Generative AI tools like Microsoft 365 Copilot are gamechangers for saving time and boosting productivity. But let’s be honest – sometimes the responses don’t quite hit the mark.
If you’ve ever received less-than-helpful (or worse, inaccurate) results, it’s tempting to blame the AI. The truth, though, is that Copilot’s accuracy depends entirely on the data it’s working with. If that data is cluttered with outdated or irrelevant information, Copilot’s responses can miss the point.
The good news? You can significantly improve Copilot’s performance by implementing smart retention and disposition policies to ensure it works with clean, relevant, and compliant data.
Let’s explore why this matters, and how to get started.
Why does Copilot sometimes struggle?
There are a few reasons why Copilot might deliver unexpected or off-target results. These are the most common ones that we’ve seen so far:
- Low quality data input: If your data is incomplete or inaccurate, Copilot’s responses will be, too.
- No context: Copilot relies on well-organised and labelled data to understand the bigger picture.
- Natural language hiccups: Sometimes, nuances in language can trip up even the most advanced AI.
- Limitations in training data: If Copilot hasn’t been exposed to your industry’s terminology or it’s working with outdated knowledge, its responses may lack relevance.
While AI models like Copilot improve over time, the reality is that they will always reflect the quality of the data they’re given. No matter how intelligent your AI, if your systems are full of irrelevant or obsolete data, those issues will persist.
How retention and disposition policies can help
This is where data lifecycle management comes into play. By defining what data to keep (retention) and what to delete or archive (disposition), you can ensure that your systems stay clean, organised, and AI-friendly.
Retention policies (keeping the good stuff)
What they do: Retention policies determine how long specific data needs to be kept in your organisation.
Why they help: They ensure that important information is securely stored and remains accessible for decision-making, compliance or historical reference.
Pro tip: Use tools like Microsoft Purview to automate retention schedules and categorise data by importance.
Disposition policies (clearing the junk)
What they do: Disposition policies outline the procedure for safely disposing of data that’s no longer needed – old, irrelevant, or redundant files, emails, media etc.
Why they help: Less ROT (redundant, obsolete, trivial data) reduces risk, and creates less noise for Copilot to sift through, giving you cleaner, more relevant AI responses.
Pro tip: Regular data audits and automated clean-up tools can make disposition dramatically easier and more successful.
Why data lifecycle management matters for AI
Think of Copilot as your incredibly clever (but occasionally literal) assistant. If you don’t clean up your data, you’re leaving it to guess what’s useful. It might pull insights from five-year-old sales reports, not realising they’re no longer relevant, or even surface outdated and inappropriate terminology from long-retired customer service scripts.
A little digital housekeeping goes a long way towards ensuring your AI is working with the freshest, most useful information, and avoids potentially embarrassing (or downright damaging) missteps.
Getting started with Microsoft Purview
If data lifecycle management sounds overwhelming to you, you’re not alone. The good news is that tools like Microsoft Purview can help automate a lot of the heavy lifting.
With Purview, you can:
- Apply retention policies automatically based on data type or sensitivity.
- Flag and clean up ROT data to declutter your systems.
- Stay compliant with data protection laws like GDPR or CCPA without manually sifting through every file.
How Cloud Essentials can help
At Cloud Essentials, we understand that refining your data for Copilot isn’t just a technical challenge – it’s also a strategic one. Creating a data lifecycle management strategy requires time, expertise, and, often, cross-departmental collaboration. That’s where we come in.
Here’s how we can support you
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- Pinpointing relevant data: We help identify which data is critical and which is clutter. Our detailed analysis ensures that Copilot works with only accurate, up-to-date information.
- Balancing retention and deletion: Striking the right balance between keeping essential data and removing irrelevant information is key. We create retention and disposition policies tailored to your needs.
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- Ensuring compliance: With evolving regulations like GDPR and CCPA, compliance is a moving target. We implement policies that meet these standards and ensure Copilot doesn’t surface inappropriate or restricted data.
- Maintaining data integrity: High-quality, reliable data is essential for Copilot. We implement robust security measures and continuous monitoring to prevent corruption or unauthorised access.
- Maximising resources: Implementing lifecycle strategies takes time and effort, but our pragmatic approach simplifies the process. Using tools like Microsoft Purview, we automate much of the work to free up your team for other priorities.
Bringing it all together
If you want Copilot (or any AI) to deliver its best, your data has to be in great shape. Think of it as a two-way relationship: the better your data, the smarter and more accurate your AI will be.
By implementing smart retention and disposition policies, you’re giving Copilot the clean, relevant data it needs to shine.
Ready to take control of your data? Let’s chat. At Cloud Essentials, we make complex challenges simple, so you can focus on what really matters. Get in touch today!
