The Memorial Day weekend gave me a little time to get caught up on some reading. Some of what I was reading were analytics reports (I know – get a life) and while I very much appreciate the cycle of continual improvement Google fosters within their analytics product, that cycle yields a continuously growing amount of data. The problem that I have isn’t so much understanding what I’m reading but trying to figure out why any of it matters to my clients. I also spend time figuring out which of the numbers are lying to me.
It’s no secret that there are an awful lot of bad actors in the digital world. Once it becomes clear how fraud is detected those bad actors move on to another form. If viewability is important, they create sites where there is 100% viewability but no content of any value. I had a client get all excited about an increase in referral traffic until I pointed out that most of that traffic was coming as a result of referrer spam. When we filtered it out, traffic was flat. Another prospect got excited by the large “stickiness” – time on site and pages viewed – that her site has. They were impressive until you filtered out the IP addresses of her employees, who spent hours a day on the site.
Silly things, I know, but it points to a common problem. An IDG study of a couple of years ago pointed out that nearly half of marketers said they struggle to make sense of the vast amount of data they get. The other half thinks they know what the numbers mean, yet many of their plans are built to achieve unrealistic metrics. The problem is compounded by what the paper identifies as the accuracy problem I mentioned above:
Why is data accuracy still such a big issue? One possible reason is a lack of investment in a defined data management process that includes ongoing, consistent data migration, data maintenance, quality control and governance. Too often data is held and managed in multiple organizational silos. This results in inconsistency, duplication, gaps and errors.
So while “garbage in, garbage out” isn’t a particular revelation, it does serve as an excellent reminder to take out the trash as best you can while compiling all of that data. You with me?
You’ve probably heard the old joke about the kid and the pile of horse manure. There are many variants, but the basic story is that a kid is digging through a huge pile of horse manure. When he is asked why his response is “with this much manure, there has to be a pony in here somewhere.” It’s a story a use to help clients understand the nature of data. Any of us who are in business see more and more of it each day. In fact, we’re probably setting up systems to provide more of it to us as well. The unfortunate truth is that most of it is…well…manure.
(Photo credit: Wikipedia)
We’re after the pony, or at least we should be. The pony is the actionable insights that are contained within the data and not the accumulation of data itself, It does take a lot of digging, and that digging can begin only after we set up systems to gather and to organize the flood of data. Knowing that website traffic grew as measured by session count tells you very little. Understanding how it grew or if that growth was because a bunch of referrer spammers hit it gives you actionable information (update the spam filters!). Knowing that your store sales were up 5% without understanding that you’ve lost market share can cause you to think that you’re doing well when in fact you’re losing ground.
Say “so what” to yourself a lot. If you can’t explain why a piece of data is meaningful, you need to discard it because it’s the manure surrounding the pony inside. If you can’t put something into a broader context, push to do so. If you can’t determine a course of action based on a particular nugget of information, ignore it and keep digging until you get to the pony. Make sense?
I don’t think there has been a baseball movie made that didn’t feature some weathered old guy seated in the bleachers somewhere. He usually utters undecipherable baseball jargon while taking copious notes. This, dear reader, is the baseball scout, who used to be how talent was discovered. If you’ve seen or read Moneyball, you know that the scout is an endangered species. This article from USA Today last week talks about how many pro scouts are still unemployed one month before the start of spring training. The reason? Data.
(Photo credit: Wikipedia)
Baseball is in the throes of the Moneyball movement. Teams have been laying off scouts and turning to sabermetrics, which Wikipedia defines as the empirical analysis of baseball, especially baseball statistics that measure in-game activity. Baseball has fallen in love with data. Maybe your business has too.
Here is the problem, both for you and for baseball. There are certain things that don’t show up in data. A player’s leadership qualities in the dugout aren’t quantifiable. Potential can often be visible but not measurable. That’s true in your office as well. The data may show you what it happening but it’s hard for it to show you what could be happening. That requires humans: scouts.
We all need scouts. We need people who use the data as a tool but who also have the experience and wisdom to know when the data is missing something. That doesn’t mean projecting one’s wishes into the numbers nor distorting the story those numbers tell. It is, however, an acknowledgment that there is often a bigger picture than what’s inside the frame.
Here is a quote from a scout:
I’ve got 23 years in the business,’’ Wren said, “and now clubs don’t want that experience? I look at teams now, and they’re hiring guys who aren’t really scouts. They’re sabermetric guys from the office, and they put them in the field like they’re scouts, just to give them a consensus of opinion.
That’s dangerous for a baseball team. It could be fatal for you. You’re up!