Big Data Analytics Promise Big Insights

Stephen DeAngelis

February 14, 2014

“Companies are getting more data,” writes, Michael Fitzgerald, “in fact, the typical company doubles the amount of data it stores every two years. But more data isn’t necessarily a good thing. Data, after all, is inherently dumb. Getting more of it often seems to compound its lack of intellect.” [“Turning Big Data Into Smart Data,” MIT Sloan Management Review, 2 December 2013] The last thing that organizations want is to become Big Data dummies. Fitzgerald reports that Jeanne Ross, director and principal research scientist at MIT’s Center for Information Systems Research, told a gathering of executives that it costs a lot of money to collect and store data. And what do organizations get from all that data? Ross told the executives that a lot of organizations only end up with security and reputation risks as well as security breaches that leave them in compromising legal positions. She told them that they should strive to foster a data-smart culture “that routinely handles data in an intelligent way [and] demands evidence-based management.” She indicated that a data-smart culture “builds on the disciplined process culture, but instead of being centralized, decision-making gets pushed out to the ranks.” Ross told the executives that to make such a culture work, “companies need to incorporate several key practices.” They are:

  • Establish a single source of truth. People need to know what’s expected of them, and how they will be measured. It sounds simple, but it can be tricky; Ross cited a case where, after a year where Aetna had lost money, every business unit head had spreadsheets showing that their part of the business made money. Each unit had their own financial truths, which they defined — even when their definitions didn’t hold up against the overarching truth of the company’s losses.
  • Use scorecards. … Ross likes scorecards, especially daily ones, as a way to motivate employees across the board — with the caveat that scorecards don’t work well if they contain too many measures. …
  • Create ownership of business rules. Businesses run by rules, Ross said. For example, Allstate Insurance had a rule that it must wait 30 days to pay out a policy when a car was stolen. The company realized this policy was costing it customer goodwill and a lot in rental car fees. … Making … changes in the business means that someone must ‘own the process’ of business rules, and be able to set new rules.
  • Cultivate your talent. Companies also must develop their people, coaching them to work effectively with data and giving them regular feedback (with or without a scorecard). Key to this development is having managers act almost as coaches, engaging consistently in one-on-one interactions to help each employee perform better.

Even if you establish a data-smart culture, you have to decide what insights you want to obtain from the data. Ben Kerschberg reminds us, “Not all data is useful.” [“Five Steps To Master Big Data and Predictive Analytics in 2014,” Forbes, 3 January 2014] Data is made useful through analysis. Bernard Marr writes, “Analytics is something any manager, leader or in fact anyone should know about. Not only because analytics is one of the biggest buzzwords around at the moment but because it will be a game changer in all aspects of life.” [“What The Heck Is… Analytics?LinkedIn, 24 June 2013] He continues:

“In today’s data-driven world analytics changes everything, not just in business, but also in fields like sports, healthcare and government. It is hard to think of any aspect of life that won’t be affected by analytics. … So, what really is analytics? Basically, analytics refers to our ability to collect and use data to generate insights that inform fact-based decision-making. Advances in information technology and a complete datafication of our world now mean we have (or will have very soon) data and insights on everything. … We are not only generating vastly more data but our ability to harness and analyze this data has improved massively over recent years. We can now analyze large volumes of fast moving data from different data sources to gain insights that were never possible before. Analyzing large and messy data sets is often referred to as ‘Big Data’ or ‘Big Data Analytics’, which have become buzz words in their own right. Different types of analytics approaches allow us to analyze numbers, text, photos and even voice and video sequences.”

Some of the most useful analysis, Kerschberg insists, involves the use of inference. “Inferences transform data into knowledge,” he writes, “which results in greater process transparency and improvements.” He adds, “When evaluating the need to institute analytics as part of your data strategy, it is important to remember that actionable knowledge is not inherent in data per se; rather, it must be extracted upon established rules and algorithms.” Another type of analytics that is receiving a lot of attention is predictive analytics. For example, an article in Consumer Goods Technology (CGT) states, “Predictive analytics make it possible for consumer goods companies to forecast customer behavior, potential risks, product associations and demand.” [“New Possibilities Using Predictive Analytics,” 29 October 2013]

Forrest Anderson agrees that “the power of big data is really in analytics.” [“It’s Not Big Data, It’s Analytics,” Research Conversations, 4 October 2013] He agrees with Ross that companies need to foster a data-smart culture or, as he calls it, a “Big Data Mindset.” He cites an article by Florian Zettelmeyer, a professor of marketing at the Kellogg School of Management, in which he indicates that a Big Data Mindset encompasses four elements. They are:

1. Designing marketing processes with data in mind
2. Engaging in research and development everywhere
3. Using predictive analytics
4. Challenging conventional wisdom

Anderson writes, “Notice he does not mention ‘Big Data’ itself.” Analysts at Tibco Spotfire agree that “one of the biggest challenges many enterprises face is trying to glean insights from big data.” [“Breaking Through Silos with Big Data Analytics,” Trends and Outliers, 12 August 2013] As the title of their article notes, too often the data is “trapped in the data silos that exist across business units and organizational functions. … Data silos create a number of barriers that impede decision making and organizational performance.” I love the Aetna example provided by Ross that demonstrated how data silos can harmful. To learn more about challenges associated with business silos, read my post entitled The Curse of Silo Thinking. To get the most out of Big Data, data must be integrated. Even though it is an essential task, it is not an easy one. The Tibco analysts write, “For decision makers to make informed decisions, they need the full range of customer, operational, and market information that’s available to them. This includes commonly-used structured data (data contained in customer databases) and unstructured data (email, social media posts, audio, video).”

Even after data has been integrated, its usefulness is limited unless everyone who needs access to it can access it. Fortunately, good analytic systems embed analytics so that they are available to everyone who needs them. Analysts from Choice Logistics notes, “The notion of embedded analytics is more about the way in which data is presented. It’s a foundational practice, which enables various business units to access a meaningful, standardized set of metrics, based on information that’s accessible when they need it. The goal; smarter business decisions faster, empowering the user.” [“The Power of Embedded Analytics,” SupplyChainBrain, 19 December 2013] The article continues:

“Embedded analytics are more than a piece of plug-in software. As with any significant technology effort, the tool must be accompanied by business-process change. Every stakeholder, both inside and outside the organization, has to get involved. Proper training, backed by top management’s ongoing commitment of time and funding, is key. To be effective, analytics must become an organic component of corporate strategy and culture.”

A number of pundits would love to eliminate the term Big Data from our vocabulary. They claim the name doesn’t emphasize the right thing — the analytics. Although I agree with the sentiment (I prefer the term cognitive computing), the buzzword is going to be around a long time. Data (big or small) is only useful if it yields actionable intelligence and insights. Analytics are the tools used to refine data into gold.