Post: Data Governance is not Protecting your Analytics!

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…

Data Governance

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

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.

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As the BI space evolves, organizations must take into account the bottom line of amassing analytics assets.
The more assets you have, the greater the cost to your business. There are the hard costs of keeping redundant assets, i.e., cloud or server capacity. Accumulating multiple versions of the same visualization not only takes up space, but BI vendors are moving to capacity pricing. Companies now pay more if you have more dashboards, apps, and reports. Earlier, we spoke about dependencies. Keeping redundant assets increases the number of dependencies and therefore the complexity. This comes with a price tag.
The implications of asset failures differ, and the business’s repercussions can be minimal or drastic.
Different industries have distinct regulatory requirements to meet. The impact may be minimal if a report for an end-of-year close has a mislabeled column that the sales or marketing department uses, On the other hand, if a healthcare or financial report does not meet the needs of a HIPPA or SOX compliance report, the company and its C-level suite may face severe penalties and reputational damage. Another example is a report that is shared externally. During an update of the report specs, the low-level security was incorrectly applied, which caused people to have access to personal information.
The complexity of assets influences their likelihood of encountering issues.
The last thing a business wants is for a report or app to fail at a crucial moment. If you know the report is complex and has a lot of dependencies, then the probability of failure caused by IT changes is high. That means a change request should be taken into account. Dependency graphs become important. If it is a straightforward sales report that tells notes by salesperson by account, any changes made do not have the same impact on the report, even if it fails. BI operations should treat these reports differently during change.
Not all reports and dashboards fail the same; some reports may lag, definitions might change, or data accuracy and relevance could wane. Understanding these variations aids in better risk anticipation.

Marketing uses several reports for its campaigns – standard analytic assets often delivered through marketing tools. Finance has very complex reports converted from Excel to BI tools while incorporating different consolidation rules. The marketing reports have a different failure mode than the financial reports. They, therefore, need to be managed differently.

It’s time for the company’s monthly business review. The marketing department proceeds to report on leads acquired per salesperson. Unfortunately, half the team has left the organization, and the data fails to load accurately. While this is an inconvenience for the marketing group, it isn’t detrimental to the business. However, a failure in financial reporting for a human resource consulting firm with 1000s contractors that contains critical and complex calculations about sickness, fees, hours, etc, has major implications and needs to be managed differently.

Acknowledging that assets transition through distinct phases allows for effective management decisions at each stage. As new visualizations are released, the information leads to broad use and adoption.
Think back to the start of the pandemic. COVID dashboards were quickly put together and released to the business, showing pertinent information: how the virus spreads, demographics affected the business and risks, etc. At the time, it was relevant and served its purpose. As we moved past the pandemic, COVID-specific information became obsolete, and reporting is integrated into regular HR reporting.
Reports and dashboards are crafted to deliver valuable insights for stakeholders. Over time, though, the worth of assets changes.
When a company opens its first store in a certain area, there are many elements it needs to understand – other stores in the area, traffic patterns, pricing of products, what products to sell, etc. Once the store is operational for some time, specifics are not as important, and it can adopt the standard reporting. The tailor-made analytic assets become irrelevant and no longer add value to the store manager.