accessibility and usefulness. Levitin
and Redman (1998) provide another perspective by arguing that since processes to
produce data have many similarities to processes that produce physical products,
data producing processes could be viewed as producing data products for data
consumers. With a basis in this view of data as resources, Levitin and Redman
discuss how thirteen basic properties of organizational resources may be translated
into properties for data.
2.2 Impacts of poor quality data
The development of information technology during the last decades has enabled
organizations to collect and store enormous amounts of data. However, as the data
volumes increase, so does the complexity of managing them. Since larger and
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more complex information resources are being collected and managed in
organizations today, this means that the risk of poor data quality increases (Watts
& Shankaranarayanan, 2009). Another often mentioned data related problem is
that companies often manage data at a local level (e.g. department or location).
This implies the creation of 'information silos' in which data are redundantly stored,
managed and processed (Lee et al., 2006; Smith, 2008; Vayghan et al., 2007). In
this vein, Lee et al. (2006) argue that data silos imply that many companies face a
multitude of inconsistencies in data definitions, data formats and data values,
which makes it almost impossible to understand and use key data. From a solution
perspective, ERP systems have been promoted as a panacea for dealing with the
lack of data integration by replacing inadequately coordinated legacy systems
(Davenport, 1998; Knolmayer & Röthlin, 2006). However, it has been suggested
that data problems may get intensified when using ERP systems since the ERP
modules are intricately linked to each other, which is the reason why poor quality
data input in one module can affect the functioning of other modules negatively
(Park & Kusiak, 2005).
Poor quality data can imply a multitude of negative consequences in a company. To
start with, poor quality data that is not identified and corrected can have
significantly negative economic and social impacts on an organization (Ballou et al.,
2004; Wang & Strong, 1996). The implications of poor quality data carry negative
effects to business users through: less customer satisfaction, increased running
costs, inefficient decision-making processes, lower performance and lowered
employee job satisfaction (Kahn et al., 2003; Leo et al., 2002; Redman, 1998).
Poor data quality also increases operational costs since time and other resources
are spent detecting and correcting errors. Since data are created and used in all
daily operations, data are critical inputs to almost all decisions and data implicitly
define common terms in an enterprise, data constitute a significant contributor to
organizational culture. Thus, poor data quality can have negative effects on the
organizational culture (Levitin & Redman, 1998; Ryu et al., 2006). Poor data
quality also means that it becomes difficult to build trust in the company data,
which may imply a lack of user acceptance of any initiatives based on such data.
When focusing on clarifying the effects of poor quality data, it is clear that many
companies experience significant costs as a result of poor quality data, although
the exact extent of such costs is difficult to estimate. According to Redman (1998),
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studies to produce estimates of the total cost of poor data quality have proven
difficult to perform. Additionally, data quality research has not yet advanced to the
point of having standard measurement methods for any of these issues. On the
other hand, Redman (1998) claims that many case studies feature accuracy
measures, but he does not provide references or mentions if these are academic
studies. According to Redman (1998), measured at the field level, the reported
error rates are in the interval of 0.530%. Furthermore, Redman (1998) claims
that at least three proprietary studies have yielded estimates in the 8-12% of
revenue range, but informally 40-60% of the expense of the service organization
may be consumed as a result of poor data. Much indicates that the economic effect
of even small data inaccuracies can be very significant. Häkkinen and Hilmola
(2008) argue that marginal data inaccuracies (e.g. 1-5%) may not necessarily
represent a major problem in manufacturing, but that such inaccuracies will have
direct effects in terms of lost sales and operational disruptions in the after-sales
organizations.
In contrast to the apparent lack of large studies of data quality in academic journal
papers (Eppler & Helfert, 2004; Kim & Choi, 2003), many industry experts provide
such studies. These industry experts include Gartner Group, Price Waterhouse
Coopers and The Data Warehousing Institute, which claim to identify a crisis in
data quality management and a reluctance among senior decision-makers to do
enough about it (Marsh, 2005). Marsh (2005) summarizes the findings from such
surveys into the following bullet-points (quoted from: Marsh, 2005):
"88 per cent of all data integration projects either fail completely or
significantly over-run their budgets"
"75 per cent of organisations have identified costs stemming from dirty
data"
"33 per cent of organisations have delayed or cancelled new IT systems
because of poor data"
"$611bn per year is lost in the US in poorly targeted mailings and staff
overheads alone"
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"According to Gartner, bad data is the number one cause of CRM system
failure"
"Less than 50 per cent of companies claim to be very confident in the
quality of their data"
"Business intelligence (BI) projects often fail due to dirty data, so it is
imperative that BI-based business decisions are based on clean data"
"Only 15 per cent of companies are very confident in the quality of external
data supplied to them
"Customer data typically degenerates at 2 per cent per month or 25 per
cent annually"
"Organisations typically overestimate the quality of their data and
underestimate the cost of errors"
"Business processes, customer expectations, source systems and
compliance rules are constantly changing. Data quality management
systems must reflect this"
"Vast amounts of time and money are spent on custom coding and
traditional methods - usually fire-fighting to dampen an immediate crisis
rather than dealing with the long-term problem"
2.3 Data maintenance effort and costs inflicted by poor quality data
As mentioned in the introduction, although there seems to be agreement in
literature that the costs of poor data quality are significant in many companies,
only very few studies demonstrate how to identify, categorize and measure such
costs (Eppler & Helfert, 2004; Kim & Choi, 2003). In practice, low quality data can
bring monetary damages to an organization in a variety of ways.
Raman (2000) argues that evidence from previous studies shows that the quality
of point-of-sale data is often poor and that even at well-run retailers it cannot be
taken for granted. Raman offers a taxonomy of retail-data quality, quantifies these
costs to the extent possible, highlights the impact of data quality on Internet
retailing, and offers guidelines to managers for improving quality. The focus of the
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paper, however, is limited to: (1) the direct costs of scanning the wrong price of
items, (2) costs associated with inventory-data inaccuracy; and (3) cost of
phantom stock-outs. For the first-mentioned the consequence of inaccurate data is
simply a subtraction of the sum of overpriced items from the sum of underpriced
items. On the costs of inventory-data inaccuracy and phantom stock-out, Raman
only offers some estimates related to very specific contexts. Raman recommends
two steps to improve data quality, which in headlines can be formulated as: (1)
“companies should make greater use of the data that they have stored”; and (2)
“that companies start measuring data quality to the extent possible”.
Ge and Helfert (2007) analyse three major aspects of information quality research:
(1) information quality assessment, (2) information quality management, and (3)
contextual information quality. In relation to information quality assessment,
among others, Ge and Helfert classify typical information quality problems which
are identified by previous research, as shown in Table 2.
Data 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 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
Table 2.Classification of information quality problems identified in literature”. Source: Ge
and Helfert (2007).
On the issue of information quality management, Ge and Helfert (2007) argue that
this is an intersection between the fields of quality management, information
management and knowledge management. Finally, on the issue of contextual
information quality they provide an overview of which publications that relate to
different data application contexts, which include: database, information
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manufacture system, accounting, marketing, data warehouse, decision-making,
healthcare, enterprise resource planning, customer relationship management,
finance, e-business, World Wide Web and supply chain management.
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
Repair planning costs
Repair implementation costs
Table 3.A data quality cost taxonomy”. Source: Eppler and Helfert (2004).
Eppler and Helfert (2004) review and categorize the potential costs associated with
low quality data. They propose a classification framework and a cost progression
analysis to support the development of quantifiable measures of data quality costs
for researchers. To address the lack of literature on poor data quality versus costs,
according to Eppler and Helfert, “cost classifications based on various criteria can
be applied to the data quality field in order to make its business impact more
visible”. Based on a literature review, Eppler and Helfert identify 23 examples of
costs resulting from poor quality data, which amongst others are: higher
maintenance costs, excess labour costs, assessment costs, data re-input costs, loss
of revenue, costs of losing current customers, higher retrieval costs, higher data
administration costs, process failure costs, information scrap and rework costs and
costs due to increased time of delivery. Additionally, Eppler and Helfert identify 10
cost examples of assuring data quality, which are 1) information quality
assessment or inspection costs, 2) information quality process improvement and
defect prevention costs, 3) preventing low quality data, 4) detecting low quality
data, 5) repairing low quality data, 6) costs of improving data format, 7)
investment costs of improving data infrastructures, 8) investment costs of
improving data processes, 9) training costs of improving data quality know-how
and lastly 10) management and administrative costs associated with ensuring data
quality. Finally, Eppler and Helfert (2004) argue that data quality costs consist of
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two major types: improvement costs and costs due to low data quality. Based on
this, they devise a simple classification of data quality costs, as shown in Table 3.
3 Proposition
This paper extends the literature on data quality costs, especially the work of
Eppler and Helfert (2004), by proposing:
(1) A definition of the optimal data maintenance effort
(2) A classification of costs inflicted by poor quality data
The two propositions are defined and discussed in the following subsections.
3.1 Defining the optimal data maintenance effort
The first proposition of this paper is shown in Figure 1. The vertical axis indicates
the incurred, aggregated costs of dealing with poor quality data. The second and
horizontal axis deals with the quality of data. The two curves in the figure
represent costs inflicted by poor quality data and the costs of maintaining high data
quality, respectively. The costs inflicted by poor quality data are for example faulty
decisions based on poor data quality, whether this is of operational or strategic
character. The costs of ensuring and maintaining high data quality simply refer to
the work of assurance or improving data quality. The total costs associated with
data quality are the aggregated cost of the two explained curves. There are two
basic assumptions associated with Figure 1. Firstly, during data maintenance the
focus is on the most critical data (i.e. the ones with the highest payoff per
resources spent) before moving on to less critical ones. This implies that the first
work of assuring data quality would have the greatest effect, i.e. the costs inflicted
by poor quality data decreases exponentially. The second assumption is that the
costs of the efforts to ensure high data quality are not causally related to the their
importance, i.e. focusing on a set of poor quality data with great impact on costs is
not necessarily cheaper than focusing on data with little impact on costs. Thus, the
costs of assuring data quality is a linear relationship between data quality and
assurance costs.
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Costs of assuring
data quality
Data
quality
Costs inflicted by
poor Data quality
Running costs
Optimimum
Total costs
Figure 1.Total costs incurred by data quality on the company”.
What can be derived from Figure 1 is that the connection between costs inflicted by
poor quality data and costs of ensuring high data quality can be logically
categorized as a trade-off, which is a situation involving the loss of one quality in
return for gaining another quality. The central thesis here is that extensively
cleaning data, thereby ensuring high quality of the data, becomes less profitable at
some point. This is illustrated by the dotted line termed “total costs”.
Although Figure 1 seems to provide a very logical perspective on the estimation of
the optimal data quality maintenance efforts, there is still some way to go. To
apply the figure on an area of a company, the two types of costs needs to be
evaluated, i.e. the costs of maintaining data and the costs inflicted by poor quality
data. The first (costs of assuring data quality) is relatively easy to evaluate, since
this is simply a question of registering resources used on this work, i.e. internal
hours spent, consultant fees, software, etc. On the other hand, estimating the
costs inflicted by poor quality data is much more difficult because of the many
indirect and intangible effects associated with it. To support the task of estimating
the costs inflicted by poor quality data, the next section looks closer at the nature
of such costs.
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3.2 Estimating costs inflicted by poor quality data
To simplify the estimation of costs inflicted by poor quality data, costs are broken
down into two dichotomies. The first dichotomy relates to how visible the costs are,
namely direct versus hidden costs. This dichotomy is used in a great deal of
management accounting literature (Joshi at al., 2001; Srinidhi, 1992) as well as
data quality literature (Kengpol, 2001). Hidden costs are sometimes referred to as
expenses that are not normally included in the purchase price of equipment or a
machine, such as maintenance, supplies, training and upgrades. For this reason
terms such as strategic activity based costing (Kaplan & Cooper, 1998), total cost
of ownership (Ellram & Siferd, 1993) and cost-to-serve (Braithwaithe & Samakh,
1998) have been invented and invested in to include all costs associated with a
given action taken by a company or department. Although this definition of hidden
costs can be claimed to be a valid one, this paper will define hidden costs as costs
that the company is incurring but which management is not aware of. An example
of such a cost could be the faulty decisions stemming from not knowing the
profitability of products. Contrarily, direct costs can be defined as costs that are
immediately present and visible to management. This could for example be faulty
delivery addresses for registered customers, resulting in wrong deliveries.
The second dichotomy relates to the level on which the costs are inflicted. More
specifically, the second dichotomy refers to the fact that data can be viewed on
both an operational and a strategic level. On an operational level, data are used as
a basis for carrying out tasks and making decisions, which normally have a
relatively short time span. An example of operational data can be delivery
addresses, pricing of products and other order processing related data. Shipping
products to the customer in the right quantity, at the right address and at the right
time can be considered as an operation, in which it is paramount that the company
can rely on the data being of the right quality. On a more strategic level, data can
be seen as a basis for making decisions in companies, where the decisions can be
regarded as having a relatively longer time span when compared to operational
data. For example, these data can be cost allocations in a company, which is used
to determine the pricing of products. If the company is not able to track and locate
both its variable and its fixed costs, it will not be possible for the company to
determine a given price on a given product. Another example of strategic data
could be cost-benefit analyses pertaining to product profitability. If a company
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presently produces three products, it is vital to know which products can be
deemed profitable and which can be deemed non-profitable.
Here, it should be made clear that operational and strategic data can be one and
the same. That is, in themselves data are not operational or strategic, but data can
only be recognized as operational or strategic because management in a given
company perceives them that way. As a result, while some data can be seen as
strategic in one company, other companies might regard them as operational. It
should also be noted that besides the operational and strategic levels, a tactical
level also exists in between. This level has not been included, as the purpose of
this paper is to provide an initial and better understanding of the relationship
between such costs. Future research should investigate what happens when this
dichotomy is changed to include three levels.
In Figure 2, the two dichotomies are combined to provide some general categories
of costs of poor quality data. The four categories generated by the two dichotomies
will be subsequently discussed.
E.g. long lead times, data
being registered multiple
times, employee
dissatisfaction, etc.
E.g. manufacturing errors,
wrong deliveries, payment
errors, etc.
Hidden
costs
Direct
costs
Effects of poor
quality data on
operational tasks
Effects of poor
quality data on
strategic decisions
E.g. focus on wrong
customer segments, poor
overall production planning,
poor price policies, etc.
E.g. few sales, low
efficiency, problems in
keeping delivery times, etc.
Figure 2.Four types of costs incurred by poor quality data”.
In Figure 2, it is highlighted that depending on the two dimensions of direct costs
versus hidden costs and operational data versus strategic data, four types of costs
incurred by poor data quality can be operationalized. In the figure, examples of
each type of these costs are given. When the cost can be classified as a direct cost
with an operational view on data, costs can for example be associated with poor
order processing data. Shipping the wrong product in the wrong quantity at the
wrong time to the wrong customer at the wrong price are examples of mistakes
that will eventually incur costs for the company. Another classical example is the
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direct cost associated with poor production quality, where it is obvious that faulty
data produces products that are not assembled properly, for example. Contrarily,
when the cost can be categorized as a hidden cost, but still with an operational use
of data, the company will incur costs on a day-to-day level of which they are in fact
aware. Costs associated with this are for example long lead times. A company that
has been producing products with the same lead time for a long period of time runs
the risk of taking this for granted, not realizing that the lead times could actually
be shorter if the data were corrected. Such data pertain to for example poor data
input to Material Requirements Planning (MPR) systems.
When costs are direct but are instead considered from a strategic data perspective,
costs incurred stem from operations, which the company knows are inefficient and
have a big impact on the strategic direction in which the company is currently
heading. An example of this could be the awareness of having lost sales in recent
periods due to decision-making based on unreliable data. Not running the newly
placed strategic inventory location properly could be an example of costs incurred
due to data not being sufficiently cleaned and organized. Lastly, when costs are not
visible to management and data are regarded as being strategic, management
knows that some data are faulty, but does not realize that this has consequences
for the company’s overall profit potential. In this case, an example would be a
wrong allocation of costs (typically fixed costs) regarding calculating individual
product profitability. Not tracking and allocating costs properly would lead to wrong
decision-making such as pricing policies and a focus on the wrong customer
segments due to products appearing profitable while others appear unprofitable,
even though they might in fact be profitable.
3.3 Application of the contributions
To utilize the contributions of this paper, it is important to define a clear
delimitation of the data in focus. The focus when using the two proposed models
could for example be on item data, sales order data, production planning data, etc.
The narrower the scope, the easier it is to estimate costs associated with poor
quality data. On the other hand, if using a scope that is too narrow, important data
may be neglected. Thus, the use of the proposed models may be the investigation
of a series of datasets separately, followed by placing these investigations in a
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common context. In practice, the focus of such data investigations may even be
placed on particular database tables.
4 Case study
In this section, a case study illustrates the way in which a company attempted to
improve the quality of their data. The company in question is a manufacturer,
developer and supplier of a wide range of automotive spare parts for vans, cars,
and trucks with total revenue of around 130.000 Euro per year. The focal company
primarily targets the automotive spare parts market, although the company applies
these products for a variety of different uses all over the world. In a normal
situation, a new car is equipped with parts produced by the same auto
manufacturer. For example, a radiator installed in the car is typically the same
brand as the car itself. Some parts in cars, vans and trucks are, however, more
prone to breaking, compared to others. These are for example the heating and
cooling systems of the car. Typical causes for the breakdown of these car parts are
normal wear and tear, but also (head-on) collisions with other cars. The original
car manufacturers actually produce these spare parts as well, but have not
specialized in the cheap production of these. As this is a costly endeavour for the
original car manufacturer, an after sales market for car, van and truck spare parts
exists. The case company currently employs workers in countries all over the world
and has 18 subsidiaries. Before turning to the empirical data, a short section
denoting the methodological choices taken is given.
4.1 Methodology
A qualitative and exploratory research design was undertaken in order to
investigate the level of master data quality by the focal company (Stake, 2000).
The research method consisted of ethnographic observations and semi-structured
interviews, because the investigated data are relatively unstructured and analysis
of them involves explicit interpretation (Silverman, 2005). Using semi-structured
interview protocols gave the interviewer the flexibility to focus on what the
company believed was the most important problems as regards their current level
of data quality. In terms of data coding, within case analysis was used as a means
to structure, reduce and make sense of the data collected (Miles & Hubermann,
1994).The single case study can be reported as being a holistic, representative
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case design with a single unit of analysis (the case company) (Yin, 2009). The case
is representative because the case company is typical of many other major
manufacturing companies as the company has had problems in managing their
data quality, which is also the main sampling criterion. As this type of case study
methodology pertains to a single case, it is only possible to generate an analytical
generalization. A statistical generalization is, therefore, not achievable, as this type
of research can be regarded as exploratory research. This is a limitation of the
paper when seen from a statistical viewpoint.
One researcher spent significant time in the actual focal company, participating for
6 months both at official meetings as an observer and in unofficial, unstructured
interviews with the company’s chief operating officer (COO), business intelligence
managers, supply chain manager and several sales managers. It should be
mentioned here that a confidentiality agreement was signed with the company
leaving all information anonymized. As one of the researchers participated in the
meetings, the researcher runs the risk of blurring his role as a researcher with his
role in the company. In order to minimize bias as much as possible, triangulation in
the form of a combination of interviews, direct observation, documentation and
participant observation was carried out (Yin, 2009). With respect to qualitative
validity criteria, credibility was ensured by checking the authenticity of the case
description with the case company, after which any discrepancies were changed.
Recognizing that two social contexts are never identical, transferability can only be
ensured by applying the results to other cases in future research. Even though only
one of the authors spent time at the case company, dependability was sought to be
ensured by comparing all three authors’ interpretations of the results, and working
out any disagreements on interpretation. Finally, confirmability can only be
ensured through the blind peer-review process.
4.2 Analysis
Because a wide range of cars, vans and trucks exists, many different types of
spare parts have to be produced by the focal company. In fact, the company
currently has a stock-keeping unit (SKU) count of approximately 8,500. Combined
with the many countries to which the focal company is selling products, customers
exceed 10,000. This creates a complex situation for the organization, in which data
to be managed are abundant with pricing of products being a particularly time-
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constraining activity. The company is currently employing two full-time business
intelligence managers whose sole task is to clean data and price products. Products
are priced on a range of factors with a benchmarking towards prices of customers
being the main one. As the market for the company’s products can be as seen as a
commodity market, precise pricing of the app. 8,500 products is particularly
important. During recent years, Chinese competitors have entered the market,
which has had the consequence that the focal company has been put under
pressure in terms of maintaining profitability. The company, therefore, decided to
improve and subsequently maintain various data elements in the organization,
thereby hopefully ensuring less costs associated with bad data quality.
The two business intelligence managers knew that the company incurred quite
heavy costs due to costs inflicted by relatively simple operational tasks. Such tasks
pertained to for example shipping products to the right delivery address or bar
coding the products with correct ID tags. Additionally, many of the customers of
the company had had individual pricing agreements with the company but these
agreements were not systematized, which meant that the sales people of the
company used a lot of time on retrieving and processing individual and unique
customer data. Besides costs that were readily visible, the business intelligence
managers also knew that the company was incurring hidden costs associated with
operational tasks. For example, both managers would spend a lot of their time
recording, retrieving, systematizing and updating pricing information gathered from
the company’s nearest competitors. These data were important for the company
since this allowed them to price their products according to the current market
situation. This updating of prices involved, however, many countries with many
individual pricing lists being gathered from many different competitors. This often
meant that the two managers together with other marketing personnel were
carrying out uncoordinated, duplicative work. That is, data were at times registered
twice. Considering the quite time-consuming work load for this data storage
activity, the company would incur many hidden costs pertaining to this operational
task. The COO of the company estimated that the costs of these unnecessary
activities were the equivalent of payroll costs for two full-time marketing
employees. In an attempt to improve data quality on this operational level, the
company attempted together with one of the authors of this paper to develop a
pricing model, in which data for pricing products would happen automatically
through a computer programme instead of having several employees trying to
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update prices manually. It was a clear goal for the organization to improve data
quality pertaining to pricing to a certain level. That is, intelligence managers, the
COO and several marketing related employees expressed that it would never be
possible nor expedient to obtain 100% correct prices. Instead, the data quality
improvement initiative should be seen as a way to get better, but not perfect,
prices. Trying to obtain perfect prices would mean a far too time-consuming data
discipline, in which the company was not interested. This aim of data quality
improvement goes well with the statement earlier in Figure 2, in which
improvement of data quality is only applicable to a certain level. Trying to maintain
data quality over a certain threshold will result in costs pertaining to data discipline
inexpediently exceeding costs saved by better decision-making due to better data
quality.
The company also incurred both direct and hidden costs on a strategic level. Direct
costs were mainly associated with supply chain or logistical operations. In all, the
company had 18 subsidiaries, each with their own assigned inventory location.
Besides these, minor inventory locations were located in the different countries to
which the company was supplying. At the time of the empirical investigation, the
company had engaged in a long debate concerning the centralization versus
decentralization of inventories. These arguments were, however, difficult to reach
an agreement upon since cost data pertaining to the use of the inventory locations
were either missing or faulty. Due to this the company knew that unnecessary
costs occurred when they moved goods to and from different inventories. This
meant costs regarding unnecessary transportation of goods, not meeting delivery
deadlines and that either stock-outs or limited capacity at inventory settings were
incurred by the company. Lastly, the company also incurred costs at a hidden,
strategic level. That is, the company essentially had no calculations of customer
profitability, but only had rough guidelines such as the volume sold and
contribution margin. This meant that the sales staff would spend time on servicing
customers with many time-consuming demands and a relatively small profit gain.
Not knowing the costs of having products produced, the sales staff were also quite
often not capable of determining the optimal price that the customer should pay for
the product. The company estimated carefully that such costs contributed with 5-7
% of total fixed costs of the company. In order to improve data quality at this
strategic level, the company set out trying to gather information on costs related to
inventory capacity and transportation costs. This resulted in a decision to centralize
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inventories, thereby removing several smaller inventories located especially in
Europe. The COO and business intelligence managers and the supply chain
department all expressed satisfaction with this decision considering the quality of
the data that were used in order to make the final decision. There were, however,
also doubts as to whether this decision actually would be the best for the company
logistically as data collected sometimes were not sufficiently reliable. This doubt
stemmed from the fact that cost data from certain inventory locations were either
missing or were obviously wrong. It was, however, judged by the company that it
would be a too great a data exercise to gather precise information on all inventory
locations. Instead, the costs concerning an adequate level of information versus a
not too expensive data collection process were sought balanced.
In the case described, the proposed matrix (Figure 2) provided a perspective on
costs of poor data quality, which contributed to a better understanding of this
issue. More specifically, the matrix helped dividing data costs into cost types of
different concreteness, which helped in evaluating the accuracy of the optimal data
assurance effort.
5 Conclusions
This paper proposed a model for determining the optimal level of data maintenance
efforts from a cost perspective. More specifically, the optimum is found by adding
the costs of data maintenance work to the costs inflicted by poor quality data (such
as errors in sales orders, delivery addresses, etc.). As the model shows, the
optimal level of data maintenance is not to achieve perfect data, but only a level
where the costs of the maintenance work do not exceed savings from the costs
inflicted by poor quality data. This data maintenance effort is dependent on the
characteristics of the particular company. Different industries have different
characteristics, i.e. the relation between costs of poor data quality and costs of
assuring data quality. For example, for airplane manufacturers the costs inflicted
by poor quality data may be very high compared to the costs of increasing the data
quality, while for a manufacturer of simple components the opposite may be the
case.
While the first dimension (i.e. costs of data maintenance) is rather straight forward
to calculate, the costs inflicted by poor quality data are much more difficult to
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define. To provide a better understanding of such costs, the paper proposed four
categories of such costs. The four categories were created by defining two
dimensions: Hidden versus direct costs and operational versus strategic
consequences of poor quality data. These four categories provide a better
understanding of how to estimate data quality related costs. Examples of effects of
poor data quality on operational tasks where costs can be considered as hidden are
long lead times and employee dissatisfaction. These types of costs are difficult to
track and the company might not notice that it is in fact incurring these costs.
When speaking of costs associated with manufacturing errors and wrong deliveries,
it was determined that what the company here is dealing with are direct costs due
to poor data quality. Contrarily, examples of effects of poor data quality on
strategic decisions on costs that are hidden are a focus on wrong customer
segments and poor price policies. Finally, direct costs associated with effects of
poor data quality on strategic decisions are for instance few sales and problems in
meeting delivery deadlines. However, estimates of costs related to poor quality
data would still be associated with great uncertainties. But, the more exact
estimates, the more the company will profit from such work. This was also
empirically illustrated by the use of single case study.
Having defined the optimal effort for data maintenance and having provided some
clarification of how to understand the costs inflicted by poor quality data, the next
step is to make the model even more operational. This means that more detailed
methods for evaluating the different types of costs inflicted by poor quality data
need to be defined. The propositions presented in this paper represent the initial
ideas of a research project, currently ongoing at the University of Southern
Denmark. The focus of this research project is to understand how data quality is
related to the expenses of a company. To achieve such insights, the activities to be
carried out during 2010 include conducting a number of case studies, which is to
end up in a large questionnaire survey. The ideas presented in this paper represent
the initial foundation for this work.
To sum up, this paper has produced a better understanding of how to define the
optimal data maintenance effort and of the nature of costs inflicted by poor quality
data. Although these contributions are to be further elaborated on in future
research, in their present form they provide a better understanding of the topic
which hopefully aids companies in their data quality work.
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.
... The interest in data analysis rapidly grew in organizations as the quality of data impacts decision making and business strategy [66]. In 2016, a IBM study reports that poor data quality leads a US cost of $3 trillion per year [98]. ...
... L'analyse des données s'est alors rapidement développée au sein des entreprises qui souhaitent améliorer leur fonctionnement. De nombreux études montrent qu'une mauvaise analyse de données entraîne des coûts décisionnels très importants et cela pour la plupart des domaines [66,98,2]. Nous pouvons citer par exemple les hopitaux et les industries qui regroupent un grand nombre d'acteurs et d'actions [106]. ...
Thesis
As event data becomes an ubiquitous source of information, data science techniques represent an unprecedented opportunity to analyze and react to the processes that generate this data. Process Mining is an emerging field that bridges the gap between traditional data analysis techniques, like Data Mining, and Business Process Management. One core value of Process Mining is the discovery of formal process models like Petri nets or BPMN models which attempt to make sense of the events recorded in logs. Due to the complexity of event data, automated process discovery algorithms tend to create dense process models which are hard to interpret by humans. Fortunately, Conformance Checking, a sub-field of Process Mining, enables relating observed and modeled behavior, so that humans can map these two pieces of process information. Conformance checking is possible through alignment artefacts, which associate process models and event logs. Different types of alignment artefacts exist, namely alignments, multi-alignments and anti-alignments. Currently, only alignment artefacts are deeply addressed in the literature. It allows to relate the process model to a given process instance. However, because many behaviors exist in logs, identifying an alignment per process instance hinders the readability of the log-to-model relationships.The present thesis proposes to exploit the conformance checking artefacts for clustering the process executions recorded in event logs, thereby extracting a restrictive number of modeled representatives. Data clustering is a common method for extracting information from dense and complex data. By grouping objects by similarities into clusters, data clustering enables to mine simpler datasets which embrace the similarities and the differences contained in data. Using the conformance checking artefacts in a clustering approach allows to consider a reliable process model as a baseline for grouping the process instances. Hence, the discovered clusters are associated with modeled artefacts, that we call model-based trace variants, which provides opportune log-to-model explanations.From this motivation, we have elaborated a set of methods for computing conformance checking artefacts. The first contribution is the computation of a unique modeled behavior that represents of a set of process instances, namely multi-alignment. Then, we propose several alignment-based clustering approaches which provide clusters of process instances associated to a modeled artefact. Finally, we highlight the interest of anti-alignment for extracting deviations of process models with respect to the log. This latter artefact enables to estimate model precision, and we show its impact in model-based clustering. We provide SAT encoding for all the proposed techniques. Heuristic algorithms are then added to deal with computing capacity of today’s computers, at the expense of loosing optimality.
... "Dirty data" is a phrase that denotes out-of-date, inconsistent, or incomplete data [Zhou et al., 2011] that cannot be properly searched, aggregated, and analyzed. Operations on such data generate costs within the organization [Haug et al., 2011], and also reduce the credibility of the company in terms of the products sold [Qalati et al., 2021]. In addition, inconsistencies in the data are one of the causes of product returns, which, in turn, due to the need to organize additional transportation, negatively affects the environment [Kawa & Pierański, 2021]. ...
Article
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Background: Global product identification standards and methods of product data exchange are known and widespread in the traditional market. However, it turns out that the e-commerce market needs data that have not already received much attention, for which no standards have been established in relation to their content. Furthermore, their current quality is often perceived below expectations. This paper discusses the issues of product name and highlights its problems in the context of e-commerce. Attention is also drawn to the source of liability for erroneous data. Methods: The research methodology is based on the analysis of data of products available on the Internet through product catalog services, online stores, and e-marketplaces, mainly in Poland, but addresses a global problem. Three research scenarios were chosen, comparing product names aggregated by GTIN, starting with e-commerce sites and ending with product catalogs working with manufacturers. In addition, a scenario of name-photo compatibility was included. Results: The results show that the product name, which in the real world is an integral part of the product as it appears on the label provided by the manufacturer, in the virtual world is an attribute consciously or not modified by the service provider. It turns out that discrepancies appear already at the source - at the manufacturer's level - publishing different names for the same product when working with data catalogs or publishing on product pages contributing to the so-called snowball effect. Conclusions: On the Internet, products do not have a fixed name that fully describes the product, which causes problems in uniquely identifying the same products in different data sources. This in turn reduces the quality of data aggregation, search, and reliability. This state of affairs is not solely the responsibility of e-commerce marketplace vendors, but of the manufacturers themselves, who do not take care to publicize the unambiguous and permanent name of their products in digital form. Moreover, there are no unambiguous global guidelines for the construction of a full product name. The lack of such a template encourages individual interpretations of how to describe a product.
... Studies such as Klein (2002) have demonstrated that users often confuse information and technical problems with online websites, leading to assumptions that a system is failing when it could be due to poor quality information. Even where an organisation is aware of poor-quality information, the impact is often difficult to calculate beyond the cost of maintaining the data through improved processes (Haug et al., 2011). Petter et al. (2012, pp. ...
Thesis
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With the rise of the world wide web, many organisations publish large knowledge bases as online informative content, enabling access for their current and potential stakeholders, customers, and service users. Providing universal access to information is a key feature of many national laws, ensuring that content is accessible for the intended audience, however there is little focus on its informativeness. Whilst there are many prior academic and industry frameworks for assessing the success of information systems, many of these focus on facets of the system itself or task completion, rather than the quality of the content. Evolutions of the WCAG (Web Content Accessibility Guidelines) have guided practitioners towards accessibility, neglecting the other attributes of information quality.
... Poor data quality can negatively impact the banking and financial sectors (Haug et al., 2011). Given its importance, many researchers (e.g., Bruno et al., 2017;Issn & Dadashzade, 2018;Lutu & Motjolopane, 2012;Najjar & Bishu, 2005) have attempted to investigate the impact of poor data quality in the banking industry. ...
Conference Paper
In the era of big data analytics, data is widely recognised as a valuable asset that can enable organisations to achieve their strategic objectives. Despite that, banks are still struggling to maintain high-quality data. Prior studies show that a data governance programme can play a critical role in improving data quality. It can provide data quality professionals with a holistic approach to formally define policies, procedures and decision rights required for managing data quality in a more systematic manner. However, few empirical studies were conducted in this area. Therefore, the present paper aims to close this gap by investigating the data quality problem in the Omani banking industry to understand how various data governance mechanisms can address this issue. The study adopted a qualitative case study with semi-structured interviews and document reviews being used to collect data. A theoretical framework by Abraham et al. (2019) was adopted to guide the collection and analysis of the data. A thematic analysis (TA) by Braun and Clark was followed for data analysis. Findings of the study suggest that the data governance mechanisms, namely ‘performance measurement’, ‘compliance monitoring’ and ‘training’, have positively contributed to mitigating data quality issues in the Omani banking sector.
Chapter
Six Sigma is a process that uses data and statistical analysis to find out the defects in a product or a process, aims to improve quality, and reduces variability. It is a measure of quality that aims for near perfection. Six Sigma is an effort designed to transform society by making progress by focusing on out of box thinking to achieve ambitious and extended targets. The case company considered for this research is one of the leading hydraulics and pneumatics component manufacturing industries. The objective of this research is to reduce the defect rate in production using the Six Sigma methodology. Six Sigma utilizes Define, Measure, Analyze, Improve, and Control (DMAIC) methodology to determine the root cause and analyzes the data. This systematic methodology can be used for the existing research. The product selection was based on the rejection rate. The product with the highest rejection rate was considered for further study. The root causes for the problem were found and analyzed statistically using Minitab software. The optimum parameters were found out, and a pilot run was performed, and the improvements were documented. The control plan was made, and the review and audit were conducted after implementing the changes.KeywordsSix SigmaDMAICQualityMinitab
Chapter
Data Quality (DQ) plays a critical role in data integration. Up to now, DQ has mostly been addressed from a single database perspective. Popular DQ frameworks rely on Integrity Constraints (IC) to enforce valid application semantics, which lead to the Denial Constraint (DC) formalism which models a broad range of ICs in real-world applications. Yet, current approaches are rather monolithic, considering a single database and do not suit data integration scenarios. In this paper, we address DQ for data integration systems. Specifically, we extend virtual data integration systems to elicit DCs from disparate data sources to be integrated, using DC-related state-of-the-art, and propagate them to the integrated schema (global DCs). Then, we propose a method to manage global DCs and identify (i) minimal DCs and (ii) potential clashes between them.KeywordsData QualityData integrationDenial constraints
Article
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In recent years, the data quality in accounting information systems which bases on information technology has been brought the reliability of audit evidences to the forefront. In the reliability of audit evidences, the ability to access the source of the evidence, the source which it is obtained, the document of the source and the internal control structure of the audited company are important issues. Therefore, the audit evidences which created by the auditor should focus on the relevance of the evidence, the source of the evidence, the timeliness of the evidence, and the objectivity of the evidence. The purpose of this study is evaluating of the effect of data quality in accounting information systems on the reliability of audit evidences. So, a literature review related to the subject of this study was conducted to achieve this purpose. As a result of this literature review, a direct and complementary relationship was found between the data quality in the accounting information systems and the reliability of audit evidences.
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The effects of trust score measurement is web donor selection is evaluated in this study. The performance of the proposed method is conducted by running a prediction model on the imputed dataset. Thus, several experiments were carried out to quantify the impact of the prediction model via Root Mean Squared Error (RMSE) and F-Measure. The results demonstrate that the proposed method improves the performance of existing web donor selection. The results showed that the RMSE, prediction accuracy, and F-Measure are improved when the prediction model is trained with datasets imputed using the proposed method. This research contributed to improved data quality, especially to the information system (IS) and database field, where good data quality benefited the data analysis performance.
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Many information quality initiatives and projects need to demonstrate the potential benefits of their IQ-related activities already in their planning stage. In doing so, practitioners rely on cost estimates based on current non-quality data effects (that are then compared to data quality improvement costs). In producing such estimates on costs caused by low quality data, it is difficult to identify all potential negative monetary effects that are the result of low quality data (as well as all possible costs associated with assuring high quality data and their progression). Consequently, this article reviews and categorizes the potential costs associated with low quality data and examines their progression. This analysis can help practitioners to identify cost saving potentials and argue a more convincing business case of their data quality imitative. For researchers, the proposed classification framework and the cost progression analyses can be helpful to develop quantifiable measures of data quality costs and to prepare – subsequently – benchmarking studies, comparing different cost levels in different organizations. Thus, the paper contributes elements of a future cost-benefit analysis method for data quality investments.
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Advances in corporate householding are needed to address certain categories of data quality problems caused by data misinterpretation. In this paper, we first summarize some of these data quality problems and our more recent results from studying corporate householding applications and knowledge exploration. Then we outline a technical approach to a corporate householding knowledge processor (CHKP) to solve a particularly important type of corporate householding problem--entity aggregation. We illustrate the operation of the CHKP by using a motivational example in account consolidation. Our CHKP design and implementation uses and expands on the COntext INterchange (COIN) technology to manage and process corporate householding knowledge.
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Almost every activity in which the enterprise engages, from the most mundane operation to the most far-reaching decision, requires data. Yet data are rarely managed well. few enterprises know what data they have; people cannot access or use data; and data quality is often low. Furthermore, individuals and business units often hoard data, leading to political battles over ownership. To help enterprises manage data as a business resource, the authors survey the fundamental properties of data and explore the special challenges and opportunities involved in working with data. "Data" consist of "data models," which are the organization's definitions of entities, their attributes, and the relationships among them, and "data values, which are the specific realizations of an attribute of the data model for particular entities. "Data records" are the physical manifestations of data stored in paper flies, spreadsheets, and databases. Data have many distinctive qualities. for example, they are intangible; easy to copy, share, and transport, can be destroyed or lost inadvertently; are used for a variety of purposes; and are renewable. These and other properties of data have management implications in five key areas: making arrangements to supply the needs of data users; ensuring that individuals can access data; protecting data security; improving and maintaining data quality; and employing data effectively in operations and strategy. The authors offer a set of prescriptions to help enterprises meet these challenges: institute data quality and data supplier management programs, hone data needs, identify and manage critical information chains, recognize the proper role of technology, develop and disseminate an inventory of data resources, specify the terms under which data can be shared, avoid futile political battles, delineate management accountabilities, and ensure that senior executives lead the data management program.
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The growing costs of managing data demand a closer examination of associated cost-benefit tradeoffs. As a step towards developing an economic perspective of data management, specifically data quality management, this study describes a value-driven model of data products and the processes that produce them. The contribution to benefit (utility) is associated with the use of data products and costs attributed to the different data processing stages. Utility/cost tradeoffs are thus linked to design and administrative decisions at the different processing stages. By modeling and quantifying the economic impact of these decisions, this study shows how economically superior data quality management policies may be developed. To illustrate it, the study uses the model to develop a data quality management policy for online error correction. The results indicate that decisions that consider economic tradeoffs can be very different compared with decisions that are driven by technical and functional requirements only.
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Many previous studies of data quality have focused on the realization and evaluation of both data value quality and data service quality. These studies revealed that poor data value quality and poor data service quality were caused by poor data structure. In this study we focus on metadata management, namely, data structure quality and introduce the data quality management maturity model as a preferred maturity model. We empirically show that data quality improves as data management matures.