A key challenge firms are grappling with is the shift to data and analytics-enabled decision-making and management. With sophisticated technologies and programs designed to provide leaders with more tools to understand, assess, and apply data-based reasoning to business decisions, the primary challenge has been turning the possibility of data and analytics into reality. To better understand the changes and challenges—and methods for effectively navigating them—I turned to Wes Nichols, the former CEO of MarketShare and former Chief Strategy Officer at Neustar, Inc., a trusted, neutral provider of real-time information services, which acquired MarketShare for nearly $500 million in December 2015.
Kimberly Whitler: How would you describe the changes in data and analytics that are impacting firms?
Wes Nichols: The biggest shift occurring is a migration from analytics being a “nice to have” ten years ago to it being a “must have” competency today. Companies have for years used data and analytics, but typically have leveraged it in a way that is tangential, rather than central, to the firm’s core strategy. But now, analytics have become a central part of firm strategy. Firms that don’t develop a competency in leveraging data and analytics to make better decisions, develop better products, and engage more effectively with customers will lose market share to those firms that do. What is most surprising to me is that this migration is happening faster than I thought it would. There are now more devices connected to the Internet than consumers; this acceleration of the Internet of Things is creating even more powerful data that marketers need to learn how to harness. We are clearly in the connected world of people, places and things, and the CMO is in the perfect role to leverage this to help accelerate revenue for their companies.
Whitler: How is the shift impacting the way companies operate?
Nichols: Companies are now better able to make data-driven decisions. There are a lot of factors impacting this: 1) computing power is cheaper, 2) analytics are more sophisticated and faster, 3) there is an orders of magnitude greater amount of data from which to draw insight, 4) there is better math and more modeling, leading to better quality insight, and 5) storage is cheaper. All of this impacts firms in several ways: 1) functional power is shifting (CMOs, the likely owner of this data and extractor of value, have become more important), 2) functions that historically rarely collaborated now need to regularly collaborate (Finance, IT and Marketing), and 3) resource investment is shifting as firms ramp up on data, security, and analytics. It’s even changing the way that decisions are being made—the type and quality of reports brought into leadership team meetings can influence team discussions and outcomes. It’s a fundamental, enterprise-wide shift.
Whitler: Who is leading the way in leveraging data and analytics effectively?
Nichols: Interestingly, it’s not the typical firms that come to mind. The historical leaders of analytics, like packaged goods and even ecommerce companies, have fallen behind (of course there are exceptions, but this is the general rule) while some of the more sophisticated industries are retail, automotive, and banking. Part of the challenge for packaged goods firms is that there is an overreliance on traditional methods of backward-looking, overly simplistic research (e.g. customer analysis) and this is confused with more contemporary and sophisticated methods of analyzing data that is predictive and forward-looking. A big difference that some of these companies are starting to realize. Another part of the problem is that packaged goods firms have historically been leaders in primary data creation, collection, and analysis. However, other industries, such as retail, banking, and automotive, are used to real-time data (more and richer quality data), which has pushed them to embrace more sophisticated, predictive analytical techniques. So in some sense, the presence of a lot of data has driven some industries like financial services to lead in developing an analytics capability. Others that have historically been reliant on traditional methods, typically housed in research departments, are having a harder time adapting to the new world.
Whitler: You mention that research is not the same as analytics. Could you explain why?
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