What is data governance?
A good data governance program is the key to data safety and a successful launch of artificial intelligence tools at your company.
What is data governance: Key takeaways
Data governance definition and importance: Data governance is a strategic framework for managing, protecting, and using data across an organization.
Key pillars: Effective data governance includes assigning data ownership, developing metadata management, and instituting data quality and access controls.
Implementation steps: Implementing data governance involves forming an AI governance board, designating AI officers, training programs, leveraging cloud AI services, and partnering with IT consultants.
What is data governance, and why is it so important for SMBs to have it when implementing AI/BI tools?
Data governance is the strategic framework that a business uses to define how data is managed, protected, and used throughout the organization. In an era where business intelligence can parse data like never before and AI agents can create whole new workflows; data governance is now mission critical for companies of any size.
Perhaps that’s why Gartner predicts that by 2027, 60% of organizations will fail to realize success from AI investments because of poor data governance. If your company is considering adopting AI tools like Microsoft Copilot for M365, purchasing licenses for AI-driven software as a service (SaaS) tools related to your industry, or using AI/BI agents on your internal/external facing systems, data governance will be the key to their success. After all, AI/BI is only as smart as the data that’s fed into it.
If you’re considering rolling out AI tools within your organization, you’ll need to have a fully formed data governance program first. Let’s dig deeper into what makes a good data governance program and how to implement it smoothly at your company.
Data governance definition: What should a data governance program include?
At its root, data governance is all about putting organizational information to work for you. But before you can do that, it pays to think through your risks. This is the critical role that data governance plays for organizations looking to add the latest artificial intelligence engines to their software stack. Data governance provides a framework that allows you to use the tools effectively and safely, while complying with your company’s regulatory requirements.
Most data governance programs include the following: data ownership policies and access management, Ai Governance boards, written procedures/policies, and employee training on proper use of AI/BI tools.
Data governance: Why it’s the key to a successful AI/BI launch
Current data storage protocols simply aren’t designed for AI agents. In the past, documents were often stored with the most recent ones coming up to the top and a huge backlog of old documents hanging around in your system for years. An AI agent crawling this type of data configuration is likely to get as many outdated results as accurate ones.AI will also allow anyone in your system to search much broader databases than ever before. To make artificial intelligence and business intelligence tools work in your system, you’ll first need to answer the question of what data should be crawled and which employee teams should have access to those materials. This can be a tricky conversation for most organizations. Yet, the work is worth it. With a little planning, you can safeguard your most sensitive data, reduce confusion in your workflows, and improve your overall results and AI-driven productivity.
Before you install any AI capabilities in your systems, we recommend including these main areas in your data governance program.
What is data governance? The four key pillars of data governance programs
No. 1: Data ownership and stewardship
Every piece of organizational data should have a clear owner. The owner of that data record should be assigned responsibility for the accuracy, security, and the ethical use of that data. Consider compliance, and whether the sharing of this data would create issues, such as violating health data security protected by the health standard HIPAA or government defense contract information protected by the manufacturing standard, CMMC. With a data steward assigned to these files, the organization has an operational champion who ensures that data policies are followed and that data remains clean and usable across departments.
No. 2: Metadata management
Put simply, metadata is “data about your data.” This is the underlying information about the files that describes the origin, structure, and context with the information. When your system shares space with AI agents, having clear nomenclatures for metadata will help your team understand what the information is, where it came from, and how it should be used. The “guideposts” about data are particularly critical for AI systems that require context to make decisions about your data.
No. 3: Data quality and lifecycle controls
Poor data quality leads to poor results. The processes surrounding data management are crucial. For this reason, your organization needs defined processes around what data is stored, how it is revised, and how long it lives in your system before it’s automatically archived or deleted.
No. 4: Access and security controls
An organization needs to define role-based access to various kinds of data, such as employee salaries or customer home addresses. Who can access what data, and under what conditions? Through what systems can the data be accessed? How will you verify their credentials? Governance frameworks define role-based access controls, encryption standards, and audit trails to prevent unauthorized use and ensure compliance.
So with all this in mind, how should your organization proceed in developing an effective data governance program? Let’s break down some of the common best practices companies are employing as on their journey to data governance.
Creating a data governance framework in preparation for AI/BI launches: How to get started
If you’re interested in rolling out new AI and business intelligence tools at your company, group effort is required. You’ll need leadership from your C-suite in addition to representation from every department within your company to fully execute new data handling procedures and training programs. Here are the first steps to take.
Step one: Form an AI governance board
Bring together legal, compliance, and cybersecurity experts to help assess risk levels for the different types of data your company handles. This board can help establish recommended access levels for different kinds of data, and put together the rudimentary process concerning data storage, metadata nomenclature, and archiving procedure.
Once this team has a process in writing, roll out these new procedures to each of the department heads in your company. They can take these new procedures to their reports to ensure the new rules are workable and well understood, and any changes to existing cybersecurity/operational plans, policies, and procedures are made. Within each department, decisions can be made about which files will be considered recent and searchable, and pre-emptive archiving can take place before AI tools are introduced.
Step two: Designate AI officers
In advance of the deployment, designate AI officers throughout the company who can help oversee deployment, monitor usage, and enforce governance protocols. These officers can help answer questions and flag issues as AI usage continues in your company.
Step three: Implement training programs
Whenever new AI tools are introduced, ensure that staff is given instruction on how to responsibly use the data within the system. Explain what data can and can’t be searched using the tools, as well as best practices for maximizing their productivity with AI. Take this time to explain the company’s AI acceptable use policies and make sure all employees sign them.
Step four: Leverage cloud-based AI services
Cloud platforms can offer scalability and built-in compliance features, which is a great shortcut for small and midsize businesses looking to jump into AI quickly. Cloud software and storage can outsource a great deal of the heavy lifting, offering good server maintenance and storage capabilities. However, it’s best not to rely on them completely, as information often travels to and from cloud platforms. Thorough cybersecurity and compliance reviews should be done, regardless.
Step five: Partner with industry-specific IT consultants
Small and midsize businesses often need to tailor their AI solutions to the industry verticals where they work, such as legal, finance, and healthcare. Because of this, we recommend partnering with an MSP that has a high level of experience in your industry. They can help you bridge the gap between generic AI capabilities and industry-specific needs. A premium MSP can also ensure that your compliance operation is up to date with best practices in your industry vertical. This is critical as you begin your AI journey.
Data governance framework: Where to go for free data governance roadmaps and resources
It’s still early days for AI, and as of this writing in 2025, many companies are feeling their way as they navigate this new technology. Fortunately, world governments are trying to get ahead of the issue by publishing free guidelines and AI roadmaps for small and midsize businesses. These guidelines are comprehensive and cover many legal, ethical, and compliance-oriented issues around using AI in business. The right MSP can help you decipher these suggested data governance frameworks and how they apply best to your organization. Here’s where you can find them.
- NIST AI Risk Management Framework (AI RMF). Developed by the U.S. National Institute of Standards and Technology, the AI RMF provides a structured approach to managing AI risks across governance, data integrity, and system safety. The accompanying AI RMF Playbook breaks down each governance domain into practical steps for SMBs, including vendor due diligence, internal policy creation, and deployment safeguards.
- America’s AI Action Plan. Released in July 2025, this federal roadmap outlines the U.S. government’s approach to AI governance, emphasizing risk management, transparency, and ethical deployment. While this doesn’t give advice to SMBs specifically, it is a useful read for companies looking to do business with the U.S. government.
- World Economic Forum: AI Governance Alliance. The WEF’s “Industries in the Intelligent Age” report series offers a comprehensive roadmap for businesses and governments to adopt and scale AI. It includes sector-specific recommendations and governance models tailored to SMBs.
- OECD AI Principles. Adopted by more than 70 jurisdictions, the Organization for Economic Cooperation and Development’s (OEDC) AI Principles emphasize transparency, robustness, and human rights protections. These guidelines are designed to help governments and businesses—including SMBs—deploy AI responsibly.
Ready to begin your AI journey? Integris can help.
Integris is a premium national managed IT service provider specializing in helping SMBs get enterprise-level IT at scale. We’d love to help you with all your software management, cybersecurity, data governance needs, and more. We can help you put together a data governance framework for your next AI adventure. Contact us today for a free consultation.
