Everyone won’t stop talking about innovation. What is it? Can we “be” it? Can we do it? Can we do it faster (even though we don’t even know if we can do it yet)? Can we do it with partners or form alliances to do it? People even joke about how much people talk about it.
Executive directors tell us to “be innovative.” They may as well tell us to be French. But if you are going to tell someone to be French, at least give them a free Rosetta Stone account and enough time to learn how to form the condescending frown when you utter phrases of contempt at your croissant (and you should pronounce that as “cwah-sownt”). And if at the next leadership meeting my fearless leaders start handing out croissants and the lyrics to Lady Marmalade, I’m leaving the company.
Here is my definition of innovation: foresight to realize what the problems of tomorrow will be, coupled with the creativity and technical ability to proactively solve that future problem.
Today, some things are so “accepted” that we don’t yet even know they are problems. By the time we realize it’s a problem it has already been solved by those with the foresight: the Innovators.
And now we’ve got a real challenge to sic our innovation skills on: Big Data. But with this Big Data thing, it just seems like we’re missing the Big Picture. I’m going to say it: we’re not innovating. We aren’t seeing the problem so we can’t solution it. The question becomes, who will have the foresight to accurately frame out the problem?
Big Data is a concept around data volume and complexities that are building up so rapidly due to the myriad ways we generate data in this new digital, social-technological age. Big Data contains such treasure troves of information and learnings within, but we just don’t have the capability yet of drawing this knowledge out due to our geriatric ways of storing, thinking about and learning from conventional data.
Big Data should yield “Big Answers” – not in size but in meaning and quality. So often, analysis on Big Data yields truths that we already kind of knew in our gut. For example, from a pharmaceutical perspective – when reports of adverse events for a drug go up, sales go down. Or, the longer you are on an osteoporosis drug the more likely you are to break your femur. There is no foresight involved in this. And if there is no foresight, there’s no innovation. Many have said that the challenge is to use Big Data to find answers to questions that we don’t even know to ask.
Right now, we are generating data faster than we can categorize or structure it. So how will we ever make use of it in the future? We are data hoarders trying to be data herders. Is there really a problem though? Or are we creating the perception that there is one to attract the problem solvers?
An aside: What came first the data or the data problem? Soon enough we’ll realize there is a solution to that just like there’s a solution to the chicken-egg problem (the egg came first. What laid it?: something that was ALMOST a chicken of course).
The solution to our current problem is that we are misunderstanding the future problem, so we are therefore misinterpreting an opportunity as a current problem. We aren’t being [*sigh*] innovative. I think the people “solving” this Big Data problem are not the right people to solve it. They are too close to an incorrect perception of the problem. It’s like asking the Imperial or Domino sugar companies to try to figure out where I’m going to put all this excess sugar they want to sell me…well I don’t take sugar in my coffee anymore so I’m not buying any of it…unless I’m going to make cookies of course. Then I’ll need some.
So what do we need?: someone to re-frame the problem. That’s what I’m here for. The problem isn’t how we are going to find a way to use all of this Big Data. How about this for futuristic foresight: we shouldn’t need any of it! It is historical archives that hold little meaning if we have a perfect understanding of the current. That’s the place we should be driving innovation to. Who cares what my brain scan showed last year. Only my current scan matters. If you want to argue that last year’s matters so I can tell how something has changed, well, if you have perfect understanding of the current, you don’t need to know about change. The need for change data is coupled with an imperfect current understanding. How do you get perfect understanding of the current? Bingo! That’s the question.
This “perfect understanding” of the current does require tremendous computing power and capacity, an unprecedented capability to analyze and instantly asses, and an as of yet uninvented way of encapsulating the current, past and future states of an entity within an accessible location. So I will still need some of that sugar – somebody let Intel know.
So next time someone tells you to start “driving innovation” think of foresight and a perfect understanding of the current and how that can be achieved. And remember, this isn’t Miss Daisy in the back seat of this car. We are driving innovation to a better place where Truth is known regardless of any past data about Truth that may have been generated and categorized.
And don’t tell them the Functional Lunatic told you. I’m busy learning to be French. Au revoir!