In his book Social Physics: How Good Ideas Spread, Alex Pentland, sets his sights on, among other things, “entire cities” as targets for solutions based on Big Data. Pentland defines Social Physics this way: “social physics seeks to understand how the flow of ideas and information translates into changes in behavior.” The author cites an example where his concept was used to alter the behavior of traders. He explains how the results were obtained: “…we had mathematical analyses based on millions of bits of data that made it possible for us to devise precise interventions and predict precisely what the outcome would be.” The claim of mathematical precision is detailed in the book which includes a Math Appendix.

Not everyone is ready to accept these claims. Writing in The New Inquiry, Nathan Jurgenson opines:

As with the similarly inferential sciences like evolutionary psychology and pop-neuroscience, Big Data can be used to give any chosen hypothesis a veneer of science and the unearned authority of numbers. The data is big enough to entertain any story. Big Data has thus spawned an entire industry (“predictive analytics”) as well as reams of academic, corporate, and governmental research; it has also sparked the rise of “data journalism” like that of FiveThirtyEight, Vox, and the other multiplying explainer sites.

As is often the case, while academics duke out the merits of innovative concepts, the real world marches on. In a recent study, IDG noted that nearly half of companies are already implementing big data projects or in the process of doing so in the future. These companies are increasingly aware of the importance of finding the right type of talent to carry out these projects, particularly those who are performing the analytics. For example in its September Newsletter, Hengtian Services, a technology solutions provider, points out: “Today, data scientists are tasked with finding the co-relations among huge data sets…. A broad knowledge of the different domains of business data and an intuitive understanding of how they can relate to one another are the most important skills of a data scientist.”

The importance of talent to successful Big Data projects is something I have touched on before here. The IDG study included this finding: “Organizations are facing numerous challenges with big data initiatives and limited availability of skilled employees to analyze and manage data tops the list.” Strategies to mitigate the need for additional individuals with these skills are therefore important to pursue. One way is to ensure the people you have on board are well positioned to take on these projects. Among the best ways to do that are to ensure sufficient time and resources are dedicated to understanding your internal data stores and making sure they are incorporated into an overall data strategy.
All the mathematical precision in the world will not yield good results if the data is faulty. Even the Big Data skeptics won’t disagree with that argument.