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?
As enterprises steadily connect production equipment, transportation networks, environmentals, and other technologies, they will face the challenges of analyzing, digesting, and acting on volumes of new data. No, we won't throw a switch and suddenly have an Internet of Things, but it will evolve in the coming years, and it could pick up its early momentum in the enterprise space.
NRF Retail's Big Show 2015The flagship industry event of the National Retail Federation, Retail's Big Show is an annual event held over four days in New York City. As the world's leading retail event, the Big Show brings together 30,000 retail professionals and vendors from more than 86 countries, and features more than 100 education sessions, 270 speakers and 550 exhibitors. The conference connects retail solution providers with retail executives searching for the most effective solutions, tools and technologies.
LEADERS FROM THE BUSINESS AND IT COMMUNITIES DUEL OVER CRITICAL TECHNOLOGY ISSUES
The Current Discussion
Visual Analytics: Who Carries the Onus? The Issue: Data visualization is an up-and-coming technology for businesses that want to deliver analytical results in a visual way, enabling analysts the ability to spot patterns more easily and business users to absorb the insight at a glance and better understand what questions to ask of the data. But does it make more sense to train everybody to handle the visualization mandate or bring on visualization expertise? Our experts are divided on the question. The Speakers: Hyoun Park, Principal Analyst, Nucleus Research; Jonathan Schwabish, US Economist & Data Visualizer
The hospitality industry gathers massive amounts of customer data, and mining that data effectively can yield tremendous results in terms of improved CRM, better-targeted marketing spend, and more efficient back-end processes. Roger Ares, vice president of analytics at Hyatt Corp., discusses the ways he and his staff use big data.
Charged with keeping track of travel assets, including employees, iJET International relies on data management best-practices and advanced analytics to keep its clients in the know on current and potential world events affecting travel, Rich Murnane, Director of Enterprise Data Operations & Data Architect, told All Analytics in an interview from the 2014 SAS Global Forum Executive Conference.
Jason Dorsey, chief strategy officer for the Center for Generational Kinetics and keynote speaker at last month's SAS Global Forum 2014, describes how Gen Y professionals are enhancing the makeup of multigenerational analytics organizations.
From analytics talent development to the power of visual analytics, All Analytics found a variety of common themes circulating throughout the exhibition floor and session discussions at the 2014 SAS Global Forum and SAS Global Forum Executive Conference events held last month in Washington, DC.
Talking with All Analytics live from the 2014 SAS Global Forum Executive Conference, Eric Helmer, senior manager of campaign design and execution for T-Mobile, discussed the importance of customer data -- starting internally -- in devising the mobile operator's marketing plans.
The big-data analytics market can be a confusing place. Among the vendors vying for your dollars are traditional database management providers, Hadoop startup services, and IT giants. In this video, All Analytics editors Beth Schultz and Michael Steinhart sit down in a Google+ Hangout on Air with Doug Henschen, executive editor of InformationWeek. Henschen discusses use cases for big-data analytics, purchase considerations, and his recent roundup of the top 16 big-data analytics platforms.
At the National Retail Federation BIG Show last month, All Analytics executive editor Michael Steinhart noted a host of solutions for tracking and analyzing customer activity in retail stores. From Bluetooth beacons to RFID tags to NFC connections to video analytics, retailers must find the right combination of tools to help optimize the shopper experience, streamline operations, and boost revenues.
The days when historical shipment trends and gut feelings were enough to forecast retail demand accurately are long over. SAS chief industry consultant Charles Chase outlines the benefits of pulling real-time sales information from point-of-sale and product scanner systems, then flowing that data into dynamic forecasting tools from SAS.
With today's advanced visual analytics tools, you can stream data into memory for real-time processing, provide users the ability to explore and manipulate the data, and bring your data to life for the business.
Dynamic data visualizations let analysts and business users interact with the data, changing variables or drilling down into data points, and see results in a flash. Advance your use of data visualization with tools that support features like auto-charting, explanatory pop-ups, and mobile sharing.
No doubt your enterprise is amassing loads of data for fact-based decision-making. Hand in hand with all that data comes big computational requirements. Can traditional IT infrastructure handle the increasing number and complexity of your analytical work? Probably not, which is why you need a backend rethink. Big data calls for a high-performance analytics infrastructure, as Fern Halper, a partner at the IT consulting and research firm, Hurwitz & Associates, discusses here.
Redbox's bright-red DVD kiosks are all but ubiquitous these days, located in more than 28,000 spots across the country. Jayson Tipp, Redbox VP of Analytics and CRM, provides an insider's look at how the company has accomplished its phenomenal nine-year growth.
InterContinental Hotels Group (IHG), a seven-brand global hotelier, has woven analytics into the fabric of its operations. David Schmitt, director of performance strategy and planning, shares IHG's analytics story and his lessons learned.