“Getting a greater understanding of your business is the promise of big data,” Michael Hoskins (@MikeHSays), Chief Technical Officer at Actian, told participants at the Telco Cloud Forum earlier this year. “You can see things which you never were able to before, and it’s taking business opportunities to the next generation. … It enables not only for you to understand what you are doing within your business, but the industry on the whole. You gain insight into areas which you never perceived before.” Hoskins chose the term “promise of big data” because he believes too many companies have yet to leverage advanced analytics to unlock that promise. He explains:
“The data explosion which is hitting us is so violent, it’s disrupting the industry. It’s like two continents splitting apart. On one continent we have the traditional tools, and on the other we have the new breed of advanced analytics software. The new tools are drifting away from the traditional, and the companies who are using the traditional are being left behind.”
No business executive wants to see his company left behind. Yet Lauren Horwitz (@lhorwitz) reports, “Companies are struggling to use data analytics intelligently.”
The Importance of Big Data and Advanced Analytics
“Data analytics is no longer a nice-to-have,” Horwitz asserts. “Rather, it’s a requirement for understanding how customers use products and services, capitalizing on their known preferences and determining how prospect behavior can be converted into sales opportunities.” She continues:
“Given the massive influx of data associated with serving customers, companies can get easily overwhelmed by these flows of unfiltered data — especially in real time. Companies are also struggling to stitch together data inflows from multiple communication channels and devices — such as customer browsing activities on the web, email and live chat conversations, and social media comments — and get a 360-degree view of the customer.”
Every challenge she mentions — big data volume, variety, velocity, and veracity — can be met using the capabilities of cognitive computing platforms. Because cognitive computing systems use natural language processing, they can gather and integrate both structured and unstructured data. Because they work at computer speed, they can keep up with both the volume of big data and with the speed of today’s business environment. As the Internet of Things (IoT) matures, all of these challenges are going to increase.
Just because a company can collect and store a lot data doesn’t mean it is using that data wisely (or even collecting and storing the right data). David Weldon (@DWeldon646) observes, “Many organizations complain that they aren’t achieving the success with big data projects they hoped for.” In an interview with Weldon, Tara Prakriya, chief product officer at Maana, explained why asking the right questions and collecting the right data to answer those questions is important. At Maana, she told Weldon, analysts “create a knowledge model that is in the form of a precise question and develop a model to answer. We then back into appropriate data within what exists in the enterprise and NO more. When getting the data, we are more surgical about the data we are looking at and only use data we need.” Maana is fortunate it can use only data under its control. In many instances, companies must collect and analyze data beyond their control in order to answer specific business questions. In either case, Prakriya’s primary point remains valid — a good business case must be made for any big data project.
As Davies noted, leveraging advanced analytics is critical in the big data era. He explains, “The sheer volume, variety and speed at which data is being collected means traditional technologies to analyze this data are being made redundant. … They’re too slow (they can’t keep up with the velocity of collection), they’re too rigid (they can’t comprehend the variety of data sets), and they’re too cumbersome (they can’t manage the sheer volume of data). In short, these tools are straining under the swell.” Deloitte analysts agree with this assessment. “With data-driven insights informing more and more strategic decisions,” they write, “companies across sectors are investing heavily in analytics, artificial intelligence-powered tools like cognitive computing and machine learning, and data-mining capabilities. Indeed, in a business climate where data has become the new currency, analytics tools are helping companies extract value from this currency. Likewise, cognitive computing and machine learning are serving as disruptive forces driving business model and data management transformations.”
Steps to Implement a Winning Big Data Strategy
Prakriya told Weldon, “There is a clear link between a knowledge model that provides answers to the right questions and intelligence decisions that move the needle.” Clearly, the first step in implementing a winning big data strategy is to ask the right questions. Good answers (or solutions) always start with good questions. Deloitte analysts call this “understanding the business” analytics agenda and priorities. They provide a couple of questions that might help start this process. Do you want more customer insights, or are you looking for insight into operational efficiency? What specific insights will help you move the performance needle?
The next step according to Deloitte is to experiment with pilot projects. At Enterra Solutions®, we recommend clients use a crawl, walk, run approach. This approach allows a company to assess quickly whether they are asking the right questions, taking the right approach, and getting the right answers. This approach allows necessary tinkering with solutions before they are scaled. Deloitte analysts assert, “Data-driven insights are only as powerful as an audience’s ability to understand and leverage them in decision-making. … [It is] important to focus on delivering data insights in natural language users understand and in formats they can access and utilize.”
A good strategy is never a static strategy. Deloitte analysts recommend companies embrace a culture of continual innovation. They explain, “The speed at which data is being generated is only accelerating. As a result, the ability to quickly generate and act upon data-driven insights has become table stakes for remaining competitive. Keeping pace in this environment likely requires organizations and their CIOs to embrace current technology disruptors such as machine learning and cognitive computing and to keep their eyes trained on the horizon for new innovations that may soon emerge.”
Deloitte analysts conclude, “Realizing analytics’ full potential — and maximizing ROI on analytics investments — requires nothing short of an orchestrated cultural shift across the enterprise in which all stakeholders begin focusing relentlessly on data, insight generation, and the critical role both play in achieving an insight-driven advantage (IDA). Simply put, IDA is the systematic use of analytics throughout an organization to deliver a competitive advantage in the marketplace.” Hoskins adds, “The journey of modernization goes from traditional, linear tools, through to business intelligence and discovery, this is where we are now, through to decision science. Traditional tools enable us to look back at what we’ve done and make reactive decisions, but businesses now want to have a forward looking analytics model, drawing out new insights to inform decision making. But this cannot be done with traditional tools. This is the promise of advanced analytics. The final stage is where we can use data analytics to inform business decisions; this is where data becomes intelligence.”
 Jamie Davies, “Can your analytics tools meet the demands of the big data era?” Business Cloud News, 12 May 2016.
 Lauren Horwitz, “Companies still struggle to unlock customer data analytics insight,” TechTarget, 21 September 2016.
 David Weldon, “Success With Big Data Starts With Asking the Right Questions,” Information Management, 19 October 2016.
 Benjamin Stiller, Nitin Mittal, and David Rudini, “Build Insight-Driven Advantage With Analytics,” The Wall Street Journal, 13 October 2016.