Use these data governance best practices to maximize the value out of your data
Today, most teams know data governance matters. Awareness isn’t the problem, but execution is. As governance efforts increase, self-service analytics rise just as quickly. So striking the right balance — enabling self-service while preventing unauthorized access — is what keeps top teams at the top.
In this article, we’ll walk you through those effective strategies, the common challenges teams face, and how to navigate them. You’ll also find actionable tips to help you apply each best practice within your systems.
What is data governance?
Data governance is a set of standards or policies that keep the data secure and reliable throughout its lifecycle. The goal is to provide safe and high-quality data for easier access and enhanced collaboration.
Data governance helps you:
Easily access high-quality data
Boost cost efficiency by reducing duplicates and eliminating unnecessary data
Stay compliant with regulatory requirements or stakeholder requirements
Build customer trust and loyalty by showing users you take their data seriously
When data governance policies are poorly defined, organizations face implementation challenges, reduced transparency, limited data usage, and sometimes even serious compliance or legal issues.
Let’s look at more common challenges that companies face.
When your business expands, your data volume and user base grow. The current governance methodologies and techniques might not work for growing structures and changing demands.
Prioritizing data security could mean asking users to jump through more hoops, like entering specific credentials every time they want to access their team’s database.
On the flip side, loosen things up for a smoother experience, and you risk exposing sensitive data. So, the challenge is finding that middle ground: keeping strong data privacy without making users feel like they’re locked out of their own systems.
With 70% of data leaders saying that self-service is a worthy goal to pursue, more users are building dashboards, copying data, and setting up pipelines on their own. This speeds things up, but it also creates risk. Users may explore sensitive data more than intended, and accidental access can lead to breaches.
That’s why governance today needs to do more than just manage permissions. It should also control how data is used.
Hex is a popular choice for a self-service analytics platform and supports governance at both ends. You can control who can access what based on roles and teams, and ensure users get accurate, consistent data.
With data mesh and decentralized ownership on the rise, data now spreads across tools, teams, and departments, each with its own setup and security standards. This fragmentation challenges how we standardize data governance strategy, because each tool handles its data differently.
Now, it’s time for the good stuff. Let’s get into the governance friction points teams face — from unclear ownership to tool sprawl — and show you practical, battle-tested ways to tackle them.
A cross-functional data governance council brings together teams from across the org: IT, engineering, marketing, HR, and key stakeholders. When everyone’s at the table, it’s much easier to spot gaps and create governance policies that work across departments.
The influence each team holds in this setup depends on your organization’s priorities. For example, security-heavy environments, like banks and financial institutions, typically follow strict governance. Other teams have limited flexibility because security is non-negotiable there.
In other organizations, data teams lead and may loosen security to improve access and user experience.
Some organizations go all in on security, applying blanket-tight controls across the board. In doing so, they compromise usability and accessibility, which slows down collaboration.
Take OAuth, for example. When every individual must authenticate with specific credentials before querying any database, it adds friction and stifles the user experience.
To ease that pain, consider data classification. Most organizations classify data into four categories: public, internal, confidential, and restricted. Public is the least secure zone, with restricted being the most secure, and the others fitting in between.
This ensures highly sensitive data gets strict authentication and tight permissions. Lower-stakes data categories get lighter controls.
Takeaway: Classify your data into these categories, or define new ones if needed, and apply security rules accordingly. This way, you keep your data security tight where it matters and your user experience intact everywhere else.
If you think data governance is just about security, think again. Its true purpose is to ensure that data is secure, usable, and trustworthy. To achieve the latter two (usability and trustworthiness), the data needs to be of high quality. Only then can teams rely on it, and customers or stakeholders trust it.
To maintain high-quality data, enforce the six dimensions of data quality within your governance framework: Accuracy, Completeness, Consistency, Timeliness, Validity, and Uniqueness. Here’s how you can start:
Define clear data standards. Start by deciding what “good enough” looks like for your org. That means defining clear data cleaning standards like:
Acceptable error margins (is 1% bad data fine? How about 10%?)
Which fields are must-haves (“First Name” is not optional)
Identifying the authoritative sources for each domain of your organization’s data
Set up data quality checks. Now that you've set your standards, make sure the data meets them. A few examples:
If you're okay with up to 10% missing data, add a check that throws an alert when it crosses that threshold
Match intermediary data with source systems to catch discrepancies (Accuracy)
Flag duplicate records (Uniqueness)
Bake these checks into your data pipelines so nothing moves forward unless it meets your quality bar.
Implement data profiling tools. These tools analyze the structure of your datasets and generate statistical summaries and metadata insights. They help you:
Spot anomalies before they break dashboards
Understand which fields are routinely messy
Get fast insights into completeness, consistency, and more
Security isn’t something you sprinkle on top at the end of data governance. It needs to be baked in from day one. That’s the heart of security by design, a proactive approach to including security in every stage of the data lifecycle.
First up: data collection. Only collect the data you absolutely need (because hoarding data is a liability), and apply validation rules right at the source. Classify data as it comes in so you can apply the right protection levels. As that data flows into your systems, make sure it’s encrypted in transit.
Now that it’s sitting in storage, apply role-based access controls so only the right teams can access the right data. And log every read and write operation — because if something goes wrong, you want a clean trail to follow.
Once data is in use, the focus shifts to the apps and systems that interact with it. Those systems should be built with input validation and secure session management.
At the end of the life cycle, permanently remove the data and log all deletion events so you can prove data was handled properly. If backups are stored, they should follow the same security rules as the original data.
When teams have access to shared data definitions, lineage, and metadata, everyone in the organization speaks the same data language. Centralized metadata management creates a unified repository or data catalogue that captures all the metadata across your organization.
This setup acts like a searchable index. Users can quickly see what data exists, where it lives, and how it’s used. It simplifies data discovery and boosts data visibility.
To effectively centralize your metadata, use tools that automatically extract it from various sources and populate it into the central repository. For example, Hex seamlessly integrates with tools like Secoda to offer a central data catalog.
Automating governance means using new technologies and tools to implement data governance policies. It keeps your framework consistent and brings some much-needed relief to overworked security folks.
Here is what they can provide:
Centralized governance tasks: Data admins, data stewards, data owners, and team members manage everything related to data management and governance in one tool.
Streamlined risk management: Automated systems monitor for threats in real time and fire off alerts the moment something sketchy happens.
Automated policy enforcement: You can define policies, rules, and principles needed to maintain data governance. These tools automatically enforce them to ensure high-quality, secure data.
As your business grows, new data sources come in, schemas shift, and data volumes increase. Impact assessments review how these changes (or any governance policy updates) affect your systems and workflows.
Run these assessments regularly to catch risks early. Maybe your system routinely misclassifies a new data source into the wrong sensitivity category. Or your existing validation rules no longer fit the shape of your new data.
Impact assessments also flag changes to governance policies. Say you expand into a new country where the data compliance requirements are slightly different. Without a check-in, that difference might slip past your radar and land you in legal hot water.
After controlled data access, the next big challenge is data usage. Analysts, marketers, product managers, and anyone who touches data should understand how to use it responsibly. Without that shared awareness, even the best governance frameworks fall flat.
To bring in this awareness, invest in strong, focused data literacy programs.
Start with short, practical training — not those hour-long marathons no one remembers. The goal is to get people motivated, help them understand why a data governance framework matters, what rules exist, and how they fit into the bigger picture. When data professionals know the “why” behind the policies, they’re far more likely to follow them and help reinforce a reliable data culture.
Traditionally, we assign a fixed shelf life to each dataset or database. After the set period, the data is automatically deleted. These are static retention policies.
Adaptive retention policies take a more flexible approach. With adaptive policies, you use queries and filters to determine how long your data objects should stay, making the retention period dynamic. This approach helps optimize storage costs while meeting the actual data needs of your org.
Let’s say you want to hold onto premium customer emails longer than casual visitor emails. You can set a rule to retain records tied to premium customer IDs for longer periods while the rest gets cleaned up sooner.
Modern data governance tools support this. They offer user-friendly interfaces and programmatic ways so you can define adaptive policies your way. A few solid options to check out include Dynatrace, Collibra, and Informatica.
Crisis management is an organization’s framework on how to deal with and respond to unexpected data threats. These threats can take many forms: data loss from natural disasters, data breaches or theft, or cyber attacks.
Identify a special data governance team responsible for spotting and responding to incoming threats. This team regularly runs risk assessments, spots potential problems, and prevents them before they occur.
Next, your framework should include detailed action plans specific to different crisis scenarios. Whether data is stolen or a privacy rule is violated, each scenario should have a dedicated roadmap.
Don’t forget your communication strategy for hard times. Internally, you can keep things straightforward. But when you’re dealing with customers, the media, or the public, you need a deliberate plan. One that explains what’s happening without causing panic and preserves trust in your brand intact.
Finally, consider investing in crisis management software or look for data governance tools with crisis management features included. These tools can help automate threat detection and, when possible, block issues before they escalate. They also make it easier to run risk assessments regularly.
Throughout this guide, we’ve covered key best practices for effective data governance. These practices power the success stories of leading organizations. In a survey by Drexel University's LeBow Center, 83% of data professionals say that mature data governance programs improve data quality.
To adopt these practices, you need the right tools. If you use a self-service analytics platform for data access and collaboration, make sure it has built-in governance features.
Hex is one such tool built with governance in mind. With role-based access control and encrypted data storage, Hex ensures sensitive data stays secure and compliant with regulations like GDPR and HIPAA. It also supports strong governance through metadata centralization, data lineage tracking, and collaborative visibility, making governance a seamless part of your workflows.
Check out how Hex empowers the teams at Workrise, a leading workforce management solution, to collaborate securely and seamlessly, and extract data insights for enhanced decision-making.