Big Data was supposed to help businesses solve many of their nagging problems; but, article after article over the past few years has more often than not discussed the failure of big data to produce expected results. Brandon Purcell, a Forrester Research analyst, believes part of the problem was that companies concentrated on gathering the data rather than using it. “Big data really focused on capturing massive amounts of data from multiple sources,” he states. “Companies got really good at that, but they’ve struggled to turn that data into insights and insight into action.”[1] The emergence of artificial intelligence (specifically the maturation of cognitive computing) could help replace disappointment with delight. Purcell explains, “The promise of AI is to complete that process — from data to insight to action — in a virtuous cycle that optimizes continuously.” According to Purcell, “41 percent of global firms are already investing in AI and another 20 percent are planning to invest in the next year.” Even though most companies are still trying to come to grips with big data, Shelly Blake-Plock (@BlakePlock), President and CEO at Yet Analytics, calls big data “old news.”[2] Purcell would probably agree with assessment. “AI has replaced ‘big data’ as the buzzword du jour,” he states, “but in my mind it actually has the ability to fulfill big data’s failed promise.” Blake-Plock cautions, however, “It turns out there’s no one answer for how to get value out of big data.”

Big Data Isn’t Going Away

Just because big data may be old news doesn’t mean it’s going away. David Weldon (@DWeldon646) reports an International Data Corp (IDC) study entitled “Data Age 2025,” concludes, “The amount of data created worldwide will increase by tenfold by 2025, and organizations will have to choose what portion of that data to manage, and how. Organizations that don’t manage this data deluge correctly could lose revenue, provide poor customer experiences, and suffer operational inefficiencies.”[3] The report predicts, “Data creation will swell to a total of 163 zettabytes (ZB) by 2025.” That’s a lot of data. The report goes on to note that disappointments of the past will probably be forgotten in the years ahead. The study concludes, “The decade centered around the conversion of analog data to digital is being replaced by an era focused on the value of data; creating, utilizing, and managing ‘life critical’ data necessary for the smooth running of daily life for consumers, governments and businesses alike. Consumers and businesses creating, sharing and accessing data between any device and the cloud will continue to grow well beyond previous expectations.”

Many analysts believe we are entering the Era of Cognitive Computing; but, cognitive computing platforms require data — and lots of it. Dave Reinsel, senior vice president at IDC, observes, “From autonomous cars to intelligent personal assistants, data is the lifeblood of a rapidly growing digital existence — opening up opportunities previously unimagined by businesses. Technology innovation will be vitally important to evaluate and fully activate the intricacies of what’s contained within this large volume of data — and storage in particular will continue to grow in importance, as it provides the foundation from which so many of these emerging technologies will be served.”[6] The IDC study concludes, “Business leaders will have the opportunity to embrace new and unique business opportunities powered by this wealth of data and the insight it provides but will also need to make strategic choices on data collection, utilization and location.”[7] IDC analysts hit the nail on the head when they combine a “wealth of data” with “insight.” Data, by itself, does not provide any insights. Only the analysis of data generates actionable insights and that is exactly why cognitive computing is coming to the forefront.

Big Data and Cognitive Computing

Manu Carricano (@mcarricano), an Associate Professor in Operations, Innovation and Data Science, and the Director of the Big Data Analytics Executive Program at ESADE Business School, agrees with other analysts that, to date, big data initiatives have, to date, been disappointing.[4] He cites a number of reasons for this, but he believes results can be achieved if companies see the “big picture.” Seeing the big picture, he explains, “is the underlying objective of the ‘Big Data & Analytics Canvas’ that we have developed over the last couple of years with organizations facing complex analytical transformation. The canvas is structured on 6 main blocks.” They are:

1. Business: formulation of Objectives; Actions to be taken (Deployment); Measured Impacts.

2. Data Integration: Data Sources; Enrichment; Data Architecture decisions.

3. Data Exploration: Discovery.

4. Data Visualization: at the center as it allows to quickly deliver results and scale up the number of users around the initiative.

5. Insight Generation: building an insight engine is key, and can be deployed around two dimensions: descriptive analytics (for many organizations, less is more) and predictive analytics.

6. Decision optimization: many initiatives focus on data science, but forget prescriptive methods rooted in OR. Prescriptive is the higher level of analytical maturity and where business impacts happen.

Cognitive computing is essential for many of those blocks (i.e., data integration, discovery, insight generation, and decision optimization). Randy Bean (@RandyBeanNVP), founder and CEO of NewVantage Partners, explains, “Now that many executives are finding measurable results from their Big Data initiatives, they are looking ahead and making decisions about investments in emerging capabilities such as artificial intelligence and machine learning.”[5] Blake-Plock agrees. He writes, “Modern big data systems that use AI and targeted, structured approaches are going to provide the kind of personalization and real-time insight that will set the standard for the next generation of data-driven decision making and automation.” Carricano acknowledges that a structured approach is essential. “The approach is simple,” he writes, “but its constant monitoring, in a Data-Driven ‘war room’ ensures a pragmatic overview of this complex transformational process, a clear communication to the key stakeholders (and particularly Top Management), and an efficient tracking on progress and results delivery.” Once the blocks are in place, companies can take the next step and transform themselves into digital enterprises.

Summary

Tushar Vijay (@CCopywriter), observes, “Developing technologies and new businesses demonstrate the power of big data and how it has come to define our lives in the modern world. From saving more lives to selling expensive stuff, it is big data which has been helping individuals, businesses and governments to find out new world solutions to problems. … The future is safe with big data, but we need to start finding new opportunities too. A lot of positive changes can break-open when we begin to explore more and plan big.”[8] A brighter future is not going to be the result of collecting more data rather it will be the result of properly analyzing the data that is collected. Cognitive computing platforms, like the Enterra Enterprise Cognitive System™ (ECS) — a system that can Sense, Think, Act, and Learn® — are essential for unlocking the nuggets buried deep in mountains of data. With the right analytics, big data initiatives result in delight rather than disappointment.

Footnotes
[1] David Weldon, “Artificial intelligence: fulfilling the failed promise of big data,” Information Management, 21 April 2017.
[2] Shelly Blake-Plock, “Where’s The Value In Big Data?” Forbes, 14 April 2017.
[3] David Weldon, “Data to see tenfold increase worldwide by 2025,” Information Management, 5 April 2017.
[4] Press Release, “Seagate Advises Global Business Leaders And Entrepreneurs To Sharpen Focus On Data Critical To The Success Of Global Business Impact,” Seagate, 3 April 2017.
[5] Ibid.
[6] Manu Carricano, “Big Data – Beyond the Hype,” IEDP, 19 January 2017.
[7] Randy Bean, “Companies Brace for Decade of Disruption From AI,” MIT Sloan Management Review, 24 January 2017.
[8] Tushar Vijay, “How Big Data is Changing the World Around Us,” Datafloq, 18 October 2016.