The Future of Analytics: What is All the Hype About?

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Expert’s ears immediately perk up upon attending a talk about how to get big insights from analytics. After all, analytics has become the new hot topic of the day.  However, asked Nipa Basu, during her keynote presentation The Future of Analytics at the DATAVERSITY Enterprise Analytics Online Conference: “What is the difference between hype and reality in analytics? What is really possible?”

As the Chief Analytics Officer for Dun & Bradstreet, an engineer of a micro-simulation model of the US economy, and an expert in the analytics space, Nipa Basu tackled what constitutes as useful insights, for business customers, that are derived from analytics.

Business leaders want to know. According to a market survey, conducted by Dun & Bradstreet and Forbes, 70 percent of business leaders say they use analytics often to make decisions. “Analytics is going mainstream,” Basu said. “There is a high level of adoption.”   She was amazed to recently hear medical professionals presenting results of improved kid’s survival rates from cancer. She notes that this is amazing application of Data Science and analytics today.

But, high prospects in analytics has drawbacks. She knows of analytical practitioners “who have been hired by different companies with the huge expectation that Analytics will do magic for” these businesses. However, these types of organizations do “not always [realize] the kind of investment in human capital and in infrastructure needed,” she noted. Basu mused to all the hype, “how do we keep the focus on analytics?” First, she said, “we are in the middle of a revolution.”

The Analytics Revolution

Before the Analytics Revolution, Basu noted, practitioners asked: “How do you draw robust insight from a small sample?” She added from her past experiences, “the sample was always small because data was always scarce. Computers were not very powerful. That problem does not exist anymore with our ability to crunch data at an incredible speed, after the Big Data revolution. There is now too much data. Today we can do a lot of things, but does big data always translate into big insights? In some cases big data has really become lots of data but not necessarily lots of meaningful insights.”

In describing analytics, she defined it as “numbers on a spreadsheet and drawing a trend line,” but added that analytics has moved onto an advanced form through “different forms of artificial intelligence.”

She included any techniques that enable computers to mimic human intelligence, using logic, if-then rules, decision trees, and machine learning. Added to that list are deep learning, and algorithms that permit software to train itself to perform tasks, such as various other machine learning (ML) statistical techniques enabling machines to improve at tasks with experience.   She believes that these open up a whole new range of possibilities.

Basu noted, “we are able to build models of predictive lift” that compete with traditional statistical models from more experienced modelers.” machine learning, she described, advances the established decision trees or score cards to multi-layer models. Through ML we “feed data to a computer and the computer creates a generic algorithm. Machine learning models usually involve adaptive learning. She explained that it is “opening up lots of new of opportunities.” She also asked: “Where is machine learning more appropriate than [in] other approaches?”

Real Machine Learning Use Cases

Basu believes machine learning to be more applicable in same situations than others. Dun & Bradstreet (and Basu) consistently use machine learning for fraud prevention. She explained that ML’s adaptive learning plays a huge role because “fraudsters are actually trying to beat the systems.” So as soon as the model is in production, as soon as they see that whatever they were doing before is not going through anymore.

Basu demonstrated that machines are good at tasks like fraud, because nobody “argues about this definition and the outcome variable is fixed and there is not a whole lot of scope of debate about the outcome variable.” Failure scores and Global Business Rankings according to Basu, also lend well to ML models. Failure scores are well defined variables that are similar across all business customer’s applications. Global Business rankings have numerous segments a very different data coverage. But some use cases do not lend themselves to machine learning.

Basu said that machine learning:

“Is really fitting the line to all the nooks and crannies of the data. There is a risk of over Fitting. [The] more flexibility there is in changing the outcome variable or changing what we are trying to predict or explain, the more is the risk of the model not holding up in a different situation.”

For this reason, delinquency and procurement scores do not lend themselves to machine learning, Basu believes. She noted:

“Delinquency is in the eyes of the beholder. For some customers, they need to get paid in the next 15 days that’s their business model, if they don’t, they will be in big trouble. For some customers 30 days terms are pretty good, so the definition is different, how its defined is different. “

In analyzing procurement scores she said, “Procurement managers need more transparency, they need to explain these decisions to do themselves, their superiors and their suppliers” making procurement scores more fluid.

The use cases above show only one aspect of analytics. But, how should companies make use of the Analytics Revolution in other areas?

An “Analytics Focus”

Basu noted that analytics leaders get excited “by the newer techniques.” As a result, she believes, the focus goes “on the techniques rather than the business questions.” She added “integral components” to new kinds of analytics, using machine learning, are becoming the norm. More specifically these include:

  • Customization or configuration
  • Real-time delivery
  • Automation

The Analytics Revolution has provided many of the benefits above. According to Basu, this is part of what we can do. But, “the kind of analytics that is relevant for customers comes through acceptance by the business users.” She emphasized that user groups, outside of the analytics practitioners “need to adopt it and understand it.” This is tricky.

She mused that the revolution in analytics is enabled by a revolution in technology. The value of analytics-as-a-service, as well as self-service analytics through accessing some advanced tools and external data, is becoming more and more popular.

“Whenever I’m involved in a discussion in analytics-as-a-service, the discussion invariably goes to the tech stack as if that’s what it is. Now, the tech stack is an enabler, it’s the platform on which you are performing analytics but there’s lot more to it, there is the need to identify the right kind of analytics to solve the right kind of business problem.”

She said that there are different companies hiring analytics practitioners with a huge expectation that analytics will do magic for those companies without realizing the kind of investment needed in human capital, infrastructure, hardware and software that would be needed.

Basu challenged her audience and analytics practitioners to answer this question “What is our responsibility to keep the organization’s focus on true analytics?“ She believes that analytics practitioners need to make sure, on a regular basis, to do business reviews, go to recent analytics talks about where, when, and how the insights were implemented in changing business processes. Then, of course, analytics professionals need to measure and thus manage that whole process, among coming up with an unbiased testing strategy and influencing skills.

She ended by telling the audience to “focus more on analytics and less on the hype surrounding it with the things that can be done but not necessarily should be done.”

Want to learn more about DATAVERSITY’s upcoming events? Check out our current lineup of online and face-to-face conferences here.

Here is the video of the Enterprise Analytics Online Presentation:

Photo Credit: Molnia/

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