I’ve been writing about data quality in marketing for quite a while now, and I think the moment is right for firms — in particular, CMOs — to turn their attention fully to the issue of data quality and governance. Why pick on CMOs in particular? Let’s look at some trends. According to Gartner, the spending that large firms make on marketing, as a percentage of total budget, hit an all-time high in 2016 – 2017, accounting for 12% of total corporate budgets. And where is that money going? Toward digital advertising and websites, of course, but also to marketing technology. In fact, Gartner anticipates that at some point in 2017, the total amount spent by CMOs on IT surpassed the amount spent by CIOs, for the first time.
And yet…what do firms have to show for all this spending? In all likelihood they have a lot of bad data. The authors of a September 2017 report from Harvard Business Review conclude that on average just 3% of company data meets a minimum threshold for data quality. Of course, the problem extends far beyond the borders of marketing, but we’ve discussed before that in terms of data quality, marketing data lags behind other web analytics data efforts, so it’s an especially important focal point. And have I mentioned that IBM estimates that bad data costs US firms $3 Trillion a year? Given the growing budgets for digital marketing and marketing technology, 2018 could be the year everyone focuses on staunching the bleeding.
If you still need convincing, here are three reasons why 2018 should be the year.
- Money. And I’m not just talking about getting value from the money you’ll continue to spend on marketing and marketing technology, I’m talking about getting full value from the money you’ve already spent on your marketing tech stack the past few years. There are some amazing tools out there for measuring marketing attribution and optimizing messaging — and a lot of people are waking up to the fact that those tools are only as good as the data going into them. It’s a basic equation: garbage in = garbage out. A ton of money has been spent trying to fix up garbage coming out, with far too little attention paid to garbage going in.
- Human Capital. You are wasting the talents of your analytics teams. Oh, and you may be crushing their souls, too. People who are hired for their brain power and their skill with data shouldn’t be continually relegated to the role of data janitor. It’s expensive, it’s bad for morale, and it causes a negative data feedback loop every time an analyst leaves your team. Absent a good process workflow, institutional knowledge winds up residing within an analyst’s head. When they leave, any progress your company has made in addressing these issues goes with them, and data quality problems start over again.
- Insights. I assure you, if you don’t make it a priority to fix your organization’s data quality problems, someone else will fix theirs first. If your company is making its bets based on bad data, and your competitor is making their bets on better data… I’m betting they’ll win.
Part of the reason I’m so confident about that is that data governance and data quality are ultimately about setting up the correct processes and systems. As of year end 2017, there is nothing wrong with the algorithms. The tools we have can measure whatever we ask them to; it just happens that we’re asking them to measure garbage. Conversely, you can have the smartest marketers and analysts in the world, but if their system for data governance is broken, the left hand will never know what the right hand is doing. Your competitor is going to beat you not just because their data is better, but because they have the internal discipline and teamwork in place to put a coherent process together. That’s a winning formula even without a huge technology budget.