By far, the number one question I get when speaking with executives is: “How can my company leverage data (big data, data science, ML, AI) effectively?”
Sometimes, they shorten it to: “What should my company do with its data?”
I understand why they ask. Every day, they read how other companies do amazing things with data, so they wonder what data can do for their company.
The catch is that they ask me this question in haste, and want an equally hasty answer. A question this deep and this meaningful deserves a lot more thought than that.
When you’re asking how your company should use its data, there is almost never an answer which is all of: quick, meaningful, and specific to your situation.
Sometimes you get two out of three. Answers that are both “meaningful” and “specific” come from really digging into your company’s needs. On the other end of the spectrum, “quick” and “specific” answers are just tips. Those are great to refine existing work but they make for a terrible start. You simply can’t build a solid foundation on tips.
If you want to succeed with data, and you don’t want to rely on sheer luck, you’re going to have to do some homework. You need to explore your business model with an eye for what is genuinely possible with data.
In an ideal world, this would be the job of someone who knows AI very well, knows your domain very well, and knows the particulars of your company. It’s pretty rare that a single person has all of that knowledge, though, which is why developing your company’s data strategy involves your executive team (who know the company) and an outisde party (who knows the AI landscape). Working together, they map out the opportunity space for your company and develop a plan for reaching your goals.
That is certainly not a a two-second exercise, which is precisely why it leads to guidance that is meaningful and specific.
Sometimes executives try a shortcut: “what are similar companies doing with their data?” This is a not-so-subtle way of asking me: “Can I just copy my competition?”
Checking out the competition isn’t a bad idea, but it’s not suitable for long-term planning. Data analysis is such a new concept in many companies that they are still figuring out how to apply it to what they do. If you only care about copying your competition, it’s unlikely you’ll develop truly useful, innovative practices that will put you ahead of them. You’ll remain an also-ran.
And that’s if you’re lucky enough to have access to the exact same data as they do. If your competitors have spent years developing some proprietary data assets on which to build predictive models and analyses, there’s no way you’ll catch up copying just their actions.
This also requires that your competition actually publishes what they’re up to. Sure, they may release an occasional press piece touting their latest AI-infused product or the like, but expect them to keep the secret sauce… well, secret. You can’t copy something if you don’t know about it.
Now you see why the question “What should my company do with data?” requires more than an off-the-cuff answer or a quick tip. This is a deep question that deserves thorough research and planning.
Take the time to methodically develop and execute a plan, and your patience will pay off.