How can you manage uncertainty in Data Governance?

Data Governance is all about improving trust-worthiness of underlying Data. There are many unknowns for which Business is totally dependent on IT to know what is what ... This dependency should be removed by adding end to end Data Linage and Business Glossary. Data Governance will improve the visibility of Data hence helps to get more value from it. Data Governance should classify and label PII and CDE attributes so Security framework can be build around them. Also, show Data Quality dashboard where Standards are not adhered. These points would help to reduce uncertainty around Data Governance.

Firstly make sure of the data reliability at the source level, do the sanity checks while moving towards maturity of the data. Make sure there are no leakages. Store the data under correct heads and sub heads.

Ensure high data quality standards. Regularly review and update your data quality measures to adapt to new types of data or changes in data sources. For example, e-commerce companies in the Middle East focus on maintaining high-quality customer data to adapt to market changes and customer needs.

Data Governance is a Vision! As data platforms are going through rapid evolutionary phase, Data Governance is seen as a must have encircling your data asset. To be able to reach to reach Data Governance vision, you must lay out well defined path with intermediate milestones and objectives like - Data Security, Data Catalog, Classification, Data Linage, Data Quality, Data Marketplace etc. Each of these are not necessarily required on day one but step by step if you start building around basic foundations to know your data. It will eventually take you there on your vision path. At some places, it will require very through planning and some places you would need more stakeholder buy in etc so shift focus according to phase!

In my experience based on a data maturity assessment using some of market references (Gartner, CMMI, ISO38505-1, etc), talking with high-level management, mapping the business strategy (goals and challenges) we should create a feasible tailor-made data governance roadmap considering 3 timing stages. First what could be done between 0-6 months, second between 6 months and 2 years, and third for more than 2 years. I like also to break the Data Governance roadmap in lanes for Policies, Standards, Processes and Change Management.

One thing I have also found helpful is to implement a three tiered data governance structure for effective data?governance?implementation. This would include: 1.Data?Steering committee: Made?up?of?senior?executives,?this?group?offers?strategic?direction,? coordinating?data?governance?with? organisational?objectives. 2. Data Council: The?implementation?of?the data governance?framework?is?the?main?focus,?with?department?heads? serving?as?data?owners. The?focus?is?on?implementing?best practices?and?coordinating?governance?with?organisational?goals. 3.Working?Groups: Composed?of?data?stewards,?or? subject?matter?experts. Tasked?with?maintaining?thorough?data?management?and?overseeing?daily?governance?operations.

In my experience, even the market offers many data governance frameworks as IBM, DAMA, CMMI, Gartner, etc. The majority are very complex, big and need a very well structured team, processes and tools to be enabled. What is making more sense in our customers is to start small with a simple framework like a 5W2H + PDCA "framework", to start to map and define action plans based on business goals and challenges that Data Governance competencies (Policies, standards, education, data quality, etc) could contribute to resolve.

Implement a data governance framework that is flexible and can adapt to changing regulations, technologies, and business needs. For instance, companies in the UAE, particularly in the rapidly evolving fintech sector, adopt flexible frameworks to accommodate the evolving regulatory landscape.

The best way to know if your data governance is having an impact on your business are through ROIs. See below for some examples: 1. Reduction in Data Errors 2. Reduction in Security Incidents 3. Increase in Data Utilization Rates 4. Improved User Satisfaction Scores 5. Demonstrable Value from Data Assets 6. Decrease in Data-Related Expenses 7. Acceleration of Decision-Making Timelines These simplified ROI metrics highlight the areas where effective data governance can contribute to positive outcomes within an organization.

Implement agile methodologies in your data governance projects. This approach allows for more flexibility and quicker adaptation to change.

I think strength lies in acknowledging weakness. You don't need to pretend to have knowledge or expertise where you lack it. Don't be afraid to admit this and say that you need to take a pause to search for information, check it, refer to professional literature, or the expert community.

Utilize advanced technologies like AI and machine learning to predict and manage uncertainties in data governance. or instance, banks are increasingly using AI to predict and manage risks associated with data governance.