By Glen Day
Generative AI (GenAI) has rapidly moved beyond initial hype to become one of the most transformative technologies of our time. This evolution is underscored by solutions like Microsoft Copilot, which is revolutionizing how businesses leverage AI by seamlessly integrating advanced automation and intelligence into day-to-day operations. GenAI is no longer just influencing creativity and problem-solving; it’s redefining how organizations function, optimizing workflows, and enhancing decision-making. As a result, it is reshaping entire business models and driving a new era of digital and human collaboration that elevates productivity and innovation.
Microsoft Copilot is revolutionizing how businesses use AI by integrating advanced automation and intelligence into day-to-day operations. However, organizations have faced significant barriers when deploying Copilot. Specifically, data security concerns have halted many deployment projects at the pilot stage.
A recent Garter study surveyed 132 IT leaders and found that while 60% of respondents have started pilots to deploy Copilot, only 6% reported finishing their pilot and actively moving to a large-scale deployment. Around 64% of respondents shared their concerns with data oversharing, leading to delayed rollouts due to information governance and security risks. However, many organizations face significant barriers when deploying Copilot, as data security concerns often stall projects at the pilot stage due to fears of data breaches, compliance issues, and uncertainty around safeguarding sensitive information in AI systems.
Why Data Readiness Matters for Copilot Deployment
Microsoft has been clear about the importance of data governance for organizations preparing to deploy Copilot. Successful Copilot deployments require a three-step approach:
- Establish Your Data Catalog: Understand what data you have, where it’s stored, and if it’s sensitive. Knowing the business context of each data asset is essential for realizing Copilot’s value.
- Fortify Your Data: Based on the contextual identification of the sensitive data, evaluate the effectiveness of your installed data protection controls against your policies and standards to determine if the controls are actually keeping you safe.
- Govern & Optimize: Reduce data clutter by removing redundant, outdated, and trivial (ROT) data. This ensures that only high-quality, relevant data is used for AI processing, enhancing the performance and accuracy of Copilot.
In this three-part blog series, I’ll provide practical insights and advice on each of these crucial steps to show you how to quickly adopt and extract Copilot’s business value and promise.
Step 1: Establish Your Data Catalog
Before Copilot can effectively operate within your environment, it’s essential to ensure your data is well-organized, well-understood and accessible. Preparation involves knowing what data you have, where it’s stored, and how it will be managed for Copilot’s processing. Easier said than done, right?
Most corporate leaders have a love-hate relationship with data. It’s fantastic when you can leverage specific data sets to generate value, wealth, and market recognition. But the reality is that much of a company’s data is poorly managed, often leading to embarrassing breaches, costly compliance fines, and unwanted headlines. For many business and technology professionals, the sheer volume of corporate data can feel overwhelming, making it seem nearly impossible to control and manage effectively.
Here are my insights of the top 3 reasons why data remains out of control:
- Lack of visibility: When organizations cannot see their data or understand how it is being used, it is difficult to make informed decisions about how to manage and protect it. If valuable data (e.g., privacy, trade secrets, or sensitive financial information) is not reliably managed and tracked, it will likely result in a material data risk or compliance concern.
- Data silos: Many organizations have data scattered across different systems and departments, which makes it difficult to manage and secure. This results in duplicate data, inconsistent data quality, and difficulty in finding data when it is needed.
- Lack of governance: Without a practical data governance framework, supported by policies and procedures, it is impossible for organizations to manage data in a consistent and transparent manner. This can lead to confusion around who owns and is responsible for data, as well as how it should be managed and secured.
“It's the blind spots that gets you. If you don't know what you don't know, that's where you're going to get caught.”
- Risk Executive, F500 Global Media Company
There are numerous data governance technologies available, but many fall short of delivering a truly enterprise-wide approach. This often leads to increased time, effort, and resources with disappointing or even failed results. Moving forward, leaders must demand more from their vendors and advisors. This includes gaining a deeper understanding of how these solutions will be deployed in phases, the time and resources required, the supporting operational model, and the tangible outcomes they can expect to achieve.
To assist in selecting the right data governance platform, here are the key capabilities needed to ensure the solution you choose will simplify and accelerate your trusted Copilot or other GenAI adoption objectives:
- Complete Data Inventories- As opposed to data discovery tools that search and find whatever data you’re looking for, tools that conduct complete data inventories, which also informs you of data that you did not know existed (“Dark Data”), is crucial for establishing an enterprise data catalog. What you don’t know can result in poor decisions or incomplete outcomes. The ideal solution would accommodate any data, regardless of where it is stored, and in short time frames, regardless of how large your data estate may be.
NVISIONx Accounts for Every Data Asset to Eliminate Dark Data Risks
- Contextual Classifications- After inventorying your data, each file and database table should be classified according to its sensitivity level and business context. Effective classifications go beyond labels like “Confidential” or “PCI” and should clearly convey why the data is sensitive in business terms. This context makes classifications more actionable, allowing your organization to apply appropriate protections and governance measures.
NVISIONx’s Contextual Classifications are Business-centric and Actionable
- Directory Services Integrations- Trying to identify and assign data ownership or accountability through interviews or surveys will be ineffective. Assume that “… users don’t know what they don’t know…” and you’ll be more right than you may expect. The right data governance solution would enable the integration of any of your various directory services to automate the association of which business unit owns or knows what data. Every file and database should have a clear owner.
NVISIONx’s Integration with Directory Services Simplifies Data Ownership and Accountability
- Workflow Automation- Managing data classifications of large and varied data can be challenging and time consuming. Workflow automation capabilities enable process efficiencies and close collaborations across various business and technology stakeholders is crucial. Data governance is a team sport that requires each player to play their role.
NVISIONx’s Workflow Automation Simplifies Collaboration Across Business and Technology Teams
What’s Next?
In the next installment of this blog series, I’ll dive into Microsoft 2nd step for successful Copilot adoption- Fortify Your Data. I’ll explore how to manage access risks, protect sensitive information, and strengthen your data security framework to support a secure Copilot deployment.
Stay tuned for more practical insights on preparing your organization for AI success!