How can you include data scientists in a data governance framework?

1 Align data governance with business objectives

Data governance should not be seen as a barrier or a burden for data scientists, but as a way to support and enable their work. Therefore, it is important to align data governance with the business objectives and value proposition of data science projects, and to communicate them clearly and consistently. Data scientists should understand how their data sources, methods, and outputs contribute to the organization's strategy and performance, and how data governance helps them achieve that. Data governance should also provide feedback and recognition for data scientists who adhere to the standards and best practices.

2 Define clear roles and responsibilities

Data governance requires collaboration and coordination among different roles and functions, such as data owners, data stewards, data consumers, and data custodians. Data scientists can play one or more of these roles, depending on the scope and nature of their projects. For example, a data scientist may be a data owner for a specific dataset that they create or maintain, a data consumer for a dataset that they use or analyze, or a data steward for a dataset that they oversee or validate. Data governance should define the roles and responsibilities of data scientists and other stakeholders, and assign them the appropriate rights and privileges to access, modify, and share data.

3 Establish data quality standards and metrics

Data quality is a key aspect of data governance, as it affects the reliability, accuracy, and usability of data. Data quality standards and metrics should be defined and agreed upon by data scientists and other data stakeholders, based on the business requirements and expectations of data science projects. Data quality standards and metrics should cover aspects such as completeness, consistency, timeliness, validity, and relevance of data. Data governance should also provide tools and processes for data quality assessment and improvement, such as data profiling, cleansing, validation, and monitoring.

4 Implement data security and privacy policies

Data security and privacy are another critical aspect of data governance, as they protect the confidentiality, integrity, and availability of data. Data security and privacy policies should be defined and enforced by data governance, based on the legal, ethical, and contractual obligations of the organization and its data stakeholders. Data security and privacy policies should cover aspects such as data classification, encryption, authentication, authorization, auditing, and retention of data. Data governance should also provide training and awareness for data scientists and other data users on the data security and privacy risks and best practices.

5 Foster a data culture and community

Data governance should not be a top-down or rigid approach, but a collaborative and adaptive one. Data governance should foster a data culture and community that values and respects data as a strategic asset, and that encourages and supports data sharing, learning, and innovation. Data governance should facilitate data literacy and education for data scientists and other data users, and provide platforms and channels for data discovery, access, and collaboration. Data governance should also solicit and incorporate feedback and suggestions from data scientists and other data stakeholders, and continuously improve and update the data governance framework and policies.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?