Building a Data Management Strategy: Best Practices for Governance and Quality

As businesses generate more data than ever before, having a solid data management strategy has become more important than ever, not just for large enterprises; even small businesses, new departments, and anyone looking to use data to make better decisions can benefit from it.

Whether you're a business owner exploring how to use data to grow, a department leader fine-tuning workflows, or a new data analyst building your foundational knowledge, this guide will help you understand what a data management strategy is, why it’s so important, and how to get it right—especially when it comes to data governance and quality. 

What is a data management strategy?

A data management strategy is your organization’s blueprint for how it collects, stores, accesses, and uses data. It’s about making sure your data is available, accurate, secure, and used effectively to support your goals.

A data management strategy helps you answer questions like:

• What data should we have?
• Where does it come from?
• How do we ensure it’s reliable?
• Who gets to access it, and how?
• How do we keep it secure and compliant?

By creating a thoughtful, careful strategy, you’ll help your team use data in a way that is consistent, responsible, and efficient.

Why data governance and quality matter

Two of the essential pillars of a successful data management strategy are data governance and data quality.

Data governance
Refers to the rules, processes, and people involved in managing data responsibly. By having good data governance in place, you ensure that the right individuals have access to the right data, that usage follows policy and compliance requirements, and that your organization has a clear structure for ownership and accountability.

Data quality
The quality of your data is essential to ensuring it is accurate, complete, consistent, and up to date. Poor-quality data leads to poor decision-making, wasted time, and missed opportunities.

Without strong governance and high-quality data, even the most advanced tools won’t deliver value.

Common challenges when managing data

Getting started with data management often reveals pain points. Recognizing these early can help you plan more effectively.

Here are some of the most common challenges:

• Siloed data: Teams using different systems often don’t share data, making it difficult to get a unified view.
• Manual processes: Reliance on spreadsheets and manual entry increases the risk of errors and inconsistency.
• Lack of clear ownership: Without clear roles, it’s hard to know who is responsible for maintaining data.
• Inconsistent definitions: Different teams may define key metrics, like “active user” or “qualified lead”, in conflicting ways.
• Shadow IT: Individuals or departments using unapproved tools can create security and compliance risks.
• Scaling issues: As data volume grows, it becomes harder to maintain structure, quality, and access controls without automation.

Key components of a strong data management strategy

To create a reliable, scalable data management strategy, you’ll have to do more than just collect data. It requires thoughtful planning across multiple dimensions—from setting the right goals to ensuring your systems work together seamlessly. 

Below are the key components every team should consider when building or refining their strategy.

1. Define your data goals

Start with your business or team objectives. Are you trying to understand customer behavior, optimize operations, or improve reporting? Defining your goals ensures your strategy is built to support real outcomes.

Use frameworks like SMART goals or Objectives and Key Results (OKRs) to tie your data efforts to specific outcomes. For example:

• Objective: Improve customer retention
  
• Key Result: Increase six-month retention rate by 10%
• Data needed: Customer activity logs, churn rates, support ticket history

When you align data initiatives with business impact, it’s easier to prioritize resources and measure success.

2. Understand your data sources

List where your data comes from: CRMs, marketing platforms, spreadsheets, surveys, financial systems, website analytics tools, and more. Knowing your sources helps you identify what data is available, what’s missing, and where inconsistencies might originate.

This step also helps you flag areas where you may be duplicating data collection or relying on sources that are no longer trustworthy or up to date.

3. Create a data inventory

Once you know your sources, take it a step further by building a data inventory. This is a centralized list or catalog of the data sets your organization uses, including details such as:

• Source system
• Owner or steward
• Type of data (structured, unstructured)
• Sensitivity or compliance level
• Update frequency
• Primary business use

A basic inventory helps you assess data quality, identify redundancies, and prioritize governance or cleanup efforts. Even a simple spreadsheet is a great place to start.

4. Establish data governance practices

Set up governance by identifying:

• Ownership: Who is responsible for maintaining each data set?
• Access control: Who can view, edit, or delete data, and under what circumstances?
• Policies: What standards and rules govern how data is collected, stored, and shared?
• Compliance needs: Are there regulatory requirements (e.g., GDPR, HIPAA, CCPA) that have to be embedded into your data processes?

Governance adds structure and accountability, reducing risk and improving trust in your data.

5. Define roles and responsibilities

To make governance actionable, clearly define who is responsible for different aspects of data management. This doesn’t require a large team, just clear expectations and communication.

Typical roles might include:

• Data owners: Business or technical leads accountable for specific data sets
• Data stewards: People responsible for data quality, integrity, and definitions
• Analysts: Users who access, analyze, and share insights
• IT or security teams: Managers of access control, security protocols, and infrastructure

Documenting responsibilities makes it easier to resolve issues and scale your data efforts.

6. Focus on data quality from the start

To ensure data is usable and trustworthy:

• Use validation rules when collecting data, such as having required fields or using format checks
• Clean and deduplicate data regularly
• Define standards for naming, formats, and structure
• Schedule audits to catch quality issues before they snowball

Data quality isn’t just about fixing errors—it’s about designing systems that prevent them from happening in the first place.

7. Organize your data storage and architecture

Decide how data will be stored, whether in cloud data warehouses, traditional databases, or data lakes. Consider:

• Scalability: Can your infrastructure grow with your business?
• Performance: Can users query data quickly?
• Accessibility: Can the right people get what they want without IT bottlenecks?
• Security: Is access controlled and logged?

Choose a structure that supports both flexibility and control.

8. Plan for data integration and interoperability

Your strategy should account for how systems will talk to each other. As organizations grow, they tend to use more tools and platforms, creating silos if integration isn’t planned early.

To ensure interoperability:

• Map out which systems will share data (e.g., CRM ↔ email marketing platform)
• Choose tools with robust APIs or native connectors
• Define common data formats or standards for smoother transfers
• Document transformation rules or sync schedules

This enables a unified view of your data across the business and helps reduce manual work or conflicting insights.

The role of metadata in data management

Metadata is the background information about your data. It describes what your data is, where it came from, who created it, and when it was last updated.

Proper metadata management helps your team:

• Understand the context of each data set
• Trace the source of issues
• Improve discoverability through data catalogs
• Ensure data lineage and compliance

Using metadata tools, you can reduce confusion, avoid duplicate work, and strengthen data governance overall.

Make data accessible—but secure

Data only delivers value when it’s used, but it must be used responsibly. Accessibility and security should go hand in hand. Making data accessible doesn’t mean opening it up to everyone; it means the right people can get the data they want, when they want it, without unnecessary barriers.

Start by defining access roles and permissions. 

For example, analysts might look for raw data access, while executives look for curated dashboards. Use identity and access management (IAM) tools to grant role-based access and prevent unauthorized usage. This not only protects sensitive data but also reduces noise for users who aren’t looking for everything.

Make it easy for users to find and understand data. 

A searchable data catalog with clear descriptions, column definitions, and data lineage can help. Promote a self-service model, where non-technical users can explore vetted, secure data through BI tools—without having to submit a request every time.

Establish clear logging and monitoring protocols. 

Regularly review who accessed what data and flag unusual patterns. This protects your data while maintaining transparency and accountability.

Invest in tools and training

Technology and people are both critical for executing a strong data management strategy. Without the right tools, teams can't implement governance or quality controls. And without training, the tools often go underutilized or misused.

Start by selecting tools that match your organization’s scale, skills, and needs. For smaller teams, this might mean choosing a cloud data warehouse with built-in access controls and simple dashboards. As you grow, you can add specialized tools for data cataloging, pipeline monitoring, or compliance reporting.

Equally important is investing in training. Help your team understand not just how to use tools, but how to think about data. Offer onboarding sessions for new employees, periodic refresher workshops, and documentation that demystifies your data stack and policies. Encourage cross-functional learning so that business users feel confident using analytics tools, and data specialists understand business priorities.

Fostering a data-literate culture makes your strategy more sustainable and drives adoption across the organization.

Best practices for data governance

By having a strong governance structure in place, you create the environment your data requires for it to thrive. It ensures your data is managed consistently and responsibly, while reducing risk and confusion. 

Whether you're just starting out or formalizing existing processes, these best practices provide a roadmap for building governance into your everyday operations.

1. Start small and scale. Don’t try to govern everything at once. Begin with critical data sets and expand.
2. Document everything. From data definitions to policies and responsibilities, good documentation ensures clarity.
3. Build a governance team. Even a small team with clear roles can maintain structure and consistency.
4. Automate where possible. Use tools that support data cataloging, metadata management, and automated policy enforcement.
5. Communicate the value. Help stakeholders understand how governance improves accuracy, trust, and efficiency.

Best practices for data quality

Good decisions require good data. But data doesn’t clean or validate itself—quality must be designed into your processes from the start. These best practices help you make quality part of your culture, not just a one-time fix.

1. Set quality standards. Define what “good data” looks like in your context.
2. Use quality metrics. Track completeness, accuracy, consistency, and timeliness.
3. Integrate quality checks into workflows. Make quality control part of your data pipelines, not an afterthought.
4. Act on issues quickly. Address errors at the source rather than patching them downstream.
5. Promote a data quality culture. Encourage teams to maintain high-quality data.

Getting started checklist

Use this checklist to put your data management strategy into motion:

• Identify your most important business goals
• List all existing and potential data sources
• Define key terms and metrics to avoid inconsistencies
• Assign ownership for data assets and processes
• Draft basic access policies and usage guidelines
• Choose a starting set of tools for governance and quality
• Begin documentation with a simple data dictionary
• Schedule monthly or quarterly quality reviews

You don’t have to have a perfect setup to start—just a structured, repeatable process.

Turn strategy into action with the right platform

A well-designed data management strategy is not about perfection—it’s about creating momentum. When you prioritize governance and quality, you lay the foundation for decisions you can trust, systems that scale, and a team that feels confident using data to drive results.

The next step? Put your strategy into motion with a platform that brings it all together.

Domo gives you the power to centralize your data, apply governance controls, automate quality checks, and empower users across your organization with real-time insights—all from a single, intuitive interface.

Whether you're just getting started or looking to mature your data approach, Domo helps you connect strategy to execution, fast.

Ready to build a smarter, more scalable data practice? Start your journey with Domo today.