As we bring 2012 to a close, many have declared this as the year of big-data and big-data analytics. As exciting as that may seem for all you data lovers, don't get out your party hats and noisemakers just yet.
With all the hoopla surrounding big-data in 2012, you just know the balloons are going to start popping -- and soon. Next year, in fact, predicts the International Institute for Analytics (IIA), an analytics research firm.
"Big-data basically meets organizational reality in 2013," said Ravi Kalakota, IIA faculty member and partner at LiquidHub, a global management and technology consulting firm.
On Tuesday, Kalakota and a handful of other IIA faculty members revealed the organization's eight analytics predictions for 2013. They are as follows.
The big-data bubble bursts. "We expect many of the startups that were funded by venture capitalists to run out of customers and revenue, leading to mergers, acquisitions, and outright failures," said Sarah Gates, vice president of research quality at IIA. The shakeups and shakeouts will be typical of what we see happen in an emerging technology market, Kalakota said. Many big-data startups -- he said he's heard that 360 such companies launched in the past year -- are trying to cross the chasm between experimental and production deployments. "But we're seeing a stall in the market... and when there's a stall, that puts startups at tremendous disadvantage." Watch for fallout on the vendor side, too, said Tom Davenport, IIA research director and well-known business analytics author and advocate (and AllAnalytics.com blogger).
Cross-industry cooperation thrives. This prediction is about merger and acquisitions within industries rather than across industries, as we'd see relative to the first prediction. "These are forming to exploit new market opportunities that are significantly facilitated by the new data that's available to the enterprise. These are big players, and these are disruptive innovations," said Dwight McNeill, IIA faculty member and president of WayPoint Health, a healthcare analytics consulting firm. This is about grocery stores getting into banking, as in the case of UK chain Tesco and Tesco Bank, and drugstores getting into healthcare benefits, as we've seen with CVS Caremark, he said.
Small analytics teams spring up. No doubt, the shortage of data scientists persists in 2013. "That means firms will need to focus more on the composition, development, and deployment of small analytical teams rather than struggling to find the perfect data scientist," Gates said. Companies aren't going to find the requisite analytical, data management, interpersonal communication, and technology skills in a single person. So it's the aggregation of those skills in a team that will matter, Davenport added.
Data scientists lose their distinction. "At the end of the day, when you look at the types of problems you're asking these people to solve, they really do fall into the province of statistics and other quantitative methods. You're dealing with data, and you're wanting to handle the uncertainty in that data, and to make predictions and to anticipate the future," said IIA faculty member Anne Milley, senior director analytic strategy, product marketing, at SAS (this site's sponsor). The lines blur, too, as technical people get experienced using large, messy, unstructured datasets. "They can encroach on the data scientist territory from that end," added Bob Morison, IIA faculty member and consultant. And as traditional business analysts gain experience with sophisticated analytics toolsets, the lines will blur there, too.
Customer-driven analytics transcends product-driven analytics. "We're seeing an acceleration in the evolution from multiple channel to cross channel to omni channel, or what you would call cross cutters," Kalakota said. This is about capturing insights on those customers standing in your place of business, product in hand, but surfing on their smartphones and making the purchase online, for instance. "Increasingly we are seeing analytics as the mechanism for making that happen."
Companies get smarter about machine learning. In other words, companies are going to figure out which applications of machine learning have the greatest return, said Gates, noting that this is taking place in retail, for example, with pricing optimization. Big-data forces this issue, "simply because we can't churn through all that data without having machine learning," Davenport added.
Insight gets more visual. With big-data comes the need for more interactive, dynamic data visualizations -- something that today's tools are getting better at delivering, Milley said. Data visualization helps not only in the analytics discovery process -- spotting patterns in the data, for example -- but also when presenting the information to the business.
High-profile data breaches drive development of predictive analytics for security. Detecting the source of data breaches has always been tough and will only get tougher as perpetrators get more sophisticated, Davenport said. It's time to bring in the predictive analytics.
Davenport said he hopes he's wrong about this last one -- as do I. How about the rest of you? Which do you think IIA has gotten right, and which ones don't make sense to you?
As long as we're on the subject of forecasts for the future, I thought this was as good a thread as any to comment on economist Paul Krugman's musings about Big Data, Analytics, and robots in today's New York Times:
Krugman focuses on the issue of the so-called Industrial Revolution 3 (IR #3), described as "computers, the web, mobile phones" and lasting "from 1960 to present." Krugman takes issue with the suggestion "that IR #3 has already mostly run its course, that all our mobile devices and all that are new and fun but not that fundamental." While he says it's "good to have someone questioning the tech euphoria...", Krugman says he's "pretty sure" that "the IT revolution has only begun to have its impact."
Krugman speculates Consider for a moment a sort of fantasy technology scenario, in which we could produce intelligent robots able to do everything a person can do. Clearly, such a technology would remove all limits on per capita GDP, as long as you don't count robots among the capitas. All you need to do is keep raising the ratio of robots to humans, and you get whatever GDP you want.
Now, that's not happening — and in fact, as I understand it, not that much progress has been made in producing machines that think the way we do. But it turns out that there are other ways of producing very smart machines. In particular, Big Data — the use of huge databases of things like spoken conversations — apparently makes it possible for machines to perform tasks that even a few years ago were really only possible for people. Speech recognition is still imperfect, but vastly better than it was and improving rapidly, not because we've managed to emulate human understanding but because we've found data-intensive ways of interpreting speech in a very non-human way.
And this means that in a sense we are moving toward something like my intelligent-robots world; many, many tasks are becoming machine-friendly. This in turn means that Gordon is probably wrong about diminishing returns to technology.
However, Krugman warns, the longterm outlook for the great mass of us is not necessarily so rosy: Ah, you ask, but what about the people? Very good question. Smart machines may make higher GDP possible, but also reduce the demand for people — including smart people. So we could be looking at a society that grows ever richer, but in which all the gains in wealth accrue to whoever owns the robots.
@Lyndon, I think the answer is "all of the above." Big-data being the trendy term that it is, we see entrepreneurs of all sorts latching onto the phrase -- be they purveyors of goods and services or companies using big-data themselves to create a marketplace differentiation. There are a ton of "big-data startups to watch" type of lists out there. Here's one from VentureBeat, for example.
I agree with all these predictions, especially the close down of many startups. I've had the priviledge to be a guest at a few angel investor meetings, (despite that I don't have a few hundred grand to invest ) and have seen so many "me to"s (What is the plural of "me to"? tos, toses? ) And I often think that they are too late into the game and nothing to differentiate themselves from existing players.
@ Lyndon Henry - I can't say for sure what are the startups are, but the ones I'm encountering appear to deal with mobile advertising and conversion. The ones I think that stand the best chance are the ones that are cashing in on the sharing economy, i.e. renting out your own car or parking space.
With the shutdown of big-data startups anticipated for the year ahead, will anybody find themselves in the lurch regarding big-data projects? In other words, any experimentation with startups going on out there?