How can you ensure that data governance policies are properly enforced in software development?

All members in organization have roles and each roles has responsabilities based in resources and area they interact betwem them: this is a live condition in the organization. The data governance policies must guarantee the all activities in members are validated to hold the security and integrity of the organizational data: evaluate how stakeholders collaborate each other in theirs own roles to generate their governance roles and power of the policies creator.

With data governance frameworks, it is important to consider ... Standardized frameworks: Use standardized data governance frameworks to ensure a consistent approach in data, ML and GenAI. This includes defining data ownership and responsibility as well as establishing clear data and AI processing and development procedures. Modern data platforms: Leverage modern data platforms with built-in data governance capabilities, such as Databricks with integrated Unity Catalog. These platforms simplify data management and compliance and provide centralized control over data access, data quality and data sequencing ... particularly important in light of the new EU AI law.

To reduce the friction between teams having to implement some of these solutions from scratch (e.g data quality), the data platform can offer a solution that automatically captures the quality metadata and uploads it somewhere where a common visualization tool (e.g Grafana) can plot the information

One of the most practical and time-saving approaches to achieve compliance, is adapting data governance by design approach. Existing change management process should be updated to make sure that data governance team and proper governance controls, best practices, standards and requirements are considered and embedded within any new initiative or change. However, to make such approach successfully applied, data governance processes and the governance team engagement should be simplified, practical and supporting the business goals at the end. It should not turn into an obstacle delaying the overall change management process.

For teams to really adopt this, the effort of the integration should be minimal. The Data Governance team should work with the data platform team to see how some of these policies can be easily automated for 2 main reasons: 1) Common solution 2) Simpler setup

Define data governance policies and practices upfront - establish a blueprint from which teams can work from. Work as a cross functional data governance council to establish this foundation. Integrate practical elements into an engineering fundamentals playbook as a single point of reference for broad adoption - for example data quality, data security, data storage guidelines, ethics/Responsible AI. Include an 'exceptions' process. Where possible, provide code fragments, examples which can serve as a reference. For sensitive AI scenarios/use cases, ensure that the software development team conduct an impact assessment and involve the DPO/data governance representative in the review before moving into implementation.

Software Developers have a lot on their side in order to create a good product, it'd be probably the responsibility of a data engineer to know more about these topics and work together with software developers to help each other.

To ensure that data governance policies are properly enforced, you should also consider ... Align with business objectives: Especially in light of the new EU AI Act, it is imperative to ensure that data governance policies support and enhance overall business objectives. This alignment helps to gain buy-in from senior management and ensures that the policies are seen as value-adding and not just restrictive. Involve stakeholders: Involve all relevant stakeholders, including senior management, IT staff and legal counsel, to develop a comprehensive governance strategy. Clearly define and assign roles and responsibilities to ensure accountability and proper policy enforcement.

?? ???????????????????? ????????????????????: Create cross-functional committees to oversee adherence to data governance policies in software projects. ?? ?????????? ???????????????????? ???????? ?????? ??????????: Integrate data governance principles in the early stages of software development (requirements gathering, design). ?? ???????? ??????????????: Incorporate data governance checks in code review processes to ensure adherence to policies. ?? ????/???? ??????????????????????: Integrate data governance checks into CI/CD pipelines to enforce policies consistently. ?? ?????????????????????????? ??????????: Use tools for data masking in non-production environments to prevent sensitive data exposure.