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Best Practices and Strategies for Achieving Information Governance Success with Shift Left Data Governance (and Integrating that Approach into Your IG and Privacy by Design Frameworks)

With the increasing centralization of data governance and a strategic focus on revenue generation, companies are prioritizing a Shift Left Data Governance approach. This tactic involves implementing data governance and security measures at earlier stages before data is stored in the cloud, which is in many ways analogous to the thought patterns behind frameworks like privacy by design and information governance by design, which endeavor to “bake in” compliance concepts into system designs as early as possible.

The Shift Left approach, in particular, is derived from a software engineering practice of monitoring and testing early and aims to streamline data security and improve data quality by standardizing data contents and formats upfront as data originates from its source.

Embracing the Shift Left Approach

The Shift Left approach offers significant advantages, including streamlined data access and efficient security. These capabilities help organizations meet the exponential growth in data consumption and creation. By moving data governance earlier in the data lifecycle, teams can proactively identify and address data-related issues, enhancing data observability and reusing metadata to assign security policies and user access rights.

With the advent and development of AI tools, in particular, we can foresee a surge in companies prioritizing Shift Left data governance and security, initiating robust data access governance and security capabilities available on cloud data warehouses and extending them back to the data as it leaves source systems. This proactive stance allows for better data management and security.

Key Best Practices for Shift Left Data Governance

Adopting Shift Left Data Governance and similar frameworks allows organizations to:

  • Identify problems early: Implementing governance measures early helps identify and rectify data issues before they become significant problems. This proactive approach ensures data integrity and reliability.

  • Standardize data formats: Ensuring data contents and formats are standardized from the source improves the overall quality and usability of the data collected. This reduces the need for extensive data cleaning and correction efforts later on.

  • Streamline data access: Early governance helps streamline data access and enhances security, reducing the risk of data breaches and compliance violations. Clear access policies and user rights should be established from the outset.

  • Automate continuous monitoring mechanisms: Incorporate continuous monitoring and automation to enforce governance policies throughout the data lifecycle. This reduces manual intervention and ensures compliance with established standards.

  • Effectively manage metadata: Align metadata, code, and data lifecycles to ensure consistency and accuracy. Metadata should be managed as code, ensuring it is always up-to-date and reflective of the data it describes.

Implementing Effective Governance Strategies

The Governance Shift Left is built on four key strategies:

  • Metadata as code: Treat metadata with the same importance as code and data. Ensure it follows the same lifecycle and is always aligned with the code lifecycle, ensuring consistency and accuracy.

  • You Build It, You Govern It: Make data engineering teams accountable for adhering to governance policies. This ensures that those who create the data are also responsible for its governance, leading to better compliance and quality.

  • Enforced policies: Governance policies should not be mere guidelines but should be enforced through code. This ensures that policies are followed consistently and cannot be bypassed, maintaining data integrity and security.

  • Context-aware policies: Ensure governance policies are well-documented, accessible, and self-explanatory. This provides clarity and understanding for all stakeholders involved, facilitating better compliance and governance.

By aligning data documentation with the software lifecycle, quality gates can be applied to data as it is typically done with software development, improving data quality and reducing maintenance costs. Eliminating the need for hand-offs between teams allows faster time-to-market and improved productivity. Additionally, automating data catalog updates ensures completeness and accuracy, with data and metadata always in sync, building trust in the data catalog.

Analogizing Shift Left to Information Governance by Design Principles

Now, for the good part. The Shift Left approach to data governance can be analogized to "Information Governance by Design" principles, much like how "Privacy by Design" principles are integrated into data privacy practices.  Here’s how that works.

First, Information Governance by Design, like Shift Left, is proactive and emphasizes anticipating and preventing data governance issues before they arise rather than reacting to them after they become problems. This foresight ensures that data integrity and security are maintained throughout the data lifecycle.

Additionally, just as  Information Governance by Design emphasizes the need to incorporate compliance measures into the design and architecture of IT systems and business practices, Shift Left integrates data governance measures into the earliest stages of the data lifecycle. This integration ensures that governance policies are not an afterthought but are fundamental to the data management process.


Moreover, Information Governance by Design and Shift Left both advocate for a holistic approach. This means considering the entire data lifecycle and ensuring that governance policies are consistently applied at every stage. By doing so, organizations can maintain high standards of data quality and security from inception to consumption.

Both approaches also emphasize the importance of automation and continuous monitoring. By automating governance policies and continuously monitoring compliance, organizations can quickly adapt to changes and maintain the effectiveness of their governance strategies.

Finally, just as Privacy by Design and Information Governance by Design principles call for transparency and accountability in data handling, Shift Left promotes clear documentation and accountability. Under a Shift Left framework, data engineering teams are specifically responsible for adhering to governance policies, and these policies are enforced through code, ensuring transparency and consistency.

Looking Ahead: The Future of Data and Information Governance

To effectively adopt and sustain approaches like Information Governance and Privacy by Design and Shift Left, organizations need to use platforms to automate data governance throughout the entire data component lifecycle, enforcing governance policies as code and automating their implementation. Ideally, the right platform should create non-bypassable rules, increase quality standards, reduce compliance risks, and provide full visibility of policy results.

And, when properly deployed, this platform can trigger a transformative approach to information and data governance, addressing the shortcomings of traditional frameworks and paving the way for a more agile, reliable, and cost-effective governance model. By effectively using proactive governance practices, we believe that businesses can meaningfully unlock the potential of their data assets, drive innovation, efficiency, and growth in an increasingly data-driven world and to a brighter and more secure digital future.

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May 30
Rated 5 out of 5 stars.

Excellent insight to baked-in governance of Meta Data and Data during the Information Life Cycle of content, relative context to ensuring democratization of informational assets.


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