Speed To Insight: Key To Big Data Success

Businesses don't have to wait long to see analytic investments pay off. Big data can deliver big returns within months, not years.

Shanker Ramamurthy, Global Managing Partner, Business Analytics & Strategy, IBM

January 5, 2015

4 Min Read

CES 2015 Preview: 8 Hot Trends

CES 2015 Preview: 8 Hot Trends

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Benjamin Franklin had it right when he said an investment in knowledge pays the best interest.

Knowledge derived from big data has helped us accurately understand everything from the weather to traffic. In the process, big data analysis has touched many aspects of our lives, such as how medical treatments are prescribed. Not all big data projects are created equal, though. According to a recent report, speed is a key factor in the success of any analytics initiative.

When raw data is quickly converted into useful insights, businesses see the most dramatic results. In fact, according to the annual IBM Institute for Business Value study, among the 1,000 executives surveyed from nearly 70 countries, 63% say they've seen a positive return on analytic investments within one year. Among executives who say they are from "speed-driven organizations" -- where data analyses lead to action in as little time as possible -- 69% say they have seen significant positive results from their data analytics efforts over the past three years.

[Want more on big data insight? Read Analytics Showdown: Should Apps Be Simpler, Or Smarter?]

Businesses that emphasize speed are more likely to have agile cultures in place that are receptive to change, particularly changes that are driven by data. Company policies, practices, and procedures can be shaped by or even conceived through data analytics. Every department should both support and be supported by data analytics. Human resources, for example, could develop unique algorithms to identify undercompensated or overcompensated employees; marketing executives could follow sales and demographic data to precisely target expansion regions; and so on. When data analytics becomes ubiquitous, business results improve.

Businesses with successful analytics initiatives are more likely to structure or analyze information in unconventional ways in order to identify valuable insights. Not too long ago, if businesses used analytic tools at all, they were likely trying to find answers to preset questions, such as the percentage of sales from different regions. Today the most effective analytic initiatives uncover information that nobody knew to ask about.

For example, WellPoint, a health benefit company, found that it could take up to 72 hours to respond to a doctor's urgent request for coverage of a treatment. In an effort to slash that time, the company implemented a system that quickly correlates clinical research, patient data, and clinical practice guidelines to expedite the pre-approval process on doctors' requests. The new system generates hypotheses and uses evidence-based learning to score recommendations on a confidence scale so nurses are given the best options for each patient in a matter of seconds, not hours or days. By shortening the approval time for treatments, WellPoint is reducing costs, improving service, and, most importantly, singling out the most appropriate treatment for each patient.

There is immense pressure to uncover valuable information from the oceans of data available today. Roughly 74% of respondents to the Institute for Business Value survey say that executives will expect new data-driven insights at an accelerating pace over the next 12 to 18 months. It helps to have the right staff in place. In particular, an effective chief data officer (CDO) and a hungry team of data scientists can introduce analytics to the uninitiated.

CDOs should have the business savvy to be able to identify new markets, pricing structures, or products while being technically competent enough to know how data should be structured and shared. Hiring a capable CDO doesn't solve all big data problems, though. The entire organization -- from the C-suite to people working in the field -- needs to be on board with data-driven decision-making; otherwise any analytics initiative may be doomed from the start.

Big data and analytics are often used to improve customer relationships and find more customers. Roughly 40% of executives surveyed said they are using data analytics to achieve operational goals, such as lowering expenses and optimizing the use of existing resources.

Big data analytics may be a fairly young field, but it's morphing at a rapid pace. Just a year ago, businesses were most concerned about managing overwhelming volumes of data. Today the priority is managing velocity. Real benefits are being realized by businesses that are nimble enough to act fast and that are data-centric enough to see opportunities hiding in oceans of information.

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About the Author(s)

Shanker Ramamurthy

Global Managing Partner, Business Analytics & Strategy, IBM

Shanker Ramamurthy is the Global Managing Partner for Business Analytics and Strategy in IBM's Global Business Services business. In his more than 25 years as a consultant and business executive in more than 30 countries across six continents, he has worked on the intersection of strategy, information technology, big data, and analytics.

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