Next-Gen Analytics Helps Spur Business Success

By Samuel Greengard  |  Posted 2014-07-31
next-generation analytics

The complexities of today's business environment haven't been lost on most IT executives. In an attempt to make sense of a growing stream of data, analytics has landed at the center of the enterprise. Understanding relationships, interrelationships and behavior can determine whether an organization soars or stumbles into the digital age.

"There's a growing recognition that all the buzz about big data is really about analytics, and it's necessary to take things to an entirely different level," says Scott Schlesinger, senior vice president and head of North America Business Information Management at Capgemini.

Only a few years ago, most organizations focused primarily on extracting data for reports and more basic business intelligence (BI). The ability to sort through larger volumes of data in more sophisticated ways eventually morphed into analytics.

However, the ability to move beyond examining data from a single stream or source is evolving into a need to view a complex array of streams, including data from an array of structured databases and unstructured sources, such as video, audio, social media, email, IM streams and documents. As Bill Briggs, chief technology officer at Deloitte Consulting explains: "In today's extremely disruptive environment, analytics is increasingly the key to business success."

How can organization navigate this environment? What's required to transition from Analytics 1.0 to Analytics 2.0? And what are the key pivot points for adopting a next-gen analytics framework?

Although there are no simple answers and the environment is changing at warp speed, it's clear that business and IT leaders must take a different—and more holistic—view of the enterprise and data. As the volume of data grows and new sources, such as the Internet of things, enter the picture, the task becomes more challenging and more critical to success. There's a need to examine and re-examine many facets of a business and look at data science in a different way.

Obtaining Business Value

The benefits and challenges of next-generation analytics haven't gone unnoticed. A 2013 Bain & Company research report indicates just how difficult it is to transform big data into analytics success—or, more specifically, better performance and higher profits. The consulting firm surveyed over 400 companies and found that while 38 percent of organizations have made progress installing big data systems and using more sophisticated analytics, less than 5 percent have the right combination of people, tools, data and intent to obtain real business value from their efforts.

A starting point for navigating next-gen analytics is to recognize that it's about introducing new tools and methodologies that dive into broader data sets in a deeper way. The volume of data ricocheting through the world has reached an astounding level.

According to IBM, people and machines generate 2.5 quintillion bytes of data every day. Remarkably, 90 percent of the world's data has been created during the last two years. Increasingly, this deluge includes GPS and geolocation data, information collected from mobile devices, and machine-generated data from sensors, beacons, RFID chips and more.

It's easy to get overwhelmed by the enormity of the analytics challenge, but Capgemini's Schlesinger offers some advice: Focus on the value analytics brings to the business. In addition to building out more robust big data capabilities and more advanced analytics tools, there's a need to focus on data science. "Moving forward, there is a need for people who understand business and data in a conceptually different way than in the past," he says.

In some cases, this means hiring data scientists and others who can provide greater insights into the business. In other situations, organizations require outside help using analytics as a service or data science as a service. The environment requires organizations to break down organizational silos, think more creatively, form new partnerships based on data needs and requirements, and tap outside help, Schlesinger says.

Turning to Next-Gen Analytics

One organization that is using next-gen analytics successfully is The Flint River Partnership, which helps farmers in the Lower Flint River Basin of Georgia make more informed decisions about irrigation, conservation and crop yields. The basin spans 27 counties and encompasses more than $2 billion in farm revenue annually.

The partnership—which includes the Flint River Soil and Water Conservation District, USDA's Natural Resources Conservation Service, the Nature Conservancy, the University of Georgia and IBM—has turned to IBM's Deep Thunder technology to generate more precise weather forecasts and push them out to mobile devices, including smartphones and tablets. This is significant because the U.S. Department of Agriculture estimates that 90 percent of crop losses are weather-related.

The analytics tool draws on public data from the National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). It combines this data with historical records from Florida, Georgia and the WeatherBug network, as well as data from agricultural sensors, wind farms and weather sensors.

Deep Thunder provides a 3D telescoping grid to break down data—and, ultimately, forecasts—into smaller grids that can be used on a local basis. The approach has led to predicting snowfall accumulations in New York City and rainfall levels in Rio de Janeiro with upward of 90 percent accuracy.

The analytics tool "provides access to a service previously unavailable to regional producers," says Casey Cox, conservation coordinator for Flint River Soil and Water Conservation District. "Farmers and researchers are currently utilizing Deep Thunder to make decisions in the field that are dependent upon weather variables.

"Irrigation is crucial to production in the Lower Flint River Basin, and having access to a sophisticated, site-specific weather model has provided more certainty in the decision-making process. Farmers are able to optimize water use by evaluating weather patterns and conditions up to 72 hours in advance."

Analytics Everywhere

Next-generation analytics ventures beyond understanding events, conditions and factors in a deeper and broader way. It's also about using data more proactively through predictive analytics and other tools.

In the marketing arena, this might translate into harnessing beacons and interactive shelves to understand how shoppers move through a store, provide them with information about products, and generate coupons and promotions dynamically, based on past purchase patterns or data residing in a loyalty program.

In manufacturing or health care, it might translate into understanding market trends in a deeper and broader way and gaining insights into the fast-changing dynamics of a supply chain or patient behavior.

To be sure, there is no shortage of potential uses for analytics 2.0 in business, education and government. Over the next decade, the concept will redefine a wide swath of industries—as well as the way people and companies interact.

"The reality," Capgemini's Schlesinger says, "is that [analytics 2.0] requires a different way of thinking and an ability to combine a growing number of data elements in a way that delivers maximum value. Next-generation analytics is not a new concept. It's simply part of an ongoing evolution of big data and analytics tools."