It’s still true that when you’re overwhelmed by an onslaught of big data you should stop and answer three essential questions: What? So what? Now what? This will help you get a handle on the data, think about its implications, and turn your insights into actions. What’s changed since my original article on this subject is the accessibility of artificial intelligence tools to help answer those questions.
What?
“What” is still about facts. As many people, including Daniel Patrick Moynihan, have said, “You are entitled to your own opinion. You are not entitled to your own facts.” A fact looks the same from every perspective and through every filter including artificial intelligence. The data itself should not be up for debate. Asking “what?” is a search for truth.
Fact: The average temperature in Paris is warmer in summer than in winter.
In their Harvard Business Review article, Andrew McAfee and Erick Brynjolfsson suggest what’s new about data today are its volume, velocity and variety. Much of the new “big” data is the same as the old data. There’s just more of it, allowing for much more refinement. Additionally, the data is available much faster. New data streams from devices like cell phones and websites simply didn’t exist before. The critical element here is to know “what” data to collect and examine; otherwise, it’s easy to get overwhelmed.
So What?
“So what?” gets at opinions. What does that data mean to you? What conclusions can you draw from the data? This is where you tie Big Data to meaningful analysis and insights to help you make decisions.
Conclusion: People in Paris will spend more time outdoors in summer than in winter.
Matt Ariker, then COO of McKinsey’s global Consumer Analytic Practice, explained in his Forbes article the importance of having a crisp business hypothesis to test. “Destination thinking” –is crucial for helping you figure out “what” data to go after in the first place. With your destination thinking in place, you can search for a statistical relationship between data and results. Without it, you run the risk of analysis paralysis because there is always more data to analyze.
Ariker told me “you can’t use Excel on (Big Data),” but that there are all sorts of new tools like Microstrategy, Business Objects, QlikView and Cognos enabling everyday people to ask and answer questions. Advanced users have new tools as well like Aster Data and Hadoop and now artificial intelligence to answer Big Data questions with ease.
Now What?
“Now what?” turns your conclusion into go-forward actions. In line with your conclusions based on the facts, what is your recommendation for what should be done next?
Indicated Action: Employ more waiters for outdoor cafés in Paris in summer than in winter.
One of the keys to making this work is linking facts, conclusions and indicated actions. For many, this is not a new idea. A long time ago, statistician, W.E. Deming said, “In God we trust. All others must bring data.” Culture is the only sustainable competitive advantage and the emergence of Big Data makes embedding fact-based decision making in your culture that much more important.
This is where artificial intelligence can be a trap.
AI What – Definitely use it to help you sort through the facts – what?
AI So What – Definitely use it to summarize the data and guide conclusions – so what? The key word is “guide.” Know that the current artificial intelligence tools analyze currently available data. They can provide important input into your conclusions, but, in many cases, not everything you need to draw the final conclusion. For that, you need to turn data into information and mix it with your own experiences to turn it into wisdom if you want to draw wise conclusions on the way to wise decisions.
AI Now What – Your actions should flow from your data and experience-based conclusions. This is why you should not let artificial intelligence tell you what to do. Don’t jump straight from the data to action. Take the time to think it through.
Had Chesley Sullenberger followed the data-derived protocols when both engines on his plane were disabled by a bird strike, he would have crashed. Instead, he analyzed the data in the light of his own experience and chose to land the plane safely on the Hudson River.
John Hall
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