Top 10 Must Have Self-Service BI Governance Capabilities

If self-service business intelligence initiatives are on your agenda, follow these 10 best practices for ensuring proper governance.

Jen Underwood, Impact Analytix

February 23, 2017

4 Min Read

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Although self-service BI solutions have come a long way and add tremendous value in daily decision making, many offerings still lack crucial governance, data security, and privacy features. Do you know what governance capabilities to look for when selecting or implementing self-service analytics solutions? In this article I will discuss why governance is important and share my top 10 must-have features for rolling out self-service BI tools to the masses.

Why Govern Self-Service BI?

A self-service reporting system with little or no governance can be challenging to remediate. Reporting issues typically arise after numerous data models and reports have been developed. A few tell-tale signs that your self-service BI reporting is out of control include seeing multiple copies of the same data, you can only report on current organizational structure, you do not have a shared reporting calendar, or rolled up detail numbers do not match summary numbers.

[Jen Underwood will be the featured speaker in a May 17 Interop ITX session, Best Practices for Developing Real-time Dashboards.]

To avoid a reporting mess, it is far better to start with governance in mind on Day One. Potential risks of not governing your self-service BI offering include:

  • Failure to comply with regulatory, security or privacy requirements

  • Bad decisions based on outdated, incorrect, or incomplete data

  • Numerous copies of uncontrolled data compromise one version of the truth

  • Inefficient, non-reusable data models, business logic and metrics

  • Inability to verify the data origins and changes if audited

  • Reporting inaccuracy or limitations across time periods

  • Loss of credibility if reports cannot be reconciled

  • Scalability, maintainability and security issues

Trust but Verify Self-Service BI Security

At an industry conference that I recently attended, a director of analytics from a global 2000 retailer shared how his security team identified data exposure holes while testing one of the less mature but top three ranked self-service BI offerings in the market today. His security team findings initially shocked me. After more thought, I do understand how easily security can be overlooked when buying these solutions.

Security is a deeper level, complex technical topic. Most business users would not understand security, digital communication protocol terminology or what types of functionality to look for when buying self-service BI solutions. Security is not a “sizzle” feature that will get a lot of business user votes to prioritize it for vendor development investment. I also suspect security functionality is assumed by users that purchase these tools with the swipe of a credit card.

My top tip for buying or implementing self-service BI solutions: Don't assume your self-service BI offering has sufficient governance or data security controls; verify it.

Be sure to include your own information security officer or enterprise architect in self-service BI reviews. If your preferred platform does not have adequate controls, you might be able to address governance gaps with work-arounds. It is better to protect the organization than to beg for forgiveness when it comes to data security.

Key Governance Capabilities

Enterprise-ready, self-service BI platforms should have built-in governance capabilities for agile, yet controllable access to data for reporting. Effective governance requires structuring appropriate authentication, data connection access, report development and sharing processes.

Here are my personal top ten governance features that I consider when evaluating self-service BI solutions.

  1. Single sign-on authentication for access control

  2. Granular permissions for log in, authoring, editing, deletion, sharing, exporting and role based conditional content viewing

  3. Watermarking for approved, “sanctioned data sources”

  4. Content version control, migration workflow and rollback

  5. Exportable data, administrators should be able to easily migrate published data – avoid “data lock in” to a proprietary engine

  6. Support for slowly changing dimensions to accurately report data over time and across organizational structure changes

  7. Data lineage for understanding report data sources and changes

  8. Collaboration with conversation capabilities to reveal reporting content context

  9. Customizable administrative reporting, usage monitoring and alerting

  10. Administrative utilities for mass deployments

More Than Features

A self-service BI governance program is more than a collection of technical features. Governance frameworks usually address people, process, and technology in balancing information value with organizational regulatory, compliance, data privacy and ethics needs. Without all areas addressed or continually monitored, self-service BI governance initiatives quickly fade into a collection of unfollowed guidelines.

About the Author(s)

Jen Underwood

Impact Analytix

Jen Underwood, founder of Impact Analytix, LLC, is a recognized analytics industry expert. She has a unique blend of product management, design and over 20 years of "hands-on" development of data warehouses, reporting, visualization and advanced analytics solutions. In addition to keeping a constant pulse on industry trends, she enjoys digging into oceans of data. Jen is honored to be an IBM Analytics Insider, SAS contributor, former Tableau Zen Master, and active analytics community member.

In the past, Jen has held worldwide product management roles at Microsoft and served as a technical lead for system implementation firms. She has launched new analytics products and turned around failed projects. Today she provides industry thought leadership, advisory, strategy, and market research.

Jen has a Bachelor of Business Administration - Marketing, Cum Laude from the University of Wisconsin, Milwaukee and a post-graduate certificate in Computer Science - Data Mining from the University of California, San Diego.

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