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How can data engineering and Data Governance teams align?

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Data engineering and Data Governance are two essential aspects of any data-driven organization. Data engineering involves designing, building, and maintaining data pipelines, platforms, and systems that enable data analysis, processing, and delivery. Data Governance, on the other hand, involves defining, implementing, and monitoring policies, standards, and practices that ensure data quality, security, privacy, and compliance. How can these two teams work together effectively and efficiently to support the organization's data strategy and goals? Here are some tips and best practices to align data engineering and Data Governance teams.

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Sultan Alharbi

Executive Manager at Bupa Arabia | Data Governance and Management

1 Understand the roles and responsibilities

The first step to align data engineering and Data Governance teams is to understand the roles and responsibilities of each team and how they relate to each other. Data engineering is responsible for creating and maintaining the data infrastructure, such as data warehouses, data lakes, data pipelines, and data catalogs. Data Governance is responsible for creating and enforcing the data policies, such as data definitions, data lineage, data access, data quality, and data ethics. Both teams should communicate clearly and regularly about their expectations, requirements, and challenges, and respect each other's expertise and authority.

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2 Establish common goals and metrics

The second step to align data engineering and Data Governance teams is to establish common goals and metrics that reflect the organization's data strategy and vision. Both teams should agree on the key objectives, outcomes, and indicators that measure the success and impact of their data initiatives. For example, some common goals and metrics could be data availability, data accuracy, data usability, data compliance, and data value. Both teams should also align their processes and workflows to support these goals and metrics, and use tools and platforms that enable collaboration and transparency.

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Sultan Alharbi

Executive Manager at Bupa Arabia | Data Governance and Management
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1. Define Shared Goals and outline top-level objectives that mirror the organization's data strategy. 2. Agree on crucial metrics that measure the success and impact of both teams' data initiatives. 3. Ensure agreement on essential targets like data availability, accuracy, usability, compliance, and overall data value. 4. Align processes and workflow to support the jointly agreed-upon goals and metrics. 5. Use collaborative tools and platforms for smoother teamwork, transparency, and efficient goal tracking.

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3 Adopt a data culture and mindset

The third step to align data engineering and Data Governance teams is to adopt a data culture and mindset that values data as a strategic asset and a shared responsibility. Both teams should foster a culture of data literacy, data quality, data ethics, and data innovation across the organization, and engage with other stakeholders, such as business users, analysts, and data scientists, to understand their data needs and expectations. Both teams should also embrace a mindset of continuous learning, improvement, and experimentation, and leverage best practices, standards, and frameworks to guide their data activities.

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4 Leverage automation and orchestration

The fourth step to align data engineering and Data Governance teams is to leverage automation and orchestration to streamline and simplify their data tasks and operations. Automation refers to using tools and scripts to perform repetitive, manual, or error-prone data tasks, such as data ingestion, transformation, validation, or cleansing. Orchestration refers to using tools and platforms to coordinate and manage complex, interdependent, or distributed data workflows, such as data pipelines, data catalogs, or data governance frameworks. Both teams should use automation and orchestration to reduce data complexity, increase data efficiency, and enhance data reliability.

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5 Implement feedback loops and reviews

The fifth step to align data engineering and Data Governance teams is to implement feedback loops and reviews to monitor and evaluate their data performance and quality. Feedback loops refer to using tools and methods to collect and analyze data feedback, such as user surveys, data audits, data quality reports, or data dashboards. Reviews refer to using tools and platforms to conduct and document data reviews, such as data quality checks, data policy compliance checks, or data impact assessments. Both teams should use feedback loops and reviews to identify and resolve data issues, gaps, or opportunities, and to improve their data processes and outcomes.

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6 Celebrate and share successes and learnings

The sixth and final step to align data engineering and Data Governance teams is to celebrate and share their successes and learnings with each other and the rest of the organization. Both teams should recognize and appreciate each other's contributions and achievements, and celebrate their milestones and wins. Both teams should also share their learnings and insights, and promote their best practices and lessons learned. Both teams should use these opportunities to build trust, rapport, and collaboration, and to demonstrate the value and impact of their data work.

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7 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?

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How can data engineering and Data Governance teams align?

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3

4

5

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1 Understand the roles and responsibilities

2 Establish common goals and metrics

3 Adopt a data culture and mindset

4 Leverage automation and orchestration

5 Implement feedback loops and reviews

6 Celebrate and share successes and learnings

7 Here’s what else to consider

Data Governance

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How can data engineering and Data Governance teams align?

1

2

3

4

5

6

7

1 Understand the roles and responsibilities

2 Establish common goals and metrics

3 Adopt a data culture and mindset

4 Leverage automation and orchestration

5 Implement feedback loops and reviews

6 Celebrate and share successes and learnings

7 Here’s what else to consider

Data Governance

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