Asking Good Questions is What Will Make Big Data Work for You

Asking good questions as the key to unleashing the potential of big data got significant blog time this past week.

The asking questions part, of course, is not such recent news.

Isidore Isaac Rabi, winner of the Nobel Prize for physics in 1944, loved to explain how he became a scientist:  “My mother made me a scientist without ever intending to. Every other Jewish mother in Brooklyn would ask her child after school: So? Did you learn anything today? But not my mother. ‘Izzy,’ she would say, ‘did you ask a good question today?’ That difference—asking good questions—made me become a scientist.”

But the big data part is new and sometime confusing. Does big data means that if you have lots of data, you’ve got all the answers?

In a recent post titled “There’s no such thing as big data,” Alistair Croll pointed out that just having lots of data doesn’t make a company smarter: “Companies have countless ways they might use the treasure troves of data they have on us. Yet all of this data lies buried, sitting in silos. It seldom sees the light of day. When a company does put data to use, it’s usually a disruptive startup….  One by one, industry incumbents are withering under the harsh light of data.”

Right. But what make these startups “disruptive” and smart about using their data?

In a follow-up to Croll on Google+, Tim O’Reilly provides a clue to the answer: “…companies that have massive amounts of data without massive amounts of clue are going to be displaced by startups that have less data but more clue, who will put in place the dynamics to make the most of the data they have and to collect new data in self-reinforcing applications that get better the more people use them.”

At the 451, Mathew Aslett continued the train of thought: “There has been so much emphasis of the ‘big’ in ‘big data’, as if the growing volume, variety and velocity of data itself would deliver improved business insights.”

To get to the desired business insights, Aslett advocates focusing on defining and delivering “simple models.” But who will do that?

Jeff Weiner, CEO of LinkedIn, tells the The Telegraph that data scientists and data tools have become the most valuable people and features in Silicon Valley: “The ability to extract useful signals from the stream of information is key to success moving forward. This is why we are going to invest a lot in this type of technology and capability. As long as we can apply correct, relevant algorithms we can stay relevant to users. If you are unable to create relevant experiences, you risk losing users.”

Joining this great discussion of the importance of and the skills necessary for asking the right questions, I added my comments in a blog post for AllAnalytics, reviewing The McKinsey Global Institute (MGI) report on big data:

“Big data, it turns out, is not about data. It’s about adding human intelligence (and training in disciplines other than computer science). It’s about the brains who can develop models that help us find insights in the data. The great new technologies that allow us to process big chunks of data do not provide answers on their own. These come from people, in the MGI’s words, with ‘deep analytical talent — people with technical skills in statistics and machine learning, for example, who are capable of analyzing large volumes of data to derive business insights.’

If coming up with the right models for analysis is what’s important, big data, it also turns out, does not need to be big. With the right model, you may deduce powerful insights from a small set of data, depending on its quality and how representative it is of the population you are analyzing.

It all depends on your question.”