Self-Service Digital Analytics: Myth or Fiction?

Is Digital Analytics more like a Deli counter or more like Whole Foods?

The paradigm is an elegant one: by properly setting up profiles and dashboards for different constituents in the organization, digital analytics data is readily available in a consumable form to anyone who gets credentials to do so.

It’s streamlined, efficient and always current on data.

It should be the undisputed standard of web analytics delivery.

So howcome it’s about as common as a sunny day in Portlandia?

The answer is: people either don’t really want it, or don’t have the time and money to set it up.

My suspicion is the real driver here is that people don’t really want it. They don’t want to be told that their analytics is all right there waiting for them. Largely this is because, without sufficient training in analytics data, methods, and statistics, most marketers and executives will look at their dashboards and say “meh” because they are not by trade equipped with the knowledge base to drill down and find the important nuggets of information.

That’s why the prevalent mode of analytics delivery is the one-report-at-a-time, when-I-need-to-see-it-only paradigm. There really is no problem with this, except that it is, on the face of it, inefficient.

But is it really less efficient than an efficiency engine?

People continue to drive decisions. And when they need to make decisions, they need information. And when they need information, they want to go to an expert and have that expert prepare information so they can look at it and rapidly see the relevant patterns. It requires specific analytics technical and business experts to be involved; as well as data analysts.

The self-service model does not necessarily provide this. It assumes the tool is able to create a display layer good enough to answer nearly every question a user might need answered. And while that’s a great idea, the tool has not yet been created that actually does it. In fact, far from it. And think of all the money that got spent putting together the amazing self-service engine, only to see that money wasted because folks don’t particularly want to use it.

What they really want to do is call “the analtyics person” and say “I need this report”. And then they really want to get that report with commentary and context.

That report would not be possible without a well-engineered and business-aligned analytics platform. But it can certainly be accomplished without anything like a self-service environment.

Self-service is, it seems, too risky in a couple of ways: first, you might not get the environment right. Second, you can’t foresee emerging requirements and will have to supply ad hoc reporting anyway.

I think we have established what is wrong here: vendors would love for the tool to do all the work. But in the real world, the tool is just that: it needs to be in the hands of a skilled expert. Much the way cars don’t yet drive down the street and go pick up groceries on their own, neither does analytics run on auto-pilot. All you need is a few random requests that require your experts to scramble for information, and the “savings” you wanted to realize by building everything into the environment starts to unravel.

My guess is that the current system of tool/business/technical/analyst/consumer will persist. This is because analytics consumers want it that way (they want to call a person and get what they want); and because no automated system has yet become expert enough to anticipate the unknown.