Getting Executives to Care About Data Quality

by Feb 26, 2020

In the last post of this blog series on being success­ful with digital analyt­ics, I shared my thoughts on why I see so many data quality issues and why data quality is so impor­tant to digital analyt­ics programs. Much of it has to do with the percep­tion of your data and of your analyt­ics team. But address­ing data quality takes time and money. Unfor­tu­nately, that time and money often comes from exec­u­tives and I have seen many cases in which exec­u­tives who are willing to spend money on websites, apps and analyt­ics tools are not as open to devot­ing money and resources to data quality. In this post I will share my thoughts on why this is the case and some sugges­tions on how to win over these exec­u­tives.

Many exec­u­tives, rightly so, focus on major projects and mile­stones. For example, launch­ing a new website with increased func­tion­al­ity is a highly visible effort within an orga­ni­za­tion. Delays or cost over­runs in major efforts like this make exec­u­tives look bad, so they normally do what­ever it takes to cross the finish line on time and on budget. But once those major mile­stones are met, the follow-on work like data quality isn’t as sexy as the project itself. There are no key dates to hit and no tangi­ble thing like a new website that you can view. This is one of the reasons why data quality doesn’t get the atten­tion it deserves.

The other key reason I believe that data quality doesn’t get exec­u­tive focus is that they don’t directly feel the pain of data quality issues. In many (not all) cases exec­u­tives are not logging into digital analyt­ics tools them­selves. Unlike you or your team who can see that a metric is broken, exec­u­tives only learn about data quality issues if those report­ing to them complain about it at staff meet­ings. While you may be frus­trated that an Adobe Analyt­ics eVar has been broken for weeks, it is likely that your exec­u­tives don’t even know what an eVar is!

So how can you get your exec­u­tives to feel your pain and care more about data quality. While there is no silver bullet for this, I do have a few approaches that have helped me over the years. The first is to lever­age the busi­ness require­ments work that we have been discussing through­out this blog series. Back in the fourth post of the series, we iden­ti­fied your website objec­tives. These were the high-level reasons why your website or app exists. Then in the sixth post of the series we iden­ti­fied your busi­ness require­ments and tied each of these busi­ness require­ments to high-level busi­ness objec­tives. Then in the eighth post of the series, you iden­ti­fied the data points that were needed for each busi­ness require­ment. Without real­iz­ing it, what you have done is applied the tran­si­tive theory to map high-level busi­ness objec­tives to detailed data points in your imple­men­ta­tion. Busi­ness require­ments serve as the conduit between objec­tives and data points.

So why does that matter? Your exec­u­tives care more about your high-level website objec­tives than they do data points. But if you can show your exec­u­tives that there are data quality issues that are nega­tively impact­ing your team’s ability to answer ques­tions about their high-level objec­tives, you can include them in your pain! For example, imagine that there is a major data quality issue around tagging for real-time chat and you are having a tough time getting your devel­op­ment team to focus on fixing it. Using your require­ment-driven SDR, you can show your exec­u­tive that you are essen­tially flying blind on the “Improve Customer Sat Score” objec­tive that she cares about:

In many cases, I have seen this get the atten­tion of exec­u­tives so they can help you to get your data back on track.

Action Items

Your home­work for this post is to:

  • Make sure your SDR has all of your busi­ness objec­tives mapped to data points and iden­tify which high-level data points might be at risk due to data quality checks you performed in the previ­ous post.
  • If you have time, you can hear me talk about some of the ideas presented above in this video.

In the next post, I will switch gears and talk about the analy­sis part of digital analyt­ics.

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