Blog post General, Strategy, SDL

The Seven Deadly Sins of Enterprise Software: Data Hoarding (#2)

This is part two of a seven-part series: "The Seven Deadly Sins of Enterprise Software," which was initially presented at SDL Connect in November of 2019.

There are many sins of software, but few of them are deadly. Only a handful of mistakes in enterprise software will have long-term ramifications for how your business operates. Last time we talked about how Bleeding-Edgeism is dangerous for an enterprise, but adopting a process of rapid-prototyping is a safe way to assuage those desires to use cool new tech.  

Another of these "deadly sins" is Data Hoarding; let's talk about what it is and what we can do about it.

Is it Collecting or Hoarding? 

You're collecting when you bring things together, into one body and one place, to display that collection.  Just as museums collect art, carpenters may collect old woodworking tools, and bakers may collect recipes—all with the benefit of using or at least displaying that collection.    

Hoarding is an entirely different endeavor. While hoarding also involves that activity of gathering things and bringing them into one place, its end-goal is hiding and protecting what has been collected. Pirates hoard treasure, misers hoard money, and sometimes enterprises hoard data. 

Giving us a definition of Data Hoarding: 

Data Hoarding is collecting more data than you need or have a plan for using 

Data can be Addicting 

One of the first things a marketing organization does is implement web analytics. Once a Google Analytics snippet makes it on to web pages, data starts flowing in. And the data is delightful! You learn what pages are getting traffic, what social media channels are generating traffic, which search terms drive users to the website.  

But then you realize that there's data you never even thought to ask for. At first, you can learn what web browsers, devices, and platforms users have. Then, you start looking at the geography of your users. You can see age, gender, even the interests of your users. You think you have all these fascinating insights into users that you never knew you had.  

A Thirst for Insights can Lead to Drowning in Data 

I've seen quite a few organizations go from, "we just set up Google Analytics," to "we've now implemented three different analytics platforms, and we're looking at a fourth" in a single year. Inevitably, when I ask these organizations, "what are you learning from all of these tools," the answer is something along the lines of, "there's so much information that we're not really sure." It's because they're drowning in data.

And if the right client asks the wrong consultant what to do about the flood of data they have, they may get the answer, "you need a data lake." 

If you're in a data flood, you never need a data lake.  

When Data gets Dammed 

Clients typically react to their massive amount of data in one of two ways: 

  • Overwhelmed by the enormous load, they ignore it 
  • Convinced they may miss something, they add to it 

Either reaction results in an enormous hoard of data. The client continues amassing data about their users, content, and site performance that never seems to contribute to any meaningful changes.  Developers shake their heads as they think to themselves, "all that data, so little change." 

And so, the data hoarding has begun.  

Course Correction 

What I've found is that there's a key principle to remember in data gathering which turns that hoard of data into a collection:

Fundamentally, we don't want data; we want insights. Insights are the answers we get when we ask our data questions. 

Our data can’t answer questions that we don’t ask it.  

Start with: 

  1. What questions do we need the data to answer? 
  2. What tools can answer these questions? 

You don’t buy a hammer before you’ve decided to use nails. So why would you pick an analytics tool before you know what customer insights you need? 

Some examples of common insights that I’ve seen clients want are: 

  • What landing pages should be updated most regularly? 
  • Which blog posts are users reading? 
  • What devices are users browsing on? 
  • How many pages are viewed before a user goes to search? 
  • Which social media platforms generate the most traffic to the site? 

With the proper questions established, now is the time to research and determine which analytics tools will work. Often (but not always), just one or two analytics tools will suffice.  

The course-correction from data-hoarding to data-collector (or miner) isn’t that difficult. It just starts with remembering one important thing: 

Data can’t answer questions you don’t ask.

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