This is Part 1 in a series about Collective Data Empowerment. If you want to get the whole series and accompanying tools in an ebook, go here.
We’re on the edge of what could be the biggest multiplier in data productivity yet.
We are experiencing a paradigm shift: from Selective Data Empowerment to Collective Data Empowerment.
If your company navigates the shift, it will activate a hidden data workforce, refocus your advanced data practitioners on the highest-impact work, and achieve a data-driven culture.
So, what makes data-driven cultures different?
- Less-technical employees work with and benefit from data.
- Anyone can contribute domain knowledge to an analysis.
- The output of one data project benefits many other projects.
- More human talent and brainpower is available for any data objective.
- People discover, reuse, and adapt prior data work.
- Exposure to data improves data literacy.
- Network effects compound data’s impact.
An enterprise data catalog can help your organization activate your hidden data workforce. Not sure what a data catalog is? Read this page to learn more.
Only 33% of full-time employees in the U.S. are confident in their data literacy, according to Qlik. The rest are members of what we at data.world call the “hidden data workforce.”
Your hidden data workforce holds the lion’s share of available brainpower and talent.
You already have the people. Now you need the plan. (That’s what this series will give you!)
- Selective Data Empowerment makes advanced users more productive with data.
- Collective Data Empowerment makes everyone more productive with data.
Here, “empowerment” means the lift in data productivity resulting from a combination of tools, practices, and strategies.
When it comes to creating data-driven cultures, most companies have invested more in Selective Data Empowerment than Collective Data Empowerment.
For example…
If you buy better statistical modeling software, that’s Selective Data Empowerment because only those who understand statistical concepts benefit directly. But if you pair it with training that helps more employees use statistics to make decisions, the statistical models become useful to more people. Now you’re in Collective Data Empowerment territory.
Another example…
Who at your company would benefit if people could ask clearer questions about data? Your data scientists and analysts would waste less time trying to wrangle ambiguous questions into an answerable state. Everyone else would enjoy faster answers that deliver what they really want to know. You can earn this productivity boost across the full data literacy spectrum by attacking the problem from several angles at once. For instance, templates to guide question formulation and threaded discussions so new collaborators can see the context behind the questions.
Lift all boats
Companies that only pursue Selective Data Empowerment will never realize more than a fraction of data's potential because Selective Data Empowerment leaves most people out.
“In data and analytics, cross-functional teams with a broad skill set as well as different work and behavior styles will lead to more innovative thinking and better outcomes.”
–Rita Sallam, Research VP, Gartner (source)
Companies that evolve into Collective Data Empowerment quickly enjoy the profound business impact of inclusion and connectivity. These companies will become data-driven cultures faster than their competitors.
This is a shift that builds on the progress you’ve already made. It’s the rising tide that lifts all boats.
This is a shift led by those who aren’t satisfied with a tiny slice of data’s potential.
Is that you?
If so, get right to it and download the complete guide to building a data-driven culture through Collective Data Empowerment.
Check out the rest of the Collective Data Empowerment series!
- NEXT: The high stakes and staggering opportunity of data-driven culture
- 16 patterns you see in pre-data-driven companies
- Finding your way with Collective Data Empowerment
- 3 ways to find out what your data people aren’t telling you
(Editor’s note: This post was updated on 9/19/2018 to add new resources and reflect the completion of the initial series of posts.)
