I often create guides, white papers and presentations on various data quality topics. With modern tracking tools you can instantly see which ones are the most popular and this gives a great insight into the topics of data quality that attract a particular audience.
In one magazine, an introductory guide I created on data quality was released and did reasonably well in terms of downloads. Later in the year a further guide was released but with a slightly different topic. The new guide was aimed squarely at project leaders and this was reflected in the content title.
Conventional wisdom might dictate that the guide with a more general data quality topic will appeal to a broader audience and therefore attract more downloads. Not so. The guide aimed at a much smaller, tightly defined niche attracted considerably more downloads, not just compared to the earlier guide but against all other content posted on the site.
The reason is due to human nature, and it forms one of the most important lessons for accelerating the buy-in of data quality management within your organisation.
Data quality management is based on proven principles. We know that it makes sense to apply data quality management techniques because there is now a huge body of knowledge that has demonstrated the return-on-investment opportunities. Aside from the financial benefits we know that regulators now integrate data quality principles in compliance directives and in sectors like healthcare and emergency services better data saves lives.
Yet I still hear tales of companies that choose to ignore this advice. I’ve heard of entire data quality teams shut down overnight because “the new management didn’t get it.”
When you’re making the case for data quality it’s easy to focus on the benefits to your business or organisation. Increased customer satisfaction, reduced lead times, healthier bottom line. We pepper our business cases with value-added statements but often we miss the real reason people buy (or so often don’t buy) into data quality – for selfish reasons.
I’ve presented data quality business cases that are iron-clad, huge examples of waste that have been painstakingly documented and can be easily remedied. Yet no action has taken place.
In other situations I’ve only been to speculate as to the possible extent of data quality issues, yet executive backing has been granted immediately.
The difference in these situations was purely down to aligning the “data quality story” to the needs of the individual.
You have to understand the motivations, fears and aspirations of someone before you can pitch your grand plan for data quality. How will the initiative impact them? What will they stand to lose or gain? How do they perceive the current state? How will they benefit from a future state?
The more specific you make the references the more likely you are to succeed with creating buy-in. I can’t stress this enough: do your groundwork, create stories that resonate and remember that some battles cannot be won.