Analytics Across the Enterprise: How IBM Realizes Business Value from Big Data and Analytics is an excellent new book written by three pioneering IBM analytics practitioners: Brenda Dietrich, Emily Plachy, and Maureen Norton.
The book demystifies the big data and analytics journey by sharing invaluable real-world perspectives on what does and doesn’t work, and how you can start or accelerate the adoption of big data and analytics across your enterprise.
Using 31 case studies to show how IBM has derived value from big data and analytics throughout its business, the book provides an essential framework for how businesses of any size, and in any industry, can discover a better way to do business and become a smarter enterprise using analytics.
In this blog post, I will briefly highlight just three of the many key concepts that resonated with me while reading the book.
Business Value comes from Action, not Insight
Analytics is any mathematical or scientific method that augments data with the intent of providing new insight. Simply stated, analytics discover insights in data. However, the business value of analytics to the enterprise is not the insights they generate. Business value occurs when actions are taken based on insights. Analytics is a means, not an end. Analytics is a way of thinking that creates competitive advantage when data-driven insights are used to inform actions, such as fact-based decision making.
Taking Action requires Readiness for Change
A prerequisite for taking action on analytical insights is readiness for change. In organizations not ready for change, analytics remains a recreational activity. Strong executive management support is required to raise awareness of the possibilities that analytics offers—possibilities that are impossible if you don’t put analytics to work. The enterprise must be willing to change the way it works by incorporating analytics into its business processes. The organization must also be ready for the chain reaction caused by taking action on analytical insights. Action generates new data to analyze, which generates new insights (or casts previous insights in a new light), which requires readiness for more actions and changes. Analytics is a continuous feedback loop.
Waiting for Perfect Data is a Waste of Time and Value
Yes, big data is messy. But does that mean you have to wait for perfect data before you can derive value from analytics? No. Rather than waiting for data stewards to improve data, get started and progress incrementally by refining and improving the data you have to work with along the way. This reduces time to value. Often inexact but fast approaches produce enormous gains since they result in better actions than the enterprise could have taken without analytics. The continuous feedback loop of analytics can also be used to fill gaps in imperfect data and reconcile disparate data and definitions across silos of information.
This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit IBM’s Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.