Next-Gen Analytics Helps Spur Business SuccessBy Samuel Greengard | Posted 2014-07-31 Print
Analytics combines data and data sources in new ways to help firms understand relationships that can determine whether they soar or stumble into the digital age.
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.
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