Copyright: andresr / 123RF Stock Photo

Beware The Underpants Gnomes of IT

“First we get the data together, then AWESOME HAPPENS.” This is the simplified version of most sales pitches that fall prey to what I like to call the underpants gnomes problem of IT & Security. I’m pilfering the Gnomes concept from South Park — they featured in the 30th episode (overall) and had a very simple business model:

  1. Collect Underpants
  2. ?
  3. Profit

When data warehousing (EDW) was all the rage, it made me think of the gnomes. The early days of business intelligence (BI)? Gnomes. The “correlation” magic that goes into most managed security service providers (MSSPs)? Yup, that’s the gnome’s business model there too. More recently: big data & analytics.

Delivering Marketing is Faster Than Delivering Capability

These are silver bullet propositions. As an emerging capability hits the market, companies rush to promise they can deliver… even when they haven’t quite figured out how yet. This leads the rest of us through the emotional rollercoaster Gartner calls the Hype Cycle.

In the IT & IT Security fields, it’s hilarious (I’d rather pretend it’s funny) to watch companies follow the same pattern repeatedly around data. The story remains consistent around getting the data in one place, doing something with or to it — we’ll figure out later — and then delivering magic answers to the questions you didn’t even know you had. One day, perhaps we’ll have technology that can do that… in the meantime we’re liable to just find disappointment.

Take a look at the 2014 Hype Cycle, for instance and look for Big Data. There it is, on the cusp of falling into the Trough of Disillusionment. If you were walking down 4th Street in San Francisco at last year’s RSA Conference, you’d have seen giant BIG DATA advertisements on your left heading to the Moscone center. One year later, 2015’s Hype Cycle doesn’t feature Big Data at all. What’s replaced it? Machine Learning. Synopsis: Give the machine all your data, [machine learning], AWESOME HAPPENS! Sound familiar?

That said, there are legitimate technology advances buried in here that could help your organization. How can you separate the wheat (assuming there is some) from the chaff and optimize for getting value sooner?

Avoiding the Traps

If you’re prone to experiment with emerging technologies, here are a few ideas that can help you find success:

Moderate your expectations of success.

Ok this might be a little circular, but in all seriousness don’t get your hopes too high. There’s a good chance you’ll eventually get value out of the solution but be cautious about letting your expectations drive up what you’re willing to pay up front.

Know what Questions You’re Trying to Answer

While GnomeTech’s products leverage Big Data Analytics powered by Machine Learning Techniques to help drive business value — consider what questions you need answered to get that business value up front. Generally solutions that promise learnings via data analysis do best when they’re tuned to answer specific kinds of questions with an appropriately quick response rate. Generic solutions may be able to answer any question , but you might not be willing to wait around for the answer… or even know what questions to ask.

Installed is Not The End State. It’s The Beginning.

Be prepared to invest time and be prepared to iterate. Companies playing with emerging technologies haven’t yet extracted lessons from their customers. They lack the maturity to be able to say: “Of our three thousand deployments, 75% of our customers have [these] kinds of questions and that’s why we’ve optimized to provide [this] kind of reporting. In your industry, we’ve also found customers ask [this other] set of questions too.” This also means they desperately need your feedback, so hold them accountable — it’ll make them better over time.

There is Value. You Gotta Wait For It, Though.

Ultimately we usually do see value out of technologies that initially fall into the underpants gnomes model. But there’s a considerable amount of dismay between inception and productivity. Expect disappointment in the early years and a hard slog towards extracting real value from the capability. Or, wait it out until companies are able to more effectively articulate and demonstrate step two.

Otherwise, you’re leaving yourself open to having unused tech sitting around collecting dust with nothing to show for it but a lack of underpants.

If you found this worth your time, consider following @reefhack on Twitter, RT, and recommend this article by clicking the heart below.