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Big Data and Cognitive Computing will Drive Digital Enterprise Transformation

April 20, 2016


The term “big data” is likely to run out of steam as the Internet of Things (IoT) and artificial intelligence (AI) systems, like cognitive computing, mature. It’s not that data is going away — just the opposite. So much data is going to be generated in the years ahead that calling it “big” will seem silly. Nevertheless, Randy Bean (@RandyBeanNVP), CEO and managing partner at NewVantage Partners, believes the time has come for big data initiatives to produce results. He reports that a survey published by his company found “that nearly two-thirds of participating executives indicate that a Big Data initiative is in production at their firms. Yet, many of these investments have yet to produce tangible business results and benefits.”[1] Those results agree with conclusions reached in an earlier survey conducted by Pricewaterhouse Coopers (PwC) and Iron Mountain. Sarah K. White (@sarahkwhite) reports, “According to [the] report entitled ‘How organizations can unlock value and insight from the information they hold,’ … companies have a lot of progress to make before they start making better use of the data.”[2]


Bean might sound like he is dissing big data initiatives; but, that’s not the case. “I have been a strong proponent of Big Data,” he writes, “making the case that Big Data approaches are transforming business processes, by putting data into the hands of business decision-makers sooner, by creating more agile environments that are conducive to discovery and rapid learning, and by eliminating obstacles to bringing new products and services to market quickly.” Richard Petley, director of PwC Risk and Assurance, agrees that the problem isn’t the data. “Data is the lifeblood of the digital economy,” he told White, “it can give insight, inform decisions and deepen relationships. It can be bought, sold, shared and even stolen — all things that suggest that data has value. Yet when we conducted our research very few organizations can attribute a value and, more concerning, many do not yet have the capabilities we would expect to manage, protect and extract that value.” What’s their point? Should or should not businesses pursue big data initiatives? Bean argues that businesses need a clear and obtainable vision of what big data can do for them. He notes a few innovative companies have done well with big data initiatives, like American Express, Capital One, General Electric, and JP Morgan Chase, but he argues, “Most firms remain far behind these early innovators, in both the clarity and evolution of their thinking, as well as their plans for execution.” The PwC/Iron Mountain report supports that conclusion. “The study found that while 75 percent of business leaders from companies of all sizes, locations and sectors feel they’re ‘making the most of their information assets,’ in reality, only 4 percent are set up for success.”


Business leaders obviously understand the potential of big data — which is why they are spending so much money on big data initiatives — but, as Bradley Maule-ffinch (@BradleyMauleffi) explains, “As most organizations have discovered, [the] volume of unstructured information is multiplying more rapidly than anyone could have predicted, and while the potential for using the resulting analytics to raise productivity and improve decision making is huge, extracting real business value from such a deluge of data is no mean feat.”[3] In order to extract business value from the growing ocean of data being generated, Bean believes the time has come “for firms to think well outside the box, with an eye deep into the future.” Aidan Russell (@RobotIRus) believes one of two scenarios will characterize that future. Scenario one will see “the data we gather from smart sensors, wearables, and the Internet of things is going to continue to grow. With that, new technologies are going to develop around it.” Scenario two sees “big data becom[ing] a relic as there is always a new trend to come out, like the cognitive technology.”[4] Frankly, I don’t see those as two different scenarios. You cannot separate cognitive computing and data. As President and CEO of cognitive computing firm, I believe that cognitive technologies are so adaptable they will become the foundation upon which digital enterprises will be built. Russell interviewed top experts in the field to see what they had to say about the future of the big data. He combined their views into five predictions.


Prediction One: Big data and analytics continue to grow. “All experts agree that we are going to continue generating larger volumes of data. If we take into consideration the number of mobile devices and the IoT devices, the big data is going to grow to unprecedented levels. As the data grows, also the ways to analyse the data are expected to grow and improve. Spark, SQL, and many new tools for analysis are going to emerge and improve. Machine learning, the cloud and the smart appliances are driving the next wave of big data.”


Prediction Two: Actionable insights are the gold nuggets to be mined. “Having insights into the data is the way of going forward if you want to win in the big data market. Being able to access, use the data and make decisions in real time is paramount. There is a gap between insight and action in big data at the moment. However, by the end of 2016, this gap is going to close and all the spent energy around the big data collection is going to redirect towards insights and execution. ‘Actionable’ or ‘fast’ data is going to replace the big data. Big is not better when it comes to data. Moreover, most businesses do not use a fraction of the data they collect. Instead, thanks to A.I., the companies are going to focus on asking the right questions for the best ‘actionable’ data.” For more on this topic, read my article entitled “Think Fast: The Importance of Time-to-Insight in Big Data Analytics.”


Prediction Three: Machine learning cleans big data. According to Gartner, machine learning is a top trend for 2016. Machine learning is the key element for data preparation and predictive analysis in the businesses of tomorrow. The next step in ‘big data’ is to create a link between cognitive computing and data analytics. … The growing volume of data sources and their complexity of information makes manual analysis uneconomic and infeasible. Thanks to [machine learning] we move beyond classic computing and information management and create systems that can self-learn. Systems able to perceive the world, without human help.”


Prediction Four: No data, no business. “‘By 2020, all businesses are data businesses or no businesses at all.’ More and more companies are going to drive value and revenue from their data. All businesses using ‘correct data’ are going to see $430 billion in productivity benefits over the competition not using data. Businesses are going to purchase algorithms rather than program them and add their data.”


Prediction Five: Privacy and security are bumps in the road. “Big data faces major challenges around privacy. The new privacy regulation from the European Union forces companies to address their privacy controls and procedures. By 2018, 50% of the business ethics violations are going to relate to data.”


Russell concludes, “Big data is only going to get bigger. More important, the companies that ignore the big data are going to be left behind, to a stopping point.” Bean adds, “Firms need to begin to embrace data as an essential corporate asset and organize business processes around the flow of data through a business, from production through consumption. The pace of business transformation will continue to accelerate, and data will be a driving factor.” Cognitive computing systems will be the catalyst and glue that help companies transform from industrial age organizations to digital enterprises.


[1] Randy Bean, “For Big Data, It’s ‘Show Me The Money’ Time,” Forbes, 29 March 2016.
[2] Sarah K. White, “Study reveals that most companies are failing at big data,” CIO, 10 November 2015.
[3] Bradley Maule-ffinch, “Extracting real business value from Big Data,” ITProPortal, 9 May 2015.
[4] Aidan Russell, “The Future Of Big Data In 5 Predictions,” WT VOX, 28 March 2016.

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