You can buy all the hardware and software you can afford, and wrap it in a wonderful platform with analytics engines, beautiful interfaces, powerful databases, and all the cloud storage and flexibility you can muster, and still fail at finding the answers you seek to grow your business. Why? People.
Tools are easy—sometimes. People are hard—always. Tools don’t think, yet. People do. They have ideas, idiosyncrasies, and opinions that sometimes make even the best-laid analytics plans go awry. You have to manage and motivate them to turn the toolset you invested in into a high-powered intelligence-producing machine that gives you the answers you need, when you need them.
So, with that in mind, let’s talk about how to put together a team that takes your efforts to make data intelligence pervasive from theory to reality—a reality that exceeds your expectations.
The people that work with data should come in all varieties, but they need to have one characteristic in common: a passion for finding answers. They must be curious and committed, whether they’re data analysts, scientists, or engineers.
After that, there are individual characteristics that each team member should possess. Data analysts must bridge the gap between the business and the data scientist. They’ll be responsible for translating business requirements into a language that the data scientists can understand.
Data scientists, just as their title implies, should be your finders, your seekers of answers, facts, and new horizons of truth that you didn’t know existed. They are a curious lot, and they need constant challenges to be fulfilled. They also need to be reined in and focused on finding information that relates to strategic goal fulfillment while giving them enough leeway to look for those insights you didn’t know you wanted.
Your data engineers must be your builders. They’ll be responsible for building and optimizing your infrastructure, for making sure that the data gets ingested, stored, and distributed to analysts and scientists. Their defining characteristics should be knowledge and creativity. The knowledge of how to cobble together maddeningly complex analytics ecosystems while being creative enough to integrate new tools and technologies into those systems as needs change.
You probably already have these folks on staff—at least the analysts and engineers—but how you deploy them to make intelligence pervasive is different than how they’ve been utilized in the past. I can’t stress enough how different the philosophy of making intelligence pervasive is from traditional approaches to deploying analytics. How your analytics team functions should reflect this difference.
Pervasive intelligence requires embedding analytics at the cellular level of your organization. Data—and its concomitant intelligence—should be everywhere; it should pervade the culture of the company. So should your analytics people. For starters, your team shouldn’t be some monolithic analytics organization that’s ensconced in a separate suite or division. To be sure, your analytics capabilities—and your organizational data—should be coalesced and consistent, but that doesn’t mean they have to be isolated.
In a pervasively intelligent organization, analysts and data scientists should be embedded within the business functions. Anyone who has some technical knowledge, and a healthy knowledge of the business, can be a data analyst, especially if you make it a priority to make your analytics capabilities self-service.
Sure, they’ll have to be trained, and you need to make sure that the people you hire have a curious mindset, but my basic point here is that analysts shouldn’t be people other people go to for answers. Instead, if you’ve really embraced a pervasive intelligence philosophy and made analytics capabilities accessible to the people who need them, everyone can be an analyst in some capacity.
Data scientists are different, though. They are a breed apart. They are curious, wicked-smart, and easily bored. The good ones can constantly make connections that turn seemingly-random data into deep insights that will surprise you time after time. And, as with data analysts, how you utilize your data scientists is different with a pervasive intelligence philosophy.
Because of their nature, data scientists will look for a zebra through the window when they hear hoofbeats outside. They’ll come up with ideas and insights that need to be contextualized through a business lens. That’s not much different than with a traditional approach to analytics.
What is different with pervasive intelligence is that—as with analysts—data scientists need to be embedded within the business functions. It will be fatal to your efforts to embed analytics into your culture to keep the data scientists within the IT ivory tower. They need to walk among the people who do their jobs so that they can understand how their often-esoteric connections actually apply to the real world.
Putting it together
I’m not saying here that your analytics capabilities should be totally distributed and disconnected throughout your organization. Far from it. You can have unified, but de-centralized management and control of your analytics capabilities. Of course, you still need an analytics director or Chief Analytics Officer if you’re large enough. What I am saying is that the people you’ve invested in to give you the answers you seek should be close enough to the business to understand the context of their efforts, so they can get at those answers. If they can, there’s no limit to what you can do.