Over 50% of organizations have paused their Copilot initiatives. Why?

Because of data quality and data governance concerns.

Generative AI is powerful, but when you feed it bad data, it generates bad responses. “Garbage in, garbage out,” as we say. Inaccurate or irrelevant responses hurt user satisfaction, decrease productivity, and lead to failed GenAI projects.

Microsoft Copilot is no exception to this problem. Think of all the duplicate and outdated data in your Sharepoint instance. Copilot uses all of it, which leads to poor outputs and halted Copilot initiatives. 

(We’ve written extensively on Sharepoint data issues, specifically how to prepare Copilot data and how to handle hallucinations and outdated and duplicate data.)

7 Unexpected Causes of AI Hallucinations Get an eye-opening look at the surprising factors that can lead even well-trained AI models to produce nonsensical or wildly inaccurate outputs, known as “hallucinations”.

We are addressing this problem with Shelf’s new Copilot Studio integration. Along with our Sharepoint integration, Shelf is the key piece that prevents bad data from becoming hallucinated and inaccurate Copilot responses.

At Shelf, we have spent over a decade helping organizations solve enterpise data quality challenges. With our Copilot Studio integration, you can now see exactly which Copilot’s responses were negatively impacted by poor quality data. The platform gives you an audit trail to help you address poor Copilot answers at the source.

Get Better Answers From MS Copilot with Shelf’s Copilot Studio Integration: image 3

Copilot’s Inherent Limitations

Imagine an employee asks Copilot about complaint processing timelines. In the organization’s Sharepoint instance, there are two documents that address this question, but they provide conflicting timelines. One document says 15 days the other 25 days. 

In a case like this, Copilot would fail to provide the user with accurate information because the nature of the data prevented it. These cases are common. What’s worse, the Copilot implementation team only discovers the error when users complain about it. By this time, the damage is done. 

Now imagine a similar scenario with confidential company information or personally identifiable information of your users, customers, or patients.

With Shelf’s Copilot Studio integration, you are immediately notified when bad data is being used by Copilot. You can see the exact conversations that are problematic and trace answer issues back to the data issues that caused them.

Shelf platform also features 22 different data quality diagnostics that analyze the quality of a given data source from different data quality perspectives. This gives you and your team the opportunity to see exactly which documents (and even the sections of those documents) are problematic and are no fit for Copilot consumption.

Ultimately, this empowers your team to control the quality of the data that fuels Copilot and directly impact the quality of Copilot’s answers.

How Shelf’s Copilot Studio Integration Works

Step 1: Connect your Data Source 

Connect the data source you want assessed for the presence of problematic documents. This can be Sharepoint or any other content repository that your organization uses. (Learn more about Shelf’s integrations.)

Step 2: Connect MS Copilot Conversations

Next, connect Copilot’s conversations so Shelf can monitor and analyze the quality of answers that the Copilot provides to its users.

Step 3: Fix, Filter or Enrich Problematic Data

Shelf gives you three options to address problematic data.

  1. Fix it at the source. For instance, you could update a document with the correct information so Copilot has good data for future conversations. 
  2. Filter it out from all Copilot conversations. You might filter out documents that contain internal or private information that Copilot should never access. 
  3. Enrich data with greater context to help Copilot understand your data better. For instance, you might provide descriptions for company specific acronyms that the Copilot does not know out-of-the-box.

Step 4: Continuously Monitor

Over time, data quality naturally falls and requires regular updates and maintenance. Similarly, the volume of Copilot conversations grows as users ask more questions. Shelf’s GenAI conversation dashboard continuously analyzes your data quality and notifies you when it discovers an issue. 

Schedule a Demo

Generative AI is transforming how we work, which means ensuring data quality isn’t just a best practice—it’s a necessity. Shelf’s Copilot Studio integration gives you and your team full control over the quality of information feeding Microsoft Copilot. Don’t let poor data compromise your Copilot initiatives. 

With Shelf, Copilot delivers accurate, reliable responses that drive productivity and user satisfaction. Schedule a demo today.