February 10th, 2026
Top 13 Data Governance Best Practices to Implement in 2026
By Drew Hahn · 16 min read
I spent weeks testing data governance best practices with marketing teams, finance departments, and operations groups to see which ones reduce costs and compliance risk. Here are the 13 that consistently delivered results.
What is data governance?
Data governance is a set of rules and processes that control who can access your company's data, how you use it, and how you keep it secure and accurate. You define standards for data quality, assign people to own different datasets, and create policies that protect sensitive information while making data accessible to the teams who need it.
For example, a marketing team might store customer email addresses in a CRM. Governance rules define who can access that data, how long you keep it, and what happens when someone requests deletion. Then, if you connect data to tools for analysis, these governance standards protect sensitive information while letting teams work with the data they need.
Who is responsible for data governance?
Data governance is a shared responsibility across executives, data owners, data stewards, and governance councils, with each role handling different aspects.
Here is what each role is responsible for:
Chief Data Officer or governance lead: Sets the overall strategy and secures executive buy-in for the governance program.
Data owners: Manage specific datasets within their departments, like your finance director owning financial data or your marketing leader owning customer data.
Data stewards: Enforce the day-to-day policies, train team members, and handle access requests.
Governance council: Made up of leaders from different departments who resolve conflicts and review policies regularly. Some of the councils I've worked with met monthly to keep teams aligned and data secure.
For business users, these roles mean knowing who to ask when you need customer data, who approves access to financial reports, and who resolves conflicts when marketing and sales define "revenue" differently.
Why data governance best practices matter more than ever
Data governance best practices matter more than ever because companies are generating massive amounts of data while AI tools make it easier to access that data without proper controls.
Here are the key benefits of data governance best practices:
Cost reduction: Governance reduces duplicated datasets, unused storage, and rework caused by conflicting numbers. I’ve seen teams save hours each week once they know which dataset is correct and where it lives. When business users can find trusted data quickly, they spend less time rebuilding reports and second-guessing results.
Risk management: Governance makes it clear where sensitive data lives, who can access it, and how that access is enforced. This matters for audits, but it matters even more for everyday analysis. Business users can explore data with more confidence without worrying that a simple query exposes information it shouldn’t.
Faster decisions: Teams move faster when they trust the data they’re working with. Marketing doesn’t need analysts to validate every campaign report. Finance doesn’t spend days reconciling mismatched numbers. When everyone works from governed sources, decisions tend to happen faster because validation is already built in.
AI readiness: Governance makes AI analysis usable in real-world workflows. When data quality, lineage, and access rules are clear, tools like Julius can generate more accurate insights instead of only misleading summaries. This prevents AI from amplifying bad data or surfacing sensitive information through generated reports.
Operational efficiency: I’ve found that clear governance gives business users enough confidence to answer their own questions. This reduces ad-hoc requests and frees data teams to spend time on the metrics and models that actually improve analysis quality.
Cost reduction: Governance reduces duplicated datasets, unused storage, and rework caused by conflicting numbers. I’ve seen teams save hours each week once they know which dataset is correct and where it lives. When business users can find trusted data quickly, they spend less time rebuilding reports and second-guessing results.
Risk management: Governance makes it clear where sensitive data lives, who can access it, and how that access is enforced. This matters for audits, but it matters even more for everyday analysis. Business users can explore data with more confidence without worrying that a simple query exposes information it shouldn’t.
Faster decisions: Teams move faster when they trust the data they’re working with. Marketing doesn’t need analysts to validate every campaign report. Finance doesn’t spend days reconciling mismatched numbers. When everyone works from governed sources, decisions tend to happen faster because validation is already built in.
AI readiness: Governance makes AI analysis usable in real-world workflows. When data quality, lineage, and access rules are clear, tools like Julius can generate more accurate insights instead of only misleading summaries. This prevents AI from amplifying bad data or surfacing sensitive information through generated reports.
Operational efficiency: I’ve found that clear governance gives business users enough confidence to answer their own questions. This reduces ad-hoc requests and frees data teams to spend time on the metrics and models that actually improve analysis quality.
13 Data governance best practices for 2026
These practices cover everything from getting organizational buy-in to automating day-to-day tasks. I’ve organized them to build on each other, but you don’t need to implement them in a strict sequence. What matters is understanding how each practice reduces costs, limits risk, or helps teams make decisions faster.
Here are the 13 best practices to implement in 2026:
1. Conduct a maturity assessment to build your business case
A data governance maturity assessment helps you understand how usable your data is for everyday analysis. It shows whether business users can answer common questions on their own and where unclear ownership, definitions, or access rules slow teams down or lead to unreliable results.
I treat this as a readiness check for analysis, not a paperwork exercise. When I work with teams, gaps often appear in places that directly affect business users and AI tools, like inconsistent metrics, unclear permissions, or confusion around which dataset to trust.
Start by asking teams a short set of practical questions:
Who decides who gets access to each dataset today?
How do teams handle conflicting data definitions between departments?
What happens when someone needs data they don't have permission to access?
How do business users decide which dataset or metric they should trust?
The answers help you build a clear business case by tying governance gaps to real costs, delays, and limits on analysis. I've seen this reveal why AI tools struggle when data lacks shared meaning or clear structure. When you address these basics, AI analysis tools work better with your data relationships and support more accurate results.
2. Secure executive sponsorship
Executive sponsorship determines whether teams can actually use governed data to answer questions quickly and consistently across the business. Without leadership support, governance work often loses priority, which leaves business users waiting on access, clarification, or validation before they can move forward.
Use your maturity assessment to connect governance gaps to problems executives already care about, like slow reporting, inconsistent numbers, and limits on self-serve analysis. For example, if your CFO worries about audit costs, show how clearer ownership and access rules reduce time spent preparing reports.
I’ve found executives respond better when governance is tied to faster decisions and fewer data bottlenecks, not just policy coverage. When leadership treats governance as a way to make data usable across teams, business users spend less time waiting on analysts, and analysis tools can work with a cleaner structure and shared definitions.
3. Define roles and responsibilities with real accountability
Data governance can fail when everyone thinks someone else is responsible. You need clear ownership at every level, from executives who set strategy to team members who enforce policies daily.
Start by assigning data owners for each major dataset or domain. Your finance director owns financial data, your marketing leader owns customer data, and your operations manager owns supply chain data. These owners make decisions about access, quality standards, and retention policies for their areas.
Then, identify data stewards who enforce those decisions across departments. Stewards handle day-to-day tasks like training team members, reviewing access requests, and resolving conflicts when two teams need different things from the same data. Make sure these responsibilities appear in job descriptions and performance reviews, not just meeting notes.
Clear ownership helps both people and AI tools work faster. Business users know exactly who to ask for data access, and AI analysis works more reliably when permissions and quality standards are consistently enforced.
4. Build a practical governance framework
A governance framework defines how decisions get made, who has authority over what, and how you escalate issues that cross departmental lines. Without this structure, every decision becomes a negotiation.
Your framework should cover:
Decision rights: Who approves new data sources and who grants access to sensitive data.
Escalation paths: What happens when teams disagree about data definitions or quality standards.
Accountability mechanisms: How you track whether people follow policies and what happens when they don't.
These elements matter for analysis because they define how quickly business users can access data and how confidently they can use AI tools without worrying about compliance violations.
I recommend keeping your framework simple because teams likely won't reference documents they can't read quickly. A two-page summary covering essential decision points might get used more than a comprehensive manual that takes too long to sift through.
5. Start small with a high-impact pilot
Start with a small, focused problem rather than trying to fix everything at once. Maybe your sales team wastes time each week reconciling customer data between your CRM and billing system. Or your marketing team can't analyze campaigns because they don't know which data source to trust.
Choose something painful enough that people notice when it improves. I prefer starting with data people already use daily rather than creating new processes. When the sales team sees customer data sync automatically between systems, they understand what governance does, and you build credibility for expanding to other areas.
6. Automate governance tasks to reduce manual work
Manual governance can create problems as your data grows. For example, when data teams spend hours classifying sensitive data by hand, governance can slow down work instead of enabling it.
I recommend using tools to automate the repetitive tasks that consume the most time. AI-powered platforms can scan your databases to find and tag sensitive information like customer emails or payment data. They can generate reports on who accessed what data and when, and enforce access policies automatically when someone joins or leaves a team.
This automation keeps data consistently tagged and up-to-date, so business users can analyze information without opening IT tickets and AI tools can work with clean, well-structured data.
7. Treat governance as a service, not a project
I've learned that governance isn't something you implement once and forget. Teams need ongoing support as they create new data sources, onboard new employees, or change how they work with data.
You can try setting up governance like an internal service desk. Make it easy for people to request access to data, ask questions about policies, or report problems with data quality. When governance feels helpful rather than restrictive, people actually use it instead of finding workarounds.
I suggest assigning specific people as governance contacts for different departments. This prevents the "I don't know who to ask" problem that leads people to just share data without checking first.
8. Invest in change management and communication
New governance policies can fail when nobody understands why they exist or how to follow them. You need a plan for getting people to change their behavior, not just announcing new rules and hoping for the best.
Start by explaining what problems governance solves for each team. Show marketers how governance helps them access customer data faster, demonstrate to finance how it reduces reconciliation time, and explain to operations how it prevents errors in supply chain data.
Create simple documentation that people can reference quickly. I personally like short videos better than long written guides for explaining common tasks like requesting data access or reporting data quality issues. People might watch a 2-minute video, but they probably won't read a 10-page manual.
9. Measure with business-focused metrics
Track metrics that matter to business leaders, not just technical stats about data quality scores or policy compliance rates. Executives care about results, so measure how governance impacts time, cost, and risk.
Good metrics include:
Time saved: How much faster teams find data or validate its accuracy.
Cost reduction: Money saved by avoiding compliance fines or reducing duplicate storage.
Faster decisions: How quickly teams can act because they trust the data.
Incidents prevented: Data quality issues caught before they impact customers.
10. Design for continuous improvement
Your first version of governance won't be perfect. Plan for regular reviews where you assess what's working, identify what people struggle with, and adjust policies or processes based on real usage.
Schedule quarterly check-ins with data owners and stewards. Ask what slows them down, what policies feel unnecessary, and where they see people working around governance instead of with it. These conversations reveal obstacles before they become major problems.
I recommend tracking common questions or complaints as signals for what needs improvement. If multiple teams ask the same question about a policy, that policy needs clearer documentation. If people keep requesting exceptions to a rule, maybe the rule needs adjustment.
11. Build governance into the tools teams already use
Governance can fail when people need to leave their normal workflow to check policies or request access. Build governance into the tools your teams already use daily rather than forcing them to visit a separate governance portal. If your analysts work in Slack or Microsoft Teams, let them search for data definitions or request access without switching apps.
Platforms like Julius surface data context and learned definitions alongside analysis, which helps business users understand what they’re working with and reduces the need to search through separate documentation. When governance sits inside existing workflows, people actually use it instead of working around it.
12. Leverage existing practices before building new ones
Your organization already has informal governance practices, even if you haven't formalized them. Someone already decided who gets access to financial data. Teams already have processes for maintaining databases and updating records. Find these existing practices and build on them rather than starting from scratch.
I recommend mapping out who currently owns different datasets and what processes they already follow. You might discover your finance team has strong data quality checks or your marketing team has clear naming conventions. Formalize what works and extend it to other areas instead of replacing everything with new processes that nobody asked for.
13. Build a data glossary with common definitions
Teams waste time arguing over what metrics mean or discover too late that they're measuring different things. A data glossary creates a single source of truth for business terms, so everyone uses "customer," "revenue," or "conversion" the same way.
Start with the terms that cause the most confusion. I've seen marketing and sales use "lead" to mean completely different things, or finance and operations calculate "revenue" differently. Document what each term means, how you calculate it, and which system holds the authoritative version.
I suggest keeping definitions in a place people can actually find them. If your team works in Notion, make the glossary searchable there. If they build reports in your BI tool, link definitions directly to the fields they're using.
5 Tools for data governance in 2026
The right tools can automate governance tasks, make data easier to find, and help you spot quality issues before they cause problems. These typically include analysis tools, business intelligence platforms, data catalogs, and quality monitoring systems.
Here are 5 tools that support governance in different ways:
Julius: An AI-powered data analysis tool that lets business users analyze data without technical skills by asking questions in natural language. We designed it to learn your data structure over time, which means you spend less time explaining table relationships and more time getting answers.
Tableau: A business intelligence platform with strong governance features including data lineage tracking, certification workflows, and role-based access controls. Teams can see where data comes from and who's validated it before building reports.
Power BI: Microsoft's BI platform includes built-in governance through sensitivity labels, data loss prevention policies, and integration with Microsoft Purview for compliance tracking. Works well if you're already in the Microsoft ecosystem.
Collibra: An enterprise data catalog that helps you document what data you have, where it lives, and who owns it. Includes workflow tools for managing data requests, policy enforcement, and collaboration between business and technical teams.
Monte Carlo: A data observability platform that monitors and resolves data quality issues like anomalies, schema changes, and freshness problems. It provides early detection and root cause analysis to prevent broken data pipelines from impacting business decisions.
Want to turn governed data into business insights? Try Julius
When you implement data governance best practices, you create the foundation for better decisions. But once those practices are in place, you need tools that let you work with that governed data.
Julius is an AI-powered platform that gives business users instant access to data insights. You can upload files or connect databases, ask questions in plain English, and get charts, reports, and answers without writing code.
Here’s how Julius helps:
Smarter over time with the Learning Sub Agent: Julius's Learning Sub Agent automatically learns your database structure, table relationships, and column meanings as you use it. With each query on connected data, it gets better at finding the right information and delivering faster, more accurate answers without manual configuration.
Enterprise-grade security: Julius maintains SOC 2 Type II, GDPR, and TX-RAMP compliance with continuous monitoring. Your data stays protected with industry-leading security standards while you analyze it.
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Catch outliers early: Julius highlights suspicious values and metrics that throw off your results, so you can make confident business decisions based on clean and trustworthy data.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
Direct connections: Link your databases and files so results come from live data, not stale spreadsheets.
Ready to see how Julius can help your team make better decisions? Try Julius for free today.
Frequently asked questions
Can you implement data governance without hiring new people?
Yes, you can implement data governance without hiring new people by assigning governance responsibilities to existing team members. Your finance director can own financial data, your marketing leader can own customer data, and your IT team can handle technical policies. Give people dedicated time to do governance work instead of just adding it on top of their current workload.
