At a gathering at the Andaz Hotel in New York, October 22, a number of the alternative investment industry’s best and brightest discussed the new drivers of growth.
The event, sponsored by Gravitas, turned on four themes: Big Data; Branding; Cybersecurity; and Innovation.
I’d like to focus on just one of those points, the first of them, for further discussion here. So: what is Big Data and why might it be one of the major Drivers of Growth for 2015?
Learning to Tell Time
When I was a child, one of the rites of passage involved “learning to tell time.” It is likely that parents skip this in our enlightened second decade of the new millennium, because digital read-outs of time are so ubiquitous that there is no real margin any longer in teaching one’s little one how to draw inferences from the “big hand” and the “little hand.”
That reflection points to one of the meanings of Big Data. In one area after another (most of them of course much more involved and intricate than that), matters that once would have been the consequence of laborious research and inference are now available immediately. So one is looking at a display rather than thinking about the hands of an analog clock. On the one “hand,” this means that the skill involved in making the inference to get there the old-fashioned way is obsolete.
But, on the other hand, the Big Data makes possible new ranges of inferences, and gives value to new skill sets. As Mark Graham of the Oxford Internet Institute has put it, there will “always be uneven data shadows; and always be biases in how information technology and technology are used and produced.” However blinding the light may become, there will continue to be plenty of roles for human beings in recognizing the “shadows” Graham mentions and sorting out the patterns they make within the light.
In-store radio frequency identification (RFID) tags, sensors, smart meters, are each examples of what SAS has called the “torrents of data in near-real time” now available.
The New Skill Sets
That rite of passage from my ‘60s childhood also taught me about ambiguity. The talk of the “big hand” confused me at first. One of the hands is fatter, the other is longer. Which of those characteristic is meant by “big”? In time I figured it out. But the term “big” in “big data” includes a similar ambiguity. Sometimes the focus is on the velocity of the data streams, sometimes on the sheer volume, and yet at other times it’s on the wide variety of sources with which entrepreneurs, managers, and investors must all deal now.
The new skill set for those who would be empowered, rather than left behind, by Big Data includes the ability to integrate everything from hard numbers to sentiment-conveying tweets; weighing the intangibles along the way and deflecting the fat tails of chaotic butterflies.
I wrote about this thirteen months ago, and then quoted Paul Rowaday enthusing about the new social media and their ability to let institutions crowd source high-volume data on catalytic events “from just about anyone, anywhere and at any time.”
Big Data could have been one of the big stories of the year now lumbering toward its close. But it wasn’t. Big Data in 2014 was, we might say, the dog that growled a bit, but didn’t quite bark. According to one recent (September 2014) study, 73% of surveyed large companies said that they plan to invest in Big Data technologies over the next 24 months. That figure is up from 64% a year before. Only 47% of the same companies, according to the same (Gartner) survey said they actually have invested in such technologies. So the talk is about what will happen. The dog is growling.
Yes, one has to admit, in some areas of public consciousness he has barked already. Throughout this year, both general public and computer cognoscenti have come to think of Big Data as a national security issue on the one hand; a threat to privacy on the other.
But Gravitas is right. The alt-invest world might well expect to hear this dog barking out through much of the year to come. Managers might well want to include Big Data oriented firms in their portfolios, to use Big Data in making their own allocation and strategic decisions, and to use it in the back office.