Managing dirty data in organizations using ERP: lessons from a case study
| DOI | https://doi.org/10.1108/02635570110365970 |
| Published date | 01 February 2001 |
| Pages | 21-31 |
| Date | 01 February 2001 |
| Author | Jodi Vosburg,Anil Kumar |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
Managing dirty data in organizations using ERP:
lessons from a case study
Jodi Vosburg
The University of Wisconsin-Whitewater, Wisconsin, USA
Anil Kumar
The University of Wisconsin-Whitewater, Wisconsin, USA
1.0 Introduction
Daily operations, planning, and decision-
making functions in organizations are
increasingly dependent on transaction data.
This data is entered electronically and
manually and then organized, managed and
extracted for decision-making. The same data
entered and used to facilitate building,
shipping, and invoicing goods is also
extracted and manipulated to evaluate
factory and sales force performance in the
short term. In the long term this data is used
to chart the course of the business in terms of
manufacturing facilities, products, and
marketing. The integrity of the data used to
operate and make decisions about a business
affects the relative efficiency of operations
and quality of decisions made. Protecting
data integrity is a challenging task. Redman
(1995) comments that ``many managers are
unaware of the quality of data they use and
perhaps assume that IT ensures that data are
perfect. Although poor quality appears to be
the norm, rather than the exception, they
have largely ignored the issue of quality''.
Other scholars (Greengard, 1998; Kilbane,
1999; Tayi and Ballou, 1998; Wallace, 1999)
also point out the importance of data quality
for organizations.
Maintaining the quality of the data that is
used in an organization is becoming an
increasingly high priority for businesses. In
a recent survey of 300 IT executives
conducted by Information Week (Wallace,
1999), majority of the respondents (81 per
cent) said, ``improving customer data quality
was the most important post-year 2000
technology priority''. The respondents
further stated that there would be
``significantly increased spending'' on data
quality in their organizations. Companies
that manage their data effectively are able to
achieve a competitive advantage in the
marketplace (Sellar, 1999). On the other hand,
``bad data can put a company at a competitive
disadvantage'' comments Greengard (1998). A
recent study (Ferriss, 1998) found out that
``Canadian automotive insurers are taking a
major hit from organized and computer-
literate criminals who are staging crashes
and taking advantage of dirty data in
corporate databases''. The study found out
that in one case several insurance firms lost
$56 million to one fraud ring.
How does a company end up with dirty
data and what can be done to prevent this?
Disparate data stores (individual,
departmental, and organizational) that have
been developed and used by organizational
users over the years lead to dirty data
problems. For example, dissimilar data
structures for the same customer data
(spelling discrepancies, multiple account
numbers, address variations), incomplete or
missing data, lack of legacy data standards,
actual data values being different from meta-
labels, use of free-form fields, etc. (Kay, 1997;
Knowles, 1997; Weston, 1998). These problems
can be compounded by the volume of data
that is stored and used in organizations. One
way of overcoming this problem is to use
technologies that integrate the disparate data
stores for an organization and help
companies clean up their data. Enterprise
resource planning (ERP) systems (SAP,
Peoplesoft, Baan, J.D. Edwards, etc.) are
examples of such systems. ``A good ERP
system offers an integrated option,
implementing browser and client-server
modes while maintaining consistent data and
function within the enterprise and out to the
supply chain'' (Stankovic, 1998). In recent
years, ERP vendors have gone beyond
providing the traditional integrated
applications, such as manufacturing,
financials, and human resources. Newer
applications that have emerged include
supply chain management, customer-
relationship management, data mining and
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[21]
Industrial Management &
Data Systems
101/1 [2001] 21±31
#MCB University Press
[ISSN 0263-5577]
Keywords
Data, Data integrity,
Enterprise resource planning,
Systems management
Abstract
The integrity of the data used to
operate and make decisions about
a business affects the relative
efficiency of operations and
quality of decisions made.
Protecting that integrity can be
difficult and becomes more
difficult as the size and complexity
of the business and its systems
increase. Recovering data
integrity may be impossible once it
is compromised. Stewards of
transactional and planning
systems must therefore employ a
combination of procedures
including systematic safeguards
and user-training programs to
counteract and prevent dirty data
in those systems. Users of
transactional and planning
systems must understand the
origins and effects of dirty data
and the importance of and means
of guarding against it. This
requires a shared understanding
within the context of the business
of the meaning, uses, and value of
data across functional entities. In
this paper, we discuss issues
related to the origin of dirty data,
associated problems and costs of
using dirty data in an organization,
the process of dealing with dirty
data in a migration to a new
system: enterprise resource
planning (ERP), and the benefits of
an ERP in managing dirty data.
These issues are explored in the
paper using a case study.
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