But over the course of dinner he admitted it wasn't that simple. Sure, dedication and hard work and staying on top of the right issues and opportunities was a critical factor, but how did you know what the RIGHT things are to focus on? Couldn't you just as easily fail by having a relentless focus on the wrong stuff?
Zynga is an amazing success, how amazing we can only guess until they file for an IPO though we do know they have grown to 1250 employees already and are continuing to grow fast. But my friend didn't claim that he and the other executives were just so smart that they had simply known the right things to do as they gre the company. And while he admitted that there is an element of luck, when pressed he began to talk about something very interesting, something that probably is the difference for all five of those companies. He said,
"we have one of the best data analytics guys in the world"
Why is this important? Because it represents an enormous sea change in the way business is conducted. There are companies that have truly embraced a data-driven culture, and they are rewriting the way decisions are made, issues are surfaced, and on what we should relentlessly focus on to succeed.
The hierarchical decision making process of the twentieth century promoted the best decision makers into positions of greater decision-making authority so that the smartest and most experienced (although sometimes the best political players) made the important decisions for our companies. But that isn't what happens in data driven businesses.
At a lunch with some friends on the Google campus recently one of them gestured around the Mountain View lunchroom and asked me, "do you think anyone in this room knows what our stock price is?" He went on, "most of them don't, but they do know the data on how many users are actively using their products." And people at Google relentlessly focus on this metric. Looking at "7 day actives" or how many people used your product (or feature) in the past week means that you can, each week, establish experiments and evaluate the impact that making one or another change has on usage. As has been discussed elsewhere (How Google Works infographic), virtually every search you do on Google is either part of an experiment or control group. This iterative product improvement method is well understood in web businesses because data is an inherent part of the customer experience. But the principles can be applied much more broadly.
The first task is to understand what data a given business needs to have to make better decisions. This in itself is an iterative process, with the definition of what data is valuable evolving over time. The second task is to develop the right processes to collect this critical data. Web businesses are swimming in data but often the wrong kinds, so even here there is an important job to be done which we call "incentive design." In short, how do you design your customer interactions in order to obtain the data you need to make decisions? And how do you use new social and mobile interactions to collect data you never had access to in the past.
Exposing this data inside the company is a critical step in the process -- you don't know who will need access to the data to make a decision so in a data-driven culture the company provides a powerful dashboard for all employees to explore and interrogate the data being collected. This is where the name of our company, Open-First comes from -- employees need open access to complete and accurate information in order to make good decisions. So change your culture to be open first, before you do the next step. The next step though is all about the data.
Once you have the data you can start talking about experiments that let you rapidly and inexpensively test your ideas and give you the feedback loop you need to find your focus. Andrew McAfee talks about this in his recent HBR article titled "IT's Three Key Organizational Transformations" in which he says
I see companies in all industries using computers to accomplish three broad and deep transformations: they're becoming more scientific, more orchestrated, and more self-organizing. None of these is complete yet, and I doubt that they ever will be. This is because innovation keeps opening up new opportunities to go further with orchestration, self organization, and science, and companies keep taking advantage of these opportunities.
These three elements are all critical, a scientific approach (we call our methodology PHAME) and devolved decision making (self-organizing which is at the same time coordinated (or orchestrated) results in an organization that is capable at moving at a speed that hierarchical twentieth century business is incapable of duplicating. So companies like Zynga will run circles around traditional businesses.
Your business needs this - a deep understanding of the data that you need to make decisions, the right processes for gathering that data, tools for presenting the data to your employees, and an experimental methodology for learning from the data and decisions.
And oh yeah,