How do you set data governance standards for integration?

Before integrating your data, it's essential to assess your data sources. Understand what data you have, its origin, structure, and the business rules that apply. Identify the data owners, stewards, and consumers, along with their roles and responsibilities. A thorough data source assessment helps evaluate the quality, accuracy, completeness, and relevance of your data, as well as the risks and challenges associated with integration. In my experience, this assessment lays the groundwork for successful data integration, ensuring that your data is reliable and aligns with business objectives.

What is your data integration constraints and assumptions? Understand it then define the goals aligned with business, for example, be cost effective, be secure, low-coding, transparent, redundant, manageable, scalable, simple, etc.

Once you understand your data sources, defining your data integration goals is crucial. Determine the business objectives and outcomes you aim to achieve, identifying users and stakeholders who will benefit. Consider how you will measure the success and value of data integration. Your goals should align with data governance principles and support your overarching data strategy. Clear objectives not only guide the integration process but also ensure that efforts lead to tangible improvements and insights. By aligning integration with business needs, you maximize its impact on organizational efficiency and innovation.

1. Establish clear data definitions and metadata standards to ensure uniform understanding and usage across integrated systems. 2. Implement data quality standards to maintain accuracy, completeness, and reliability of data during integration processes. 3. Define data security and privacy standards to protect sensitive information and ensure compliance with regulations. 4. Standardize data exchange protocols and formats to facilitate seamless data flow between systems. 5. Regularly monitor and audit data integration processes to ensure adherence to established standards and identify areas for improvement.

Once you've selected your data integration method, it's time to implement your solutions. This involves designing, developing, testing, deploying, and maintaining data integration pipelines, workflows, and applications. Adhere to data governance standards for quality, security, privacy, and compliance, ensuring your solutions are reliable, scalable, and efficient. Document your data integration processes and metadata, and communicate them clearly to data users and stakeholders. In my experience, a well-implemented integration strategy enhances data accessibility and drives informed decision-making across the organization.

Data governance standards should be dynamic and adaptable. As your data sources, goals, and methods evolve, regularly review and update your standards to reflect these changes. Ensure they align with industry standards and best practices, benchmarking your data integration performance against peers and competitors. In my experience, maintaining up-to-date governance standards not only ensures compliance and data quality but also supports innovation and efficiency in data integration, keeping your organization competitive and responsive to new challenges.

All data governance policies related with data integration must include guideline about the designing stage and its assets like data standarization, data mapping or contingency plan.