Consider this: Of all the world’s data, 90 percent was produced just in the past two years. And from the looks of it, things don’t seem to be slowing down.
Data is everywhere around us, and the ways it is used, absorbed, assimilated, and disseminated are changing constantly. But even more interesting is how this data is being collected and grouped to help us understand—and eventually predict—the decisions we make every day. This is the primary focus of predictive analytics.
From the point when you get up in the morning and check the news (or, let’s be honest, Facebook) to how you find the quickest route to work, predictive analytics is playing a role in the way you make your decisions. It’s not just pulling data out of thin air, it’s calculating and analyzing your digital footprint to give you information best suited for your specific needs. The possibilities this brings are endless.
There are few people as well versed with predictive analytics as Mark Abdollahian, CEO of Acertas and co-founder of Sentia Group. Abdollahian has a profusion of experience from corporate strategy to national policy, and from economic development to finance. His work informs the U.S. government, the World Bank, and the United Nations, as well as various global private sector companies.
Most importantly, he is someone who believes that analytics can revolutionize and change our lives in ways we had never even thought possible.
Abdollahian says a strong indication of this revolution is the growing prevalence of big data. Today, data is being used to inform and operate increasingly complex financial, social and political systems.
Most data scientists break big data down into four components: volume, velocity, variety, and veracity. Volume refers to the sheer amount of data being created every day (nearly 2.5 quintillion new bytes); velocity is the speed at which all this new data is created and streamed; variety refers to the different kinds of information being created constantly, with the Internet of Things really driving its growth in recent years; and veracity refers to the quality of data, whether accurate or inaccurate.
However, without automation, dashboards, algorithms and predictive analytics, human beings would have “paralysis by analysis” given the sheer volume, velocity and variety of data being generated each day, and not be able to make better decisions.
Because we now have the ability to be more informed—with computers assisting us in performing complex calculations faster than we could have ever hoped—we can build a more educated and accurate set of predictions.
However, there is also a growing need for data to accommodate for something Abdollahian likes to call “complex connectivity.” Global connectivity today, through big data and social media, is beginning to create increasingly complex interactions around the world, more than we ever thought possible. Abdollahian employs the use of behavioral modeling, which places a heavier focus on human causation—that is, why we do the things we do—in addition to more traditional market, aggregate or survey data.
According to Abdollahian, the key is to accommodate for the inherent human, cultural, social, and financial uncertainty in all of our behavior. He believes the exercise of predictive analytics is very much tied to taking big chunks of information and noticing the structures and patterns that emerge with it.
It comes as no surprise that this can be invaluable to how we do marketing today as well. In that case, predictive analytics data would first be used to understand the environment, whether this is in traditional or digital media, social narratives or individual sentiment, global or local. From that, companies can package the right message and strategy for the right time and situation. We see these kinds of analytics everywhere, from the front page of The New York Times to our Facebook news feeds.
By taking vast varieties of information we collect through big data and combining it with behavioral modeling, we can better track and predict individual behavior, which leads to anticipating collective behavior and better societal outcomes.
“As big data and behavioral modeling merge, a lot of insight will be created,” said Abdollahian, “but as with most technological revolutions, it’s our responsibility whether we use that for better or worse.”