Help Your Team Understand What Data Is and Isn’t Good For
Leaders today increasingly turn to big data and advanced analytics in hopes of solving their most pressing problems, whether it’s a drop-off of repeat customers, a shift in consumption patterns, or an attempt to reach new markets. But when it comes to uncovering the motivations and rationale behind individual behaviors within a social system, data can only do so much. It can guide the discovery of a problem, but it won’t determine the solution. In other words, data analytics can tell you what is happening, but it will rarely tell you why. To effectively bring together the what and the why, leaders need to combine the advanced capabilities of big data and analytics with tried-and-true qualitative approaches such as interviewing groups of individuals, conducting focus groups, and in-depth observation. Solving social behaviors still requires small-scale qualitative exploration to engage people and learn more about what’s truly motivating the behaviors that show up in the data.
Leaders today increasingly turn to big data and advanced analytics in hopes of solving their most pressing problems, whether it’s a drop-off of repeat customers, a shift in consumption patterns, or an attempt to reach new markets. The prevailing thought is that more data is better, especially given advancements in tools and technologies such as artificial intelligence and predictive analytics.
But when it comes to uncovering the motivations and rationale behind individual behaviors within a social system, data can only do so much. It can guide the discovery of a problem, but it won’t determine the solution. In other words, data analytics can tell you what is happening, but it will rarely tell you why. To effectively bring together the what and the why — a problem and its cause, in order to find a probable solution — leaders need to combine the advanced capabilities of big data and analytics with tried-and-true qualitative approaches such as interviewing groups of individuals, conducting focus groups, and in-depth observation.
In my conversations with business leaders about how they use data analytics, a primary focus is on technical, large-scale systems. This is where big data and analytics can really shine, in applications such as predictive maintenance. Industrial companies, from railroads to oilfields, use predictive analytics to ensure smooth operations; rather than wait for a mechanical breakdown to occur, predictive maintenance prevents problems and avoids downtime.
What works with locomotives and oil rigs, however, can be far less effective when it comes to influencing people’s behaviors. With social systems and the behaviors generated by large groups of individuals — who does what and under what conditions — it is far harder to identify solutions to problems. This points to the shortcoming of using data analytics alone for solving problems that arise from individual behavior.
That’s not to say big data and analytics don’t play an important part. Rather, by understanding the strengths and limitations of using big data in this way, leaders can employ the most effective strategies for identifying the what and why of a problem, and how to solve it — and can help their teams learn to do the same. Here are five important considerations that everyone who works with big data needs to understand:
Data analytics are most effective as part of an overall process to identify, explore, and test, but are not the only tool for the task. Solving social behaviors still requires small-scale qualitative exploration to engage people and learn more about what’s truly motivating the behaviors that show up in the data.
Joel Shapiro, JD, PhD, is clinical associate professor and executive director of the program on data analytics at Northwestern’s Kellogg School of Management.
Help Your Team Understand What Data Is and Isn’t Good For
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