In my prior blog I shared lessons around Modernization of Analytics, and I touched on the perils of not keeping end users happy. For Directors of Analytics, these people typically make up your biggest group of users. And when these users aren’t getting what they need, they do what any one of us would do…go get it done themselves. In many cases this can lead to them purchasing different analytics tools and in bad cases it can lead to them getting their own data and analytics stack to achieve self service.
In the analytics world I’m not saying it’s necessarily bad to have multiple tools in a company, but the governance models have to be in place to ensure the data and resulting analytics are accurate, consistent, trusted and secure! Most organizations believe they have this covered with the implementation of a Data Governance Policy…
A Data Governance Policy formally outlines how data processing and management shall be done to ensure data is accurate, accessible, consistent, and secure. The policy also establishes who is responsible for information under various circumstances and specifies what procedures should be used to manage it.
Do we see what’s missing? No mention of analytics usage. How the data is managed and how it gets to the tool is governed but once in the tool then it’s dark and open season to do as you please in the name of self-service or just getting the job done. So, what is Analytics Governance?
Analytics Governance Policy formally outlines what processing, transformations and editing of the analytics is permitted beyond the data layer to ensure accurate, accessible, consistent, reproducible, secure, and trusted results.
We all have a dashboard with key metrics that we monitor and are possibly compensated on. We all try to avoid having multiple incarnations of this dashboard, but this rarely seems to happen. Having an Analytics Governance policy in place helps avoid differing results when using multiple tools or unique authors. In the perfect world we have the 1 aligned to dashboard that we all have input into and trust. Then an Analytics Governance policy also ensures only certain people can make aligned edits to the dashboard going forward.
Hopefully, most readers and nodding their heads and agreeing- which is great. I believe we all aspire to be honest and do what is right, and an Analytics Governance policy just formalizes that for Analytics. I think more importantly it formalizes the need to have a conversation around the data needs beyond what the source is providing and focuses towards asset building and usage. It also leads to looking for solutions where lineage and change management are supportive of self-service analytics (and yes Motio can help here).
Think about it
Policies exist to help protect everyone. Most often we think of malicious scenarios and believe they can’t happen to us. Unfortunately, I’ve seen and worked with companies where they have happened; A simple local filter on a dashboard to show all accounts vs active accounts where a bonus was at stake. A team accessing the governed data as per the governance policy but lifting it to a cloud database for self-service usage outside of IT’s control.
The risks associated with no analytics governance policy in place:
- Bad decisions – incorrect analytic results or results that aren’t trusted
- No decisions – stuck in analysis on the analysis
- Wasted cost – lost time with teams doing their own with their own tools
- Loss of brand equity – slow market responses, bad choices or data leak going public
Talk it over with your teams and stakeholders. Having open conversations around these topics can be tough but bridging the gaps between IT and lines of business is so essential for success and positive culture. Everyone wants to be the most agile, responsive but most of all – right!
If you want to learn more about how Motio solutions support self-service analytics, contact us by clicking the button below.