“Beauty is only skin deep”. This old saying came to mind recently when speaking with a friend. Jane was relaying to me the amount of time she spends correcting data for management reports every month. Answering simple questions like “how many new customers did we add?”, “how many customers placed repeat orders?” or “what was the top selling product?” Without the correct answers, management ran the risk of making poor decisions on future investments in marketing campaign, capacity planning and sales and support resources.

On average Jane spent two days a month checking for duplicate customer names and standardising product codes and descriptions, just so the reports would give an accurate reflection of sales. All this was managed in multiple Excel worksheets. The reporting tool the company had invested in was still being supplemented with manual worksheets, as management did not trust the information from the tool of choice. As the months and quarters went by Jane spent more and more time managing the worksheets.

I asked her if she had shared the pain she was going through with other managers outside of the sales organisation, or did they have a data steward. Alas, the answer was “no”.

This is a common problem I come across every week when talking to customers. While senior management see the final results of well structured and standardised information, they do not realise the effort and cost associated with manually preparing/correcting reports. More worrisome is the impact the bad data are having on other parts of the business is being disguised and goes uncorrected as the data are being corrected for management reports.

What should Jane do? I explained that she needed to measure, in terms of cost, the impact poor data quality is having on the business apart from the two days a month she herself was spending making corrections. This could be as simple as looking at the number of duplicate mailers being posted every month. In one example 1,500 duplicate customers x $10 for brochure costs x 12 times per year equates to $180,000 wasted. Next I recommended she share her pain with other business units, like finance and operations. She should check to see had they done a data quality check-up? Are they having similar problems? Are there delays in getting payment because invoices go to the wrong address?

With an online data quality health check up Jane and her colleagues can quickly profile their data and identify errors and apply simple metrics to see the impact of bad data. Armed with hard dollar costs she will have taken the first step to getting senior management attention and funding for a data quality initiative.

From what I’ve seen, what has worked for other companies is to create data quality scorecards for the individual business units. This creates healthy competition and a sense of urgency to address the data quality problems. It also demonstrates to senior management that the information they use to make decisions on is akin to being only ‘skin deep’ and the supporting data underneath can be very ugly and costly.