EXPERT SPEAK
Saul Judah is Research
Director at Gartner
Business case for data
quality improvement
An initiative to improve quality of data management is
likely to progress by making a business case explains
Gartner’s Saul Judah.
P
oor data quality destroys business
value. Recent Gartner research
shows that organisations estimate
the average negative financial impact of
poor data quality to be $9.7M per year.
In other words, this is their estimate and
the impact they believe it has on them
per year.
This is likely to worsen as information
environments become increasingly
complex. Data quality issues are faced by
organisations of all sizes and complexity.
Those with multiple business units and
operations in several geographic regions
with many customers, employees,
suppliers and products will inevitably face
more severe data quality issues.
Poor data quality contributes to a
crisis in information trust and business
value, such as financial and operational
performance. Unless tangible and
pragmatic steps are taken to understand,
address and control data quality, the
situation will worsen.
Many organisations struggle to
successfully propose a programme for
sustainable data quality improvement.
Effective business engagement and
funding may be limited for several
reasons:
• Value to business may be unclear
• No connection might be evident
between data quality improvement and
business outcomes
• Business may not understand
importance of their role in data quality
improvement
A business case for data quality
improvement must start and end with
the business outcome. The business case
should demonstrate how any improvement
in the underlying data creates better
business outcomes.
Gartner has identified five steps to
creating a business case for data quality
improvement:
1
Understand top business priorities
and find right place to start
If a business case is to be taken seriously,
it must be presented in the language of
the business and speak to the critical
and specific business priorities of key
stakeholders. Understanding the business
vision will not only enable you to identify
senior-level support for your business case,
but also help to identify and engage the
right level of senior business sponsorship.
2
Select business performance
metrics to support the right
business outcomes
Ironically, one of the main reasons for
unsuccessful business cases for data
quality improvement is because they focus
on data quality. Successful business cases
address the key components necessary
to achieve the business vision, such
as financial performance, operational
performance, legal and regulatory
compliance and customer experience.
3
Profile the current state of
data quality and its business
implications
Once the scope of the business case has
been agreed on, initial data profiling can
begin. Data profiling should be carried out
early and often. Establishing a benchmark
at the initial level of data quality, prior
to its improvement, will make it easier to
objectively demonstrate the causal impact
on business value after improvement and
justify later requests for further funding.
4
Describe the target state to
achieve business improvements
Business leaders sometimes struggle to
understand that data quality improvement
is not a once-and-done activity. It is very
important to make it clear that unless a
sustainable environment for data quality
improvement is established, it will rapidly
revert to its original poor state. The target
state for data quality must be described
in terms of how it can positively and
sustainably improve critical business
metrics such as financial results.
5
Estimate financials for the
business case
A go or no-go decision for business
case proposals often comes down to the
financials, and this is no different for data
quality improvement. A good business
case must identify the anticipated
benefits of the initiative and must be
tangible, quantifiable and desirable to the
stakeholders.
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