11 Data Governance Best Practices in 2024 For Better Data Management

By Julian Lankstead

Published on December 13, 2023

Captivating illustration as the word "Data" gracefully spirals into the abyss of a mysterious black hole

Data governance isn't just a buzzword — it's the backbone of effective data management. Yet, like the elusive perfect cup of coffee, getting it right can be challenging. 

As we navigate 2024, there's a fresh brew of data governance best practices that everyone should know. 

This guide has the whole stack, whether you're refining your existing data governance program or just starting. 

Let's explore 11 practices set to revolutionize your data approach!

What are the best data governance practices?

Data governance has some golden rules that make everything else tick. They're practical steps that anchor your data governance efforts. Here are the 11 data governance best practices to optimize data management: 

  1. Define clear roles and responsibilities (e.g., Data Owners, Data Stewards).

  2. Develop a centralized data governance framework.

  3. Develop and enforce data quality standards.

  4. Put in place data classification schemes

  5. Implement data lifecycle management processes

  6. Collaborate across departments for holistic data management.

  7. Maintain comprehensive data documentation (e.g., data dictionaries, metadata)

  8. Use automated tools for data quality assessment and remediation.

  9. Establish a process for resolving data-related issues and conflicts.

  10. Implement robust data security and privacy protocols.

  11. Monitor and update data governance policies in response to technological and business changes.

Let's jump right to the list of top-notch practices to provide further details and keep your data game strong in 2024:

1. Define clear roles and responsibilities (e.g., Data Owners, Data Stewards).

In data governance, defining roles is crucial. Without clear roles and responsibilities identified and communicated, a data governance program will not succeed.

Assigning important roles establishes a workflow where everyone knows their part. This makes the entire data governance process harmonious and effective.

 Here are some of the key positions you might want to appoint in a robust data governance framework:

Data Owners

These folks are the decision-makers for specific sets of data. They determine the data's purpose. They decide who has access and are also responsible for its quality and integrity.

Data Stewards

Often called the "guardians of data," Data Stewards manage day-to-day data handling. They ensure it's accurate, up-to-date, and used in compliance with the organization's policies.

Data Managers or Custodians

These are the technical hands on deck. They manage the data's storage, maintenance, and security. They ensure it's safe, retrievable, and backed up.

Data Consumers

These people use the data for various tasks. They use it for analysis, reporting, or other functional needs. They rely on the data to be accurate and accessible. Consumers of data are the most widespread group in the organization as most employees use data in their day-to-day work!

Data Governance Council

This group usually comprises senior stakeholders who provide direction and resolve issues. They oversee the entire data governance initiative.

Data Architects

These people design the structure and organization of data. They focus on how it integrates, relates, and flows within systems.

Data Quality Analysts

The detectives of the data world. They identify, investigate, and resolve data inconsistencies and inaccuracies.

Laying out these roles and responsibilities creates a clear path for accountability. It ensures that the right person handles each piece of the data puzzle.

2. Develop a centralized data governance framework.

Imagine trying to manage a bustling city without a central governing body. Roads might get built, but who knows where they'd lead? 

Similarly, a clear data governance framework acts as the city planner. It ensures that data infrastructure grows organized, cohesive, and purposeful.

While you may have good data governance policies, these frameworks are the bigger picture. They help you marry every data governance principle across departments in your organization.

These are some of the benefits of implementing a clear data governance framework;

1. Unified Vision

All departments and teams have a shared understanding of data goals, standards, and practices. This ensures everyone is rowing in the same direction, smoothing organization-wide initiatives.

2. Efficiency

With a single, unified framework, there's less duplication of efforts. This saves your organization's time and resources. It also increases the chances of consistency for teams handling data governance independently.

3. Clear Accountability

A centralized approach clearly delineates roles and responsibilities. Whether it's data quality, security, or compliance, there's clarity on who's responsible for tasks. This makes it easier to address issues and implement changes.

4. Consistent Enforcement

Policies, standards, and practices are enforced across the organization. This uniformity ensures that data quality, security, and compliance benchmarks are always met. It doesn't matter which department or team handles the data.

5. Streamlined Decision Making

With a central data governance team or body, decisions related to data (whether it's adopting new technologies, setting policies, or addressing challenges) are faster.

6. Holistic View

A centralized framework offers a bird's-eye view of the organization's data landscape. This comprehensive perspective allows for better planning, forecasting, and strategy development.

By centralizing data governance, you can ensure uniformity in managing data assets.

3. Develop and enforce data quality standards.

When dealing with data, quality is paramount.

Data quality takes center stage in an era where data-driven decisions can make or break an entity. 

Poor quality data can lead to misguided strategies, operational inefficiencies, and compliance issues. In a competitive landscape, relying on such erroneous data can be costly. And it is not in financial terms only but also in lost opportunities and reputational damage.

Developing and enforcing data quality standards is not a best practice but a necessity.

When we talk about developing standards, it's about setting the benchmark for what's acceptable and what's not. It's a guide that helps the organization align its data collection, storage, and usage practices. 

Enforcement ensures that these standards aren't just theoretical. Active enforcement means a system checks, confirms and rectifies deviations. 

It's a proactive approach that ensures every piece of data meets the organization's criteria.

By prioritizing quality and its standards, you're showing your commitment to attaining data accuracy, efficiency, and, ultimately, success.

4. Put in place data classification schemes

Data classification is an essential compass guiding the data management process.

By segmenting data into distinct categories based on criteria such as sensitivity, relevance, or usage patterns, classification schemes help determine how data should be handled, stored, and accessed. 

For instance, everyday operational data might be readily accessible to many employees. In contrast, sensitive data would be available to only a few.

This process serves a dual purpose: 

First, it enhances security. Applying security protocols becomes easier when you know what data is sensitive or critical. This ensures that data breaches or unauthorized access incidents are minimized.

Secondly, it boosts operational efficiency. When teams know where to find the needed data and are assured of its integrity, the decision-making process becomes smoother and more informed. No more hunting for information or second-guessing its accuracy.

While it might seem like a backend, technical endeavor, implementing a robust data classification scheme impacts an organization's day-to-day operations. It ensures both safety and efficiency.

5. Implement data lifecycle management processes

Data isn't static. 

As everything in life goes through phases, data, too, embarks on a journey, from its creation to eventual archival or disposal. 

Consider the journey a piece of data makes. It starts with creation or collection, where we get raw data from various sources. It then undergoes cleaning processes, transformation, or integration with other data sets. 

Once processed, this data is stored and used in operations, analytics, or decision-making. And, over time, as it becomes less relevant or outdated, it goes into archival stages or, in some cases, purged.

Implementing lifecycle management processes means being proactive at each of these stages. It ensures data remains secure, accessible, and of the highest quality during transition. 

For example, during the storage phase, it’s crucial to protect data from breaches. Similarly, in the archival stage, protocols might dictate data storage duration. They may also state conditions under which it can be retrieved.

But why is this so critical in data governance? Your organization can avoid data mishandling, inconsistencies, and security vulnerabilities.

Embracing data lifecycle management isn't just about managing bits and bytes. It's about respecting the value data brings to an organization and ensuring it's treated with diligence.

6. Collaborate across departments for holistic data management.

Collaboration is the secret sauce to making an effective data governance strategy. And that's because best practices emerge when departments communicate and collaborate. 

When departments come together, pooling their knowledge, resources, and insights, your organization benefits from a holistic approach to data management.

First, collaboration breaks down silos. This is crucial because data, by its very nature, flows across the organization. 

A customer's journey, for instance, might span marketing, sales, support, and finance. If each department manages its data independently, there's a risk of discrepancies, overlaps, and gaps. By collaborating, they can ensure data consistency, relevancy, and a wholesome view.

The sales team might discover innovative data visualization tools. At the same time, the IT department may have insights into cutting-edge security protocols. Sharing this knowledge across the organization elevates everyone's game.

Collaboration also fosters a shared sense of responsibility. Data management isn't just the purview of the IT department or the data analysts. Each person understands their role in maintaining data integrity in a collaborative environment. 

This collective effort ensures that data remains valuable, driving innovative strategies.

7. Maintain comprehensive data documentation (e.g., data dictionaries, metadata)

Data documentation is the roadmap to understanding and utilizing vast amounts of information. 

It's the foundation that ensures data remains accessible, understandable, and usable over time. 

Without it, even the most valuable datasets can become ambiguous. This could lead to potential misunderstandings or misinterpretations. 

By documenting data, your teams can identify and understand each element's source and purpose. This practice ensures consistency across various departments and projects, streamlining processes and decision-making. 

If, for instance, you're integrating new datasets or revisiting older ones, comprehensive documentation provides the clarity needed to harness data's full potential.

In the long run, investing in thorough data documentation safeguards your data's integrity and value. It facilitates smoother collaborations and more informed decision-making.

8. Use automated tools for data quality assessment and remediation.

Maintaining impeccable data quality is both a priority and a challenge. Automation is a formidable ally here due to its ability to address the complexities and scale of contemporary data environments.

This makes automation a necessity rather than some modern luxury.

Automation in data quality assessment is a system's capability to sift through data and identify errors. 

These automated checks are essential since manual validation becomes error-prone with data growth. The precision of automated tools ensures that data quality checks are accurate and consistent, regardless of the dataset's size.

Also, the speed at which data is generated and consumed nowadays is staggering. Automated tools keep pace with this rapid flow, offering real-time or near-real-time assessments. 

This immediacy ensures that issues are flagged and addressed promptly. This minimizes the potential effects of poor data quality on downstream processes or decisions.

Beyond mere assessment, the real magic happens when these tools shift from identifying problems to solving them. Advanced systems can auto-correct known issues or provide detailed insights on corrective measures. 

This accelerates the resolution process and ensures that data remains reliable for all operational and strategic initiatives.

9. Establish a process for resolving data-related issues and conflicts.

No matter how meticulous, every organization will encounter data hiccups. Whether it's discrepancies between datasets, conflicting data interpretations, or challenges in data integration. These issues, if not sorted, can lead to more significant problems.

Having a solid process for resolving data issues stops minor glitches from causing setbacks.

To start, open channels of communication are crucial. Teams and departments need platforms to raise and discuss data concerns. This open dialogue can help identify the root cause of issues and brainstorm solutions.

Training plays a pivotal role, too. Equip your team with the understanding to solve common data challenges. The more informed the team, the more effectively they can address issues as they arise.

Additionally, fostering a culture that values feedback is vital. Sometimes, the best insights come from front-line workers, like the customer support reps who interact with data daily. 

Lastly, documentation is your friend. You're building a knowledge base by maintaining a record of past data issues, their causes, and their solutions. This resource can help in addressing recurring challenges and training new team members.

 10. Implement robust data security and privacy protocols.

We've all heard those horror stories of big companies getting hacked. And it's not just a PR nightmare – it hits the bottom line and shakes customer trust. Now, toss in a sprinkle of privacy concerns, and you've got yourself a full-blown data drama. 

With these concerns, there's increased scrutiny of how organizations treat personal information.

So, why is beefing up our data security a big deal? For starters, no one wants to be on the front page for a security blunder. 

Data security protects the organization from potential external threats like hackers or malware. 

Additionally, data security measures guard against internal mishandling or misuse of data, whether intentional or accidental. With clear protocols, employees know the dos and don'ts of data access, sharing, and storage.

Privacy protocols emphasize the ethical handling of data, especially personal or sensitive information. 

With regulations like the GDPR and CCPA, non-compliance can result in severe penalties. 

But beyond legal repercussions, respecting privacy means you value the owners. That means appropriately storing, collecting, processing, and distributing their personal details with their knowledge and consent.

11. Monitor and update data governance policies in response to technological and business changes.

Standing still isn't an option in the fast-paced realms of technology and business. As technology evolves and business strategies shift, data governance policies must adapt.

Revisiting these policies ensures they remain relevant. 

For instance, new software may require tweaks in data access controls. Likewise, expanding into a new market might come with its own data regulations.

It's not only about compliance, though. Updating policies with technological advancements ensures the organization stays efficient and competitive. 

Your governance policies should complement new data tools integration, not hinder it.

Also, as businesses pivot or refine their strategies, data requirements change. Governance policies must support these new directions. They should ensure data remains a catalyst for growth and innovation rather than bottlenecks.

In short, an effective data governance strategy evolves in response to the changes in technology and business.

Data governance definition

Data governance isn't just tech jargon. It's the heart of how organizations handle their most valuable asset: data

Data governance principles control the policies, processes, and practices surrounding data acquisition, usage, and maintenance. This framework ensures that data is always available, usable, and, above all, trustworthy. 

But it's more than an IT endeavor. Everyone from each department has a role to play. 

It's about setting clear roles with data stewards and data owners front and center. Each person has a specific responsibility to uphold the integrity and security of the data. 

In short, we enforce data governance policies to support an organization's data-driven endeavors, ensuring that data, in all its complexity and volume, is a tool for success rather than a liability.

It ensures businesses harness data's power effectively rather than just stockpiling it.

How can data governance benefit you?

Data governance acts as your compass, providing direction and clarity. But how exactly can it be your ally?

1. Clear Structure

A robust data governance framework provides a well-defined roadmap. Everyone knows their roles, from data stewards focusing on data quality to data owners ensuring appropriate access.

2. Optimized Data Management

Think of good data governance as what keeps data management running well. It ensures top-tier data quality and security.

3. Swift Data Retrieval

With data governance tools like the data catalog, locating critical data elements becomes easy.

4. Compliance and Protection

Stay ahead by ensuring compliance with evolving regulations. It's not just about dodging penalties; it's about safeguarding your organization's reputation.

5. Empowered Decision Making

Reliable, high-quality data is the bedrock of informed decisions. Data governance ensures you're making choices on solid grounds whenever and wherever.

6. Fosters collaboration

Break down silos and foster collaboration. Whether integrating new tech or aligning teams, data governance keeps everyone on the same page.

7. Future-Proofing

As digital transformation initiatives unfold and technology shifts, an adaptable data governance program ensures you remain agile.

The value of data governance isn't just in its processes or tools but in the transformation it brings.

Manage your data better with Alation data intelligence program

Understanding and implementing data governance best practices is essential for your business's success. But to truly maximize the benefits of these practices, you need the right tools by your side. 

Alation's data intelligence program serves as that perfect complement.

Alation’s homepage

Leveraging advanced machine learning, Alation streamlines dataset documentation and discovery, enabling your team to work more efficiently. Its lucid display of data lineage, and quality instills trust, fortifying your team's decision-making prowess. 

Alation’s Active Data Governance Solution page

More than just organizational aid, Alation prioritizes security. It strikes a balance where data is both accessible to those who need it and shielded from potential risks. 

For enterprises aiming to set the gold standard in data governance, Alation is more than a mere utility—it's the edge in your strategic arsenal.

Wrapping up

Understanding data governance processes is just the start. The real journey begins when you tailor your data governance programs to perfection. 

How do your current initiatives measure up?  

Alation's data intelligence program offers more than just management. It's a beacon for those aiming to implement data governance with unmatched precision. 

Ready to upgrade? Book a demo session with Alation for unparalleled data governance efficacy. 

Author Bio 

Julian Lankstead is the founder of JulianLankstead.com. He helps businesses increase revenue by providing strategies to improve business process efficiency. 

  • What are the best data governance practices?
  • Data governance definition
  • How can data governance benefit you?
  • Manage your data better with Alation data intelligence program
  • Wrapping up
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