This chapter is from the book
This chapter is from the book
This chapter is from the book
Why Analytics Matters
- “People respond to facts. Rational people will make rational decisions if you present them with the right data.”
- —Linda Sanford, Senior Vice President, Enterprise Transformation, IBM Corporation
Quite simply, analytics matters because it works. You can be overwhelmed with data and the value of it may be unattainable until you apply analytics to create the insights. Human brains were not built to process the amounts of data that are today being generated through social media, sensors, and more. While gut instinct is often the basis for decisions, analytically informed intuition is what wins going forward.
Several studies have highlighted the value of analytics. Companies that use predictive analytics are outperforming those that do not by a factor of five.12 In a 2012 joint survey by the IBM Institute of Business Value and the Said Business School at the University of Oxford of more than 1,000 professionals around the world, 63% of respondents reported that the use of information (including big data and analytics) is creating a competitive advantage for their organizations.13 IBM depends on analytics to meet its business objectives and provide shareholder value. The bottom line is that analytics helps the bottom line. Your competition will not be waiting to take advantage of the new insights from big data. Should you?
IBM has approached the use of analytics with a spirit of innovation and a belief that analytics will illuminate insights in data that can help improve outcomes. The company hasn’t been afraid to make mistakes or redesign programs that haven’t worked as planned. Unlike traditional IT projects, most analytics projects are exploratory. For example, the Development Expense Baseline Project explored innovative ways to determine development expense at a detailed level, thereby addressing a problem that many thought was impossible to solve. IBM analytic teams haven’t waited for perfect data to get started; rather, they have refined and improved their data along the way. For example, the Coverage Optimization with Profitability project team described in Chapter 9, “Increasing Sales Performance,” knew it had incomplete data, but rather than wait for the various data stewards to improve their data, the team jumped in and made progress incrementally on data governance and data cleansing. Using this approach will reduce your time to value. Using this approach will reduce your time to value. The key is to put a stake in the ground with a commitment that analytics will be woven into your strategy. That’s how IBM does it. This approach is also effective with big data. Rather than postpone the leveraging of big data, you should embrace it, establish a link between your business priorities and your information agenda, and apply analytics to become a smarter enterprise.