What is growth made from?
Where does success come from? Why is it so hard to sustain? Clayton spends the latter half of his finding ways to find better ways for business.
Historically, companies that look virtually untouchable, 10 to 20 years later find themselves in the middle of the pack or at the bottom. Some of the reasons why are due to well-educated leaders that fund money towards efficiency, not innovation. What seems like solutions are not ways to grow.
Theories are statements of causality. It’s ‘what causes why’. Theories get a bad label by many because it sounds like ‘theoretical’ – you’re just guessing. In many cases, theories come from leaders that don’t have much else. They lack the tools and may have some bias about how to solve problems.
But is it really their fault? Most people don’t have access to predictive data sets that are truly predictive in nature. And they don’t know how to combine predictive with historic data.
Here is my point: I would bet you could drive to New Orleans from your current location.
Just for argument’s sake let’s say that you’re somewhere in North, Central, or South America,
You have a good credit card and the car is in reasonable health to get you to New Orleans.
You’re not allowed to talk to anybody along the way. No asking for directions, no offline paper maps. Just signage, the Sun. Normal human things allowed.
Just to make it a little bit interesting, I’m going to put tape all over the dashboard. That really sticky stuff that you can’t peel off. You can’t see the instruments at all. Can you still drive to my house? Yes. You know where the sun rises, you have a credit card and you can stop every 4-5 hours to get gas no matter what. You can keep up with existing traffic so you’re not speeding. It might take a little bit longer but you’re going to get to New Orleans.
Why is that the case? Because you have predictive data and you have intelligence. Even if you’re not too good with geography you have some basic understanding of where things are relative to other things. It’s a little bit like Bayesian mathematics. You don’t know where New Orleans is but you know where it’s not.
Now let’s keep up our experiment. Let me give you back your dashboard But I’m now going to take away the history of the car. It turns out it’s a piece of crap. It might break down. You certainly can prepare and bring some spare parts. This could be problematic. You might get to New Orleans but you might break down in the middle of the jungles and you don’t have the tools needed. Chances of getting to New Orleans have diminished. If the car is healthy, just like a business you will make it.
Now finally, I’m going to give you back the history of the car and I’m going to give you back your dashboard. only one problem, I’m going to black out the windows. You can’t see the Sun and you’re not allowed to see signage at all. Do you think you can make it to New Orleans? No. If someone wants to make the argument that a Tesla could make it, fine I’m just blacking out the cameras also.
What I did is I took away your predictive data. In life, it’s devastating, For business, we’re allowed – ego, bias, mansplaining,, leadership authority Indulgence. You name it, guessing is allowed. You’re probably saying no I have Predictive Analytics, I have data scientists and they can see the future because they’re studying tons of data ……..of the past. No. if you’re cutting up data and trying to train a machine based on historic data, you’re not truly pulling it off. You’re not creating predictive data sets based on theories. You’re creating pseudo predictive data sets based on knowledge of the past. You can’t drive to New Orleans if you can’t see out the windows.
Dr Dean Spitzer Wrote a book that touches upon this subject matter. Most businesses will acquire hordes of data that’s easy and cheap to acquire but offers little value about creating predictive data sets. It’s the difficult to acquire expensive data that can create hypotheses about the future.
As Clayton Christensen would point out, it’s due to a focus on profitability growth at the expense of innovation. I agree with that but even the best Consultants, lacking true predictive data sets can be dangerous.
My hypothesis is for leaders to make good decisions, they need some level of confidence that the investment will pay off. So here are some steps to consider:
Challenge your organization and yourself: Do you truly have predictive data?
What are some ways that you can create this? I would make the argument that any business can create predictive data sets.
Do your best to not introduce bias and ego into how these data sets are created. It’s hard. Let those that are closest to the problem be part of the solution.
Integrate predictive data sets with historic and real-time data realizing that all three are important but all three have separate KPIs.
Once this capability is in place, make sure you test it and verify your hypothesis before rolling out two larger decision-making. just like the car example, when you do have true predictive data capabilities, you will discover quickly that you can go fast. There’s far more confidence when you have the right sets of data. Testing goes much faster and you’re going to reach Product-Market Fit with more confidence.
Where Does Growth Come From? Clayton at Google.