A Series of Unfortunate Events – a Data Quality Tale of Woe


A data quality tale of woe

A Series of Unfortunate Events, by Lemony Snicket, is, like most data quality tales, an unfortaulogy. An unfortaulogy is a series of unfortunate tales, in this case, unfortunately, thirteen, in which our data quality heroes, the Baudelaire orphans; Violet, Klaus and Sunny;  struggle to survive the murderous plots of the data quality problem – their evil Uncle Olaf.

Like most data quality heroes, the Baudelaire children struggle to communicate that there is a data quality problem. Uncle Olaf is a master of disguise!

He masquerades as a application problem, but changing one application for another often only makes the data quality problem worse.

He hides as a training problem, but more training for the users doesn’t, by itself, sort out the data quality problem.

He can be found as a process problem, as a governance problem, or in a myriad of other disguises.

Our data quality orphans are sent to stay with a number of well meaning, but poorly discerning adult relatives. Like many adults, these relatives struggle to see what is before their face.

The data quality problem, in his disguise, appears to be harmless. Business is, unfortunately,  functioning, kind of, and, while the orphans can see the problem they cannot communicate it to the decision makers.

The results are unfortunate and predictable – in line with the author’s wishes I don’t recommend that you read the books to find out what happened. It would be most unfortunate.

For data quality professionals the lessons are clear. We need to communicate in specifics and we need to be relevant.

As importantly, when the disguise causes the business to latch into a perceived slution that cannot work we need to be better at getting this across. I recently, for example, was asked to help a client explain why the new CRM application could not solve the client data quality problem.

Obviously, like most applications, the new package had no real data quality capability – there was no reason to believe that this would solve the data quality problems inherent in all the previous packages. Like the Baudelaire orphans. however, my client could not get the business executives to see this – they could not see through the disguise.

What did work was simply looking beyond this application to the broader environment. Some simple investigation uncovered the limited scope intended for the CRM application, in fact there was even another CRM application being used to manage some front office activities. By identifying where else customer data was being captured, modified and used we managed to unmask the impostor and show that the proposed solution could not address the problem.

in many cases, we are sold on new applications (CRM, MDM, ERP, etc.) or on new initiatives (data governance, BPM, etc.) when in fact, the problem that we are trying to solve is data quality. Surely, if you have a data quality problem it makes sense to look at a data quality solution?

It can often be easier to see through the disguise when we can shift from a tactical single environment or project view to creating an understanding of the broader implications. This allows us to solve our immediate problem while building a framework to solve the bigger, enterprise problems.

– originally published at: http://www.masterdata.co.za/index.php/news/87-mdm-insights/279-a-series-of-unfortunate-events

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