Data Quality Janitor

Data Quality Janitor?

Yes, that phrase came up in a recent interview session. Cal Braunstein of the Robert Frances Group, and I have been interviewing participants in our “Poor Data Quality – Negative Business Outcomes” survey, run with the IBM Information Governance Community. We prepared a spreadsheet of the interview responses gathered thus far, and I had a look to see whether there were instances of strong concurrence or disagreement among the interviewees. I won’t explore the responses here, except to say that there is general agreement about poor data quality difficulties encountered. Like the flu, there is a lot of bad data going around. Of course, I think that bad data is a social disease, and I’ve said so here, and here.

Ah ha, there is lots of poor data quality going around this season

One thing we always ask about is an interviewees’ “ah ha” moment for poor data quality. In our online survey, 51 respondents acknowledged having such a moment, and here in ranked order by percent responding, are the eye-opening data quality problems they had.

data quality janitor

The responses may be eye-opening, but I’ll save you the eye-straining. The top three “ah-ha” Poor data quality – Negative business outcomes moments had to do with inaccurate data with corrupt, null or plugged values, missing data with key elements or records not completed, and ambiguously defined data with several data sources having conflicting definitions for the same data, and no system and definition of record available.

Enlightening Internal Survey

 We have spoken to many survey respondents, but one interviewees’ discussion stood out. This colleague  mentioned that his firm performed an internal survey to gauge how various departments saw the organization’s customer data quality situation. Many of the functional areas surveyed believed that customer data quality was good. Our interviewee, on the front line of data quality remediation, knew there were problems.

The one functional area that also knew there were real customer data problems was Sales. This is no surprise. The hard-working sales people are on the front line of customer interaction. They hear the complaints about wrong billing addresses, misdirected invoices, shipping address problems and the like. Our interviewee mentioned that a catalog mailing was so rife with address errors that it took a battery of forklifts to move the stacks of catalogs that were “returned to sender” by the U.S. Postal Service. Guess what? There are fines and penalties associated with a mass return like that one.

Ah ha, Sales is the data quality janitor

That is what our interviewee told us one of his colleagues in Sales thinks. Does anyone believe this is the right kind of work for a sales team? What better work could there be for sales people than to become part-time data quality janitors? Don’t bother answering. The good news is that senior management has taken ownership of the customer data quality issue, and the familiar combination of people, process, and technology is being applied to improve the situation. We plan to track the progress of this case to see how customer data quality improves.

The Bottom Line

data quality strategy Like 82+% of our survey respondents, your customer data quality is probably not what it should be. The question is, what are you doing about it? The interviewee I sited earlier in this post also told us his firm invested heavily in data quality tools from a major player and experienced a 6 month payback. In earlier posts, I have written about the rapid payback and huge return on an investment in data quality technology and processes.

Have you had an “ah ha” moment about the effect of poor data quality on business outcomes? We want to know, so please reach out and tell us. Cal Braunstein and I are ready to help you improve your organization’s data quality situation. Sometimes, the first step is an internal survey like the one we did with the InfoGov community, but targeted specifically to your organization. Perhaps in your organization there is a belief that data quality is fine, when it is really not. We can help you identify the pain points, and prescribe the short and long-term solutions to get your data clean, and keep it clean. Contact us today, and get started on the road to great data quality.



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