Ed Lindsey

Ed Lindsey

Building A Business Case For Data Quality: Overview

Building A Business Case For Data Quality, 2 of a 7-part series

When building a business case for a data quality initiative, you can either build a case from the top down or from the bottom up. From the top down, you’ll be working with corporate executives who have already identified a problem that is most likely caused by a data issue. Some examples are:

  • Fines for violating the do-not-call list
  • A large number of mailings failing to reach intended recipients
  • Failure to receive volume discounts from vendors because there are multiple item numbers for the same item
  • An increase in the average talk time in the call center because agents are spending too much time correcting the data in the system

Building a business case from the top down is the most efficient way of doing so, but unfortunately, it is rare that metrics such as the ones above are clearly defined. Instead, you often need to build the business case from the bottom up. This approach will be the focus of this series of Blogs.  The bottom-up approach starts with looking at the data first. Here you need to profile the data with an automated profiling tool, such as Informatica Data Explorer, or you can ask Informatica to assist you with a Data Quality Assessment or Business Value Assessment (which is actually a hybrid of the two approaches) service to get you started. Profiling your data can reveal its content, quality, and structure with minimal effort. (more…)

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Introduction: Building A Business Case For Data Quality

Building a business case for data quality is a waste of time. Nobody really cares. Improving data quality for quality’s sake is a waste of money. Sounds funny coming from a data quality specialist, someone who has spent the last decade preaching data profiling and data quality. But the fact is people from the business side do not care about data quality. What they care about is the impact poor data quality has on their line of business.

When you look at how the business measures itself (after you get past revenue and profit), the talk is about key performance indicators (KPI). What are some of the KPIs for a call center? You will hear about goals of reducing talk time. The business wants to lower costs. You will hear about goals of decreasing hold times. The business wants to improve the customer experience. (more…)

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Business Value Assessment Versus Data Quality Assessment

Here at Informatica, I provide a number of services for our customers and prospects.  Two of them are the Business Value Assessment and the Data Quality Assessment.  Customers are frequently confused about what the two services provide.  So, let me explain each at a high level.

The DQ Assessment starts with the data.  It is targeted toward the IT organization and in mainly looking at characteristics of the data.  The characteristics Informatica considers important are:

Completeness

  • Is all the requisite information available?
  • Are data values missing or in an unusable state? (more…)
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Data Quality – An Expanding Ring

I was talking to a customer the other day who is about to embark on a data quality quest.  He asked me to explain my view of a data quality initiative.  I explained to him that data quality is a never ending process.  I said it was like dropping a pebble in the water.  The initial ring is small but slowly expands outward.  The data quality process is similar.  You start small, with one application or one subject area, show some success then look to expand the process with additional applications or subject areas.  Then expand further to additional business units or business functions until you have encompassed the entire enterprise.  Then just when you think you’re done, you need to continue to monitor and repair data because it will degrade over time.

While what I said was true, he said that the business would reject that description out of hand.  Business believes that all IT projects are never ending projects.  It is this thought process that always pitches Business against IT.  Business believes that IT never delivers a finished project.  His analogy is that Data Quality is more like building a building.  It takes a lot of time and effort to get the foundation right, then you need to add the structure, electrical, water, communication, and finishing work.  Then you move in and begin to use the building but you’re not done. (more…)

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