Data Governance Framework Maturity

According to the 2019 State of Data Management Report from 863 participants across Globe by Profisee, Data Governance is one of the top 5 Strategic initiatives in 2019. Advance trends in Machine Learning and AI has bolstered the digital transformation initiatives globally along with availability of full fledge DG (Data Governance) platform products like Collibra DG, Erwin DG, Informatica DG, SAP Master Data Governance and others have accelerated its adoption.

One must evaluate these platforms as they differ in terms of its features and its capabilities like the ability in providing Native Connectors, API’s and Webhooks to synch data with various Applications and thus enabling to build workflows to automate data rather than manually synch them up. Some platforms also provide Cloud and/or Server options and while some provide Hybrid Option with Secured Gateways.

Recent high-profile incidents at Facebook and loss of Customer data at regular intervals from high profile Organizations has also brought back Data Governance and Security into the front pages and into limelight with the Executives. Secondly, most Enterprises have realized that by maturing Data Governance, one could measurably benefit from Quality, Transparency and Trust in their Reports, Measures & KPI’s and thus improvement in the bottom line.

Maturity

An Organization can be grouped into any one of the 6 different maturity levels from an Immature to Sophisticated Maturity Levels and this can easily be validated by conducting an internal Survey and Assessment of their current State.

Instead of boiling the Ocean, Organizations can identify the Business Verticals of importance in their EIM Model along with its domains with a prioritization schedule. The most important requirement for a successful Data Governance engagement is to get an Executive Support all the way in its implementation.

Building a smaller yet nimble Organizational structure with clarity on Collaboration, Engagement, Transparency & Responsibility of efforts among various stakeholders, Data stewards is a prerequisite for an effective engagement.

Conducting roadshows on Success Stories to build more Successful ones, does keep the foresight needle straight with gusto as the Journey being bit long towards reaching its Zenith phase along the Data Governance Maturity Curve.

Drivers

Here some key major data points that can be used to evaluate DG Maturity level Curve in an Organization. (If you agree on 5 or more data points, your Organization may be a good candidate to mature its Data Governance Framework)

1. Lack of Trust in Critical and Significant Enterprise Reports
2. Lack of Data Lineage of Reports to Models and Reports
3. Lack of Transparency and Agreement to Measures and Metrics in Business and Cross Domains
4. Lack of Data Stewardship, Ownership of Line of Business Data Assets
5. Lack of identifying Redundant Reports and its Usage
6. Lack of Role Based Access Control to PII Data
7. Lack of Data Catalog & Business Glossary, MDM and Reference Data
8. Lack of Data Quality Scores on Data Sets
9. Lack of Ability to Tag Data Informational Tags by Users and Viewers to Organize its Usage
10. Lack of Issue Stewardship & Resolution documentation process for repeat data issues

Conclusion

Organizations are maturing their Data Governance environment in their current ecosystem including Data Lakes in building and streamlining Data Governance Principles, Policies & Standards to Curate and build Conformed layer for Fit to Purpose of Enterprise Data Assets to enhance its high degree of Value and Trust for Self-Serve of Data by its Data Citizens.