Our new book Data Fluency is about the individual skills and organizational capabilities necessary to communicate effectively with data. We are fascinated by the interplay and interdependence between the two. That is, it takes people who are skilled with presenting data to enable the sharing of insights; equally important, data fluency requires a organizational culture that values decision-driven discussions.
In fact, the framework we introduce in our book is one step more complex. We present the distinction between data consumers and data authors. Those who use data to inform their work versus those who’s work it is to inform people with data. This distinction applies both at the individual level and at the organization level, where we consider the data fluent culture (how do people consume and make use of data?) and the data product ecosystem (what capabilities, processes, and tools are in place to produce effective data products?). As a result, we end up with four building blocks that compose a data fluent organization.
However, it is rare to find a companiy that is strong in all four of these quadrants. Chapter 3 of our book identifies some of the common challenges we see as companies stumble in their efforts to make use of their data. Below are four situations we’ve seen in our experience working with dozens of companies trying to build analytics into how they run their business:
Reports have a way of multiplying like rabbits. Start with a perfectly useful and important report: a monthly sales report with product enhancements and utilization metrics sent to strategic accounts to make them aware of improvements coming and past usage. Customers see the information and want to know more. The report grows. A missing metric is added along with a detailed breakout. New reports are spawned, but the old ones don’t go away. Someday, somebody might still find them useful. The general thinking is: “If we report on everything, surely the right information will exist somewhere in a report.” Perhaps they’re right, but if no one can find what they need, everyone’s left sorting rabbits.
Departments in an organization can easily become independent silos, operating with their own set of norms, conventions, and terminology. This impacts what you can do with your data and what you can understand. You’ve experienced this problem if you’ve ever been on a customer service call where you give all your personal information at the start of the call and then have to give it all again every time you’re transferred. Each organizational department may use different data systems and terminology, processes, and conventions in data conversations and products.
Working with data can require a lot of technical skill. And data can tell stories and reveal truths that an organization may not want to share broadly. Why not centralize your efforts and limit access to data to the highly trained few who can be trusted to bring order to chaos?
Like an over-eager police force hunting down deviants, this IT-led vision of business intelligence focuses on control, consistency, and data management. An extreme approach, however, comes at the expense of the individuals who use the data. Distancing analysis from the people who must use it results in data producers and their products that are disconnected from the decision- making process. Data products aren’t trusted and they often aren’t useful. All the problems of a command and control economy emerge.
In Search of Understanding
An organization’s capability to make fluent decisions from data depends on how well the organization knows itself. Self-awareness helps you answer the difficult questions: What does success look like? Are we moving in the right direction? Who should we compare ourselves to?
For a new organization—especially one in an emerging market—it takes time to figure out what matters most. These organizations often lack focus in their data analysis, measurement, and communication while on the path of discovery. Even with the best intentions, organizations can struggle to make good use of their data as they search for the information and metrics that will align with their emerging strategy.
Each of these areas is a failure of one or more of the quadrants in the data fluency framework. It may be a lack of leadership, a organizational culture that prefers anecdotes to data, or sparse skills for delivering data in ways that are easy to digest.