Data governance is the practice of managing the quality, security, and usability of your data assets. It involves defining roles, policies, standards, and processes to ensure that your data is consistent, accurate, and accessible across your team. But how do you implement effective data governance processes for your team? Here are some tips to help you get started.
The first step is to understand what kind of data your team needs, how it is used, and what are the main challenges or risks associated with it. You can conduct a data inventory, a data quality assessment, and a data impact analysis to identify the sources, types, formats, owners, users, and dependencies of your data. This will help you prioritize your data governance goals and align them with your team's objectives and expectations.
Effective Data Governance is a Team work! Process would play a enabler role but changing dynamism of Business would make Process irrelevant over the period of time unless Team is active and agile. First, identify the Data Governance objectives for the project. Start with small and meaningful use case. Learn on the way, Process and Systems required in your Organization to make is success over the period of time. Key stakeholders like Data Owner and Data Stewards should actively participate in all stages of Project implementation. It would be nice to have automated, monitoring and alerting for Data Quality, Data Products Access and Trust Worthiness.
Understanding your team’s data needs is not just about the present, but also about anticipating future requirements. This foresight can help you prepare for upcoming projects and avoid last-minute scrambling for data. Additionally, as advanced analytics and AI become increasingly prevalent, they may necessitate additional data types and sources. Therefore, keeping an eye on technological trends and incorporating them into your data needs assessment can be beneficial.
The next step is to assign clear roles and responsibilities for your data governance activities. You need to establish who is accountable for defining, creating, maintaining, securing, and using your data. You can use a RACI matrix (Responsible, Accountable, Consulted, Informed) to clarify the expectations and communication channels for each data role. Some common data roles are data owners, data stewards, data custodians, data analysts, and data consumers.
Apart from Data Owner and Data Stewards, I noticed, its worth to identify Technical Steward and Technical SME who are familiar with Technicalities of Data Governance and Data Systems. Assign distributed roles and responsibilities to not overly burden Data Stewards.
Defining roles is not a one-time task. It’s crucial to ensure that everyone in the team understands their responsibilities and the importance of their role in data governance. Regular training sessions can help reinforce this understanding. Furthermore, rotating roles periodically can give everyone a holistic understanding of the data governance process, fostering empathy and collaboration within the team.
The third step is to develop and document your data policies and standards. These are the rules and guidelines that govern how your data is collected, stored, processed, shared, and disposed. Your data policies should cover topics such as data quality, data security, data privacy, data ethics, data retention, and data lifecycle. Your data standards should specify the technical requirements and best practices for your data formats, naming conventions, metadata, and data models.
Data policies should not be rigid. They need to be flexible enough to accommodate changes in technology, regulations, and business needs. Regularly reviewing and updating these policies ensures they remain relevant and effective. This adaptability can help your team respond quickly to changes and maintain the integrity of your data governance framework.
The fourth step is to execute and monitor your data governance processes. These are the workflows and procedures that enable your data policies and standards to be enforced and followed. Your data processes should include activities such as data validation, data cleansing, data integration, data access, data backup, data audit, and data reporting. You should also use tools and technologies that support your data processes, such as data catalogs, data quality tools, data security tools, and data analytics tools.
In my experience, governance processes are too often designed for the now, and don’t fully consider the future. By this I mean that teams decided to embark on a governance journey, and they come up with processes that figure out how to document and audit their existing environment. This is great for getting a handle on the current state of your data ecosystem and optimizing. However, an equal amount of thought needs to go into what the ideal state of governance processes would be, if we weren’t constrained by our current technical debt. By doing this, teams would have a bimodal plan for governing their existing ecosystem as well as how net new data and analytics use cases would be handled.
Implementation of data processes is where your planning comes to fruition. Automation can play a significant role in improving efficiency. Leveraging automation for routine tasks will help to free up your team to focus on more complex issues. This not only improves productivity but also reduces the risk of human error.
The fifth step is to communicate and demonstrate the value of your data governance efforts to your team and stakeholders. You should highlight the benefits and outcomes of your data governance processes, such as improved data quality, enhanced data security, increased data usability, and reduced data costs. You should also solicit feedback and suggestions from your team and stakeholders to continuously improve your data governance processes and align them with your team's needs and expectations.
Communication is key in any data governance framework. When communicating the value of data, it’s important to make it relatable. Use real-world examples and case studies to illustrate how good data governance has led to better decision-making and improved business outcomes. This can help your team and stakeholders understand the tangible benefits of data governance.
The final step is to foster and nurture a data-driven culture within your team. You should promote the awareness, understanding, and appreciation of your data assets and their potential. You should also encourage the collaboration, learning, and innovation around your data. You can achieve this by providing training, coaching, mentoring, and recognition for your team members on data governance topics and skills. You can also create forums, events, and platforms for your team to share, discuss, and showcase their data insights and achievements.
Data Culture is inherent to multiple new age Data Driven organizations. The real challenge is for age old Organizations and applications where things are moving very slow pace. Adopting Data Culture is difficult but not impossible. You may start with training, Data Literacy and making system Data Democratized. Once Data is available to everyone, empower and promote data consumers to get value out of it. Incentivize the right business use cases. Slowly it will become self moving and motivating Culture!
Promoting a data-driven culture is an ongoing effort. It requires continuous reinforcement through training and communication. Celebrating successes, learning from failures, and always encouraging curiosity and innovation around data can foster a positive data culture. This not only improves data governance but also empowers your team to leverage data in their roles effectively.