Comments on: Begin at the end … ensuring data quality success!

By: IAIDQ Festival del IDQ Bloggers - Episode #2 | The DOBlog

IAIDQ Festival del IDQ Bloggers - Episode #2 | The DOBlog — Mon, 01 Jun 2009 19:17:07 +0000

[...] “new kid” on the Information Quality blogging block, William Sharp. In his post “Begin at the End – Ensuring Data Quality Success” elegantly sums up one of the challenges in developing, presenting, and implementing [...]

By: Tim Wilson

Tim Wilson — Wed, 20 May 2009 21:50:26 +0000

I was in a similar discussion this afternoon! There’s a subtlety to your post that is important, and I think Dylan’s comment might miss it a little bit.

There are two extremes on the “customer data quality value proposition” front: 1) quantify direct business benefits that the project will provide and forecast (and later measure) the ROI of the initiative, and 2) list all the myriad benefits of improving customer data quality and hope to get enough buy-in to get the initiative funded. Neither of these work on their own.

In my experience, you need to lead with the second approach — tell the “story” of customer data quality and get heads nodding. Then, follow up (in the same discussion) with the lowest hanging fruit — what you can quantify that also has a significant payback. THEN, combine the two: “We can quantify *x*, which shows enough of a return to warrant undertaking the project, but there’s a lot of other benefits that are extremely difficult/fuzzy to measure. So, ‘x’ is the baseline, and the initiative stands on its own based on that. BUT, the initiative bubbles to the top of the list when you accept that there are a slew of additional benefits that aren’t worth trying to pin down specific measures on.

By: Jim Harris

Jim Harris — Wed, 20 May 2009 14:51:53 +0000

Excellent post!

I have seen too many data quality projects try to get by with a weak case such as “fix the bad data” or “reduce the number of erroneous records” without linking to a tangible business case or clear definition of ROI.

When a data quality project is performed this way, it will always been viewed as either a failure or at best a non-event that no one even notices no matter how well the data quality was actually improved.

Therefore, I completely agree that you have to present a solid business case that illustrates the negative impact of poor data quality on decision-critical enterprise information, such as incorrect business decisions, bad customer experiences and ultimately lost revenue.

By: Dylan Jones

Dylan Jones — Wed, 20 May 2009 06:28:19 +0000

Nice post William, in these climates I think it’s impractical to embark on any kind of DQ activity (other than compliance) without a clear ROI model in place.

The key as you say is to have a firm understanding of what you expect to achieve, this can be done using a sample or lightweight trial beforehand but you’re absolutely right, when the project kicks off, sponsors do expect to see results.

Can I also welcome your blog to the global DQ community, I’ll update our records and add you to the next Data Quality Pro Blog roundup.

-Best of luck with the site,
Dylan