4. Data using organizations know the role of humans.

Written by Raleigh Gresham

Data using organizations understand the many roles people play in data use. They’re fluent in the languages of organizational theory and politics, but spend little time managing and catering to them. Instead, they leverage or short-circuit them to get things moving.

Data using organizations refuse to let man-made information silos become excuses for not getting past the analysis. They figure out how to use data in spite of teams’ attitudes and choices and then work to get these same teams collaborating.

Data using organizations know that the majority of people are not in love with data, and that most of the actual day-to-day doers of things avoid analytics and analysts when they can. They compensate for this by obsessing over making the results of analytical work irresistible to the doers. Data using organizations use design thinking to empathize with the doers not the analysts or strategy teams. They are really good at writing. They know most people understand and react to good writing more than good math.

Data using organizations don’t believe analytics is a zero-sum game. They believe, like two artists painting the same scene, two analysts answering the same question employ their own unique approaches, yielding two distinctly valuable yet directionally similar results. Data using organizations do not support analytical competitions where the competitors have based their careers on having data or models that the other competitors don’t.

Data using organizations are “open-sourced” minded. They want the results of analytics work to be synthesizable and pirate-able. They believe that when their analysts have the ability to build on, adapt, and leverage each other’s work makes generating and advancing new analytical ideas fast. Data using organizations make sharing analytics ingredients and recipes very easy. Data using organizations expect analytic results to be remixed and adapted.

Data using organizations spend very little time hunting for singular sources of data truth. They avoid the paralysis of analytics by committee, focusing more on rules for making action more than rules for calculating metrics.

Data using organizations tend to polarize. They don’t create things that everyone in the organization agrees with or likes. They’re pleased when something doesn’t work for one group because that means it’s working perfectly for another group.

Data using organizations know humans are the most important part of any data work. They’re not caught up in the trendy pursuit of a human-less, analytics factories. They know that good data use depends more on people than computers and algorithms (a dependence that deepens as the complexity of data and systems grows). They don’t waste time automating a computer to be a human. They let a computer remember things and find patterns, and let humans use their “gut” to ask the questions and distill the answers with reality.