Data
Quality
According to
Meta Group enterprises now share 28% of their data warehouse data
with partners, suppliers, and customers and forecasts more than 50%
growth in this usage by 2002/03. For e-business collaboration,
companies are also giving affiliates and customer’s access to ERP
and other back-end systems via the Internet. Opening systems offers
opportunities for faster product development and fulfillment and
better relationships. You are right there are risks in exposing
internal data—but, fortunately there are solutions for avoiding them
and leveraging these opportunities. One should understand the
importance of Data Quality.
PricewaterhouseCoopers’ “Global Data
Management Survey 2001” found 75% of companies studied reported
problems from poor Data Quality. Even if companies cleanse warehouse
and internal systems data during data migration, Data Quality is
vulnerable to degradation. New data enters daily from new and
expanded internal systems and the Web (where input is
uncontrollable), resulting in data issues.
For example,
inconsistencies and typos in names, addresses, and product data
occur, generating duplicates—such as Mark Atkins versus Mark Akins.
Information is entered into the wrong fields, becoming buried.
Errors, such as transpositions in product numbers, also increase.
Low Data Quality has a negative impact on the potential success of
new and existing business systems investments e.g. CRM, BI,
eBusiness and ERP applications.
This negative
impact can be quantified in terms of costs and lost revenue
opportunities. Statements from industry analysts regarding Data
Quality include the following:
·
"No. 1 reason for CRM project failure is the
data which is ignored". Recommended action is "to have a Data
Quality strategy", Gartner, Nov. 2001
·
"75% of respondents reported significant
problems as a result of defective data and poor Data Quality", PWC
Global Data Management Survey 2001.
·
Poor Data Quality is a serious threat to any
organization's competitiveness. Poor Data Quality affects the
confidence and professionalism of a business.
·
Data Quality is defined using the following
measures:
o
Completeness - What data is missing or
unusable?
o
Conformity - What data is stored in a
non-standard format?
o
Consistency - What data values give
conflicting information?
o
Accuracy - What data is incorrect or out of
date?
o
Duplication - What data records are
duplicated?
o
Integrity - What data is missing important
relationship linkages?
A one-time fix
can’t solve these problems. Companies need an enterprise data
quality solution. This involves:
- Implementing
automated batch and real-time data quality processes enterprise
wide—wherever data is deployed for new purposes, external data
integrates with internal systems, and customers and affiliates
search internal databases.
- Regularly
auditing data in critical systems.
- Proactively
modifying systems to parallel and support the rollout of new
strategies. To do this job requires top-level sponsorship and an
understanding of how information flows support the business as
well as robust data re-engineering tools. Look for software that
handles any data and provides mathematically-based matching
technology, to ensure accuracy and completeness in finding related
records.
The ISO Quality Management Toolkit:
the definitive resource for Quality
Management Projects Click
Here
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