← Blog Aug 2, 2018 · Ian Greenleigh · Originally published on Medium
Photo by dylan nolte on Unsplash
This is Part 3 in a series about Collective Data Empowerment. If you want to get the whole series and accompanying tools in an ebook, go here.
Think about a challenge your company faces. Who among your colleagues knows the most about it? Who has the most experience with it? Who feels its weight every day? They probably aren’t all analysts and data scientists.
Now recall a data analysis that was focused on the challenge. Were the people you just pictured in the room, or on the thread, from the beginning? Were they consulted and considered throughout? If not, you might have a homogeneity problem (pattern #5).
These are 16 patterns we see in companies that are struggling to evolve into data-driven cultures. They share a root cause:
Most companies have invested more in Selective Data Empowerment than Collective Data Empowerment.
In the first post of this series, we distinguished between the two:
On to the patterns…
People don’t know what data your company has, and can’t find data when they need it. How can you use something if you don’t know it exists?
Data and analysis arrive too late to capitalize on opportunities. People struggle to understand it, and their questions aren’t answered fast enough.
High-paid data people spend most of the day making data more usable, answering the same questions, and refreshing reports. They can’t focus on the advanced tasks that only they can do.
Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets. — Steve Lohr, The New York Times