By Dr. Emily Plachy and Maureen Fitzgerald Norton
Like many of our clients, IBM is focused on becoming a smarter enterprise by connecting people, processes, and data. A smarter enterprise makes decisions differently, creates value differently, and delivers value differently — and it all starts with the creative application of analytics.
In conversations with our clients about this transformation, they often ask how IBM first approached analytics and the strategy behind it. Where should we get started? What advice would you offer from your experience so far? What are you doing inside IBM now?
That was the impetus behind “Analytics Across the Enterprise,” a new book authored by us and fellow IBM analytics practitioner, Dr. Brenda Dietrich, that details how IBM realizes business value from Big Data and analytics.
It covers how IBM moved aggressively the past several years to build breakthrough analytics capabilities by hiring talent, developing software, and acquiring firms with analytics tools that enhance our portfolio.
The book shares many lessons learned from an IBM perspective, but there are three key findings from our research that really stand out.
You don’t have to understand analytics technology to derive value from it.
Organizations can use a proven solution and get value without understanding exactly how the analytics method works within it. You need to learn how to use it effectively, but it’s not necessary to understand the inner workings in order to apply analytics to business decisions. Just as the user of a car navigation system doesn’t need to understand the details of the routing algorithm, the analytics user doesn’t have to understand the details of the math. Some users will want to understand the algorithms and inner workings of an analytics model in order to trust the results prior to adoption, but they’re the exception.
Doing things fast is almost always better than doing things perfectly.
Often inexact but fast approaches produce big gains because they result in better choices than people would have made without the use of analytics. Over time of course, the approximate analytics methods can be refined and improved to get additional gains. However, for many business processes, there is eventually a point of diminishing returns — the calculations may become more detailed and precise, but the end results are no more accurate or valuable.
Relationships inferred from data today may not be present in data collected tomorrow.
The relationships that you infer from data about the past do not necessarily hold in data that you collect tomorrow. You can’t analyze data once and then make decisions forever based on old analysis. It’s important to continually analyze data to verify that previously detected relationships are still valid and to discover new ones. Social media sentiment, in particular, has a much shorter half-life than most data.
The book urges readers not to give up on driving an analytics culture, because, quite simply, analytics works. Several studies have highlighted the value of analytics. Companies that use predictive analytics are outperforming those that do not by a factor of five. In a joint survey by the IBM Institute of Business Value and the SaidBusinessSchool 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. The bottom line is that analytics helps the bottom line.
Dr. Emily Plachy is an IBM Distinguished Engineer, Business Analytics Transformation; Maureen Fitzgerald Norton, is Distinguished Market Intelligence Professional, Business Analytics Transformation