In the last post of this blog series on being success­ful with digital analyt­ics, I showed how to update your SDR as you imple­ment new items. That is one way to boost your overall imple­men­ta­tion scores with respect to the percent­age of busi­ness require­ments that can be addressed. The other way you can boost your scores is to ensure that the data points you are collect­ing are accu­rate. Even if you have the data points you need to address a busi­ness require­ment, if the data is incor­rect or untrust­wor­thy, you still cannot fully answer the busi­ness ques­tion. There­fore, in this post, I will share some thoughts on how to focus on data quality.

Data quality in digital analyt­ics is the elephant in the room that no one likes to talk about. But in my career, I have audited hundreds of digital analyt­ics imple­men­ta­tions and have yet to find one where the data was always trust­wor­thy. The most common issues I encounter are:

  • Metrics that have incon­sis­tent numbers, wild swings in numbers (i.e. YoY) or suddenly stop collect­ing data
  • Dimen­sions that are missing values for metrics that are logical coun­ter­parts (i.e. No Product ID with Add to Wish­list metric)
  • Dimen­sions that are captur­ing unex­pected values or values that are not adher­ing to a prede­fined naming conven­tion
  • Imple­men­ta­tion mistakes that nega­tively impact data (i.e. Adobe customers not using Merchan­dis­ing eVar when it should be used or using an sProp when an eVar should be used)

So why do so many orga­ni­za­tions have data quality issues? I have seen several reasons. First, so much time is spent at orga­ni­za­tions imple­ment­ing digital analyt­ics tools, that teams are usually exhausted and want to put the imple­men­ta­tion behind them. The prospect of contin­u­ously check­ing and re-check­ing data is not an appeal­ing one. It is like asking someone to run a 5k race each week right after they ran a marathon! Second, orga­ni­za­tions love to do website or app redesigns and these major changes can wreak havoc on digital analyt­ics data if your imple­men­ta­tion hasn’t been set up in a robust manner (i.e. data layer approach vs. DOM scrap­ing). Third, data quality is not gener­ally a focus of exec­u­tives. Exec­u­tives tend to focus on the big things like getting the newly redesigned website live by June 1st more than they do about the details of data quality. I will share some strate­gies on how to change this in the next post.

So why is digital analyt­ics data quality so impor­tant? Many years ago I called data quality the silent killer of digital analyt­ics. This is because poor data quality slowly sucks the life out of digital analyt­ics imple­men­ta­tions. If your inter­nal users log into your analyt­ics tool and encounter data quality issues, it nega­tively impacts the overall percep­tion of your team and your analyt­ics program. When your busi­ness stake­hold­ers utilize your data, you are, in effect, asking them to make busi­ness deci­sions that will impact their repu­ta­tion (and possi­bly career) based upon your data. That is a big ask. If they start to sense that the data is not trust­wor­thy, they may stop using your data and either revert to their “gut” or look for data else­where. If that trend becomes perva­sive within the orga­ni­za­tion, your invest­ments in digital analyt­ics will all be for nothing. It will impact your budgets, your team head­count, but most impor­tantly your team repu­ta­tion and morale. As they say, a repu­ta­tion takes a long time to build but only a few moments to lose.

I wish there were an easy way for me to tell you how to address data quality issues. Unfor­tu­nately, dealing with data quality simply requires time and resources. You can set up alerts within your analyt­ics tool or stan­dard devi­a­tion formu­las in spread­sheets to proac­tively see if metrics might be expe­ri­enc­ing issues. Vali­dat­ing dimen­sions and imple­men­ta­tion issues is much harder. You can spot check dimen­sion value/metric combi­na­tions or invest in 3rd party tools like Observe­point or Alar­m­duck. My goal of this post is not to show you exactly how to address data quality issues since every orga­ni­za­tion has differ­ent needs, but rather, to make you aware this data quality needs to be a key part of your analyt­ics program.

Action Items

Your task for this post is to do a data quality audit on your imple­men­ta­tion:

  • Check all of your metrics and see if there are any that look suspi­cious. I suggest that you check a few data points each week instead of attempt­ing to check all of them every few months. The latter has a tendency to get pushed off in my expe­ri­ence.
  • Open your dimen­sion reports with metrics that have logical pair­ings and see if the data looks ok and if the values are under­stand­able.
  • Consider making auto­mated spread­sheets that can down­load the data you reviewed in the previ­ous steps on a recur­ring basis or if that is too much work for you, check out some 3rd party tools that can help.

In the next post, I will discuss some strate­gies to get your exec­u­tives to care more about data quality.

We’re here to help you through this.

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