Adopt Copilot with Confidence: Step 3- Govern & Optimize for Success

Part 3 of a 3 Part Series

By Glen Day

Generative AI technologies, such as Microsoft Copilot, promise transformative benefits for businesses. Yet, success hinges on more than just deploying AI—it requires a strong foundation in data governance. As the final piece in our three-part “Adopt Copilot with Confidence” series, this blog explores how to govern and optimize your data estate to maximize Copilot’s impact.

In our earlier blogs, we covered two essential steps:

1. Establish Your Data Catalog

• Understand where your data resides.
• Understand the business context of each data asset.
• Identify which data is sensitive.

2. Fortify Your Data

• Assess your data risks.
• Remediate access risks and enhance data protection controls.
• Enhance policies to be AI-ready.

Now, we focus on the third and critical step: Govern & Optimize. This stage is where the magic happens—reducing data clutter, improving quality, and addressing abandoned data, a unique focus of NVISIONx, to ensure Copilot processes only what matters.

The Challenge of Data Overload

Today’s organizations manage enormous volumes of data, much of which is redundant, outdated, or trivial (ROT)—a common industry term. However, NVISIONx extends this definition with the inclusion of abandoned data—data from prior employees, contractors, or interns who have left the company, yet their personal data stores still exist without clear ownership or purpose, redefining it as redundant, outdated, trivial, or abandoned (ROTA). These unmanaged data estates not only inflate storage costs but also present avoidable security and compliance risks. Left unchecked, ROTA data can derail AI initiatives like Copilot by introducing unexpected risks, impairing analysis accuracies, and lowering performance.

According to various studies, 30% to 80% of corporate data is ROTA. The Veritas Global Databerg Report further supports this, revealing that, “… 85% of stored data is either dark or ROTA, with 33% identified as ROTA and the remaining 52% as dark data.” Without robust governance, businesses risk significant inefficiencies and potential compliance failures. For example, the FTC recently fined a notable web pharmacy $1.5 million for over-retention of expired sensitive customer data, highlighting the regulatory and reputational risks of failing to manage data effectively. Tackling this challenge is crucial to realizing AI’s potential.

1. Identify ROTA Data

Before optimization can occur, ROTA data must be identified. Advanced data governance platforms play a pivotal role by leveraging automation to:

- Uncover dark data hidden across the organization.

- Identify and analyze ROTA information, integrating records retention insights (derived from the prior context classification efforts outlined in Blog 1).

This ensures you start with a comprehensive understanding of your data estate.

??????2. Remove What You Don’t Need

Removing ROTA data is not just about decluttering—it’s about enabling compliance and operational efficiency. In terms of defensible data disposal, companies must also preserve data that is on active legal hold, so coordination with legal and records management is essential. By setting clear deletion criteria aligned with regulatory and business requirements, organizations can:

• Be confident that records retention and legal hold mandates are properly addressed to prevent inadvertent destruction.
• Find what they need on demand and trust they have the correct versions.
• Minimize storage and management costs.
• Lower security risks associated with excess data.

????3. Optimize Remaining Data for AI Readiness

Streamlined data leads to better AI outcomes. High-quality, contextually relevant data ensures that Copilot’s outputs are accurate, actionable, and aligned with business needs. It’s not just about less data; it’s about better data.

Key Capabilities for Effective Data Governance

To successfully govern and optimize your data, you need tools and capabilities that go beyond the basics. Here’s what to look for:

• Automated ROTA Identification: Automation reduces time and effort in finding and classifying redundant data.
• Contextual Classification: Business-centric classifications make data actionable, enabling precise governance.
• Workflow Automation: Streamlined workflows ensure collaboration across departments for efficient data management.
• Continuous Monitoring and Optimization: Governance is not a one-time task. As your data estate evolves every minute, ongoing oversight ensures sustained compliance and efficiency.

Benefits of Governance and Optimization

When done right, governance and optimization deliver substantial business benefits while also providing business leaders confidence and trust in their GenAI initiatives:

• Improved Security: Protect sensitive data by eliminating unnecessary exposure.
• Streamlined Compliance: Simplify audits and ensure regulatory adherence.
• Cost Savings: Reduce storage and operational expenses by removing ROTA data.
• Enhanced AI Performance: Deliver accurate, actionable insights to users for optimal results.

Conclusion

With the right approach to governance and optimization, your organization can confidently adopt Microsoft Copilot and unlock its full potential. By addressing data clutter and focusing on quality, you’ll set the stage for a transformative AI journey that drives both innovation and business value.

As we conclude this three-part Adopt Copilot with Confidence series, I want to personally thank you for your interest and engagement. Your contributions and curiosity drive meaningful discussions about AI readiness and data governance. Together, we can pave the way for successful AI adoption and transformative business outcomes. If you have questions or wish to share your own experiences, please don’t hesitate to reach out. Thank you for joining us on this journey.