Unlocking the Potential of Unstructured Data with Microsoft Copilot and Azure Native Qumulo

It has been a true pleasure to see our friends at Qumulo constantly innovating and delivering a service that adds more value with every release. In the post below, authored by Qumulo, you will learn more about the value of their NEW Copilot integration and how to implement it with Azure Native Qumulo. Enjoy!

karautenMSFT

Principal PM Manager, Azure Storage 

Partner post from our friends at Qumulo

Learn more about Azure Native Qumulo here!

When working with unstructured data, employees may spend hours every week looking for information within internal data sources, performing analyses, writing reports, making presentations, finding and creating insights in dashboards, or customizing information for different clients and groups. Microsoft Copilot helps employees to be more creative, data-informed, efficient, ready, and productive when dealing with unstructured data.

Using Microsoft Copilot + the M365 suite of productivity products is seamless, but as organizations become more data-driven and lean into Copilot across their data estate, the need for Copilot integration with full featured file systems is more apparent than ever. Copilot can unlock the value contained in petabyte scale systems, delivering the latent intelligence already earned by an organizational dataset. Azure Native Qumulo (ANQ) Copilot Connector enables an organization to take full advantage of the data typically stored in enterprise scale file systems.

The Challenge: Deep Analysis of Unstructured Data

Using natural language to access the value of their file data is beneficial for customers in all industries. In fact, most data is stored in unstructured formats because files have been the standard structure for applications that do not rely solely on an underlying database. Microsoft Copilot can extract insights rapidly from both legacy documentation and modern application outputs by using a centralized unstructured data store like Azure Native Qumulo.

For example:

• In the healthcare industry, medical professionals use imagery technology to aid the diagnostic process. In manycases, these images need to be retained for decades so that radiologists, cardiologists, etc., can perform patient studies across long time horizons. Hospitals with large networks must manage and search across petabytes of data generated from modern and legacy medical imaging applications.
• The telecom industry leverages AI to boost network performance and find reliability faults within their infrastructure. This helps to automate operations and use data-driven insights for enhanced network coverage. Multiple petabytes of data is generated monthly, and resides in unstructured data storage. Understanding this data requires highly trained technicians to summarize findings.
• AI-based self-driving cars have sensors, automotive analytics, and connections to cloud services. These cars use data analytics to make real-time decisions based on the data that it gathers from the in-car sensors. The storage capacity per vehicle could reach 11 terabytes by 2030 making centralized data stored in the 100’s of exabyte range. Summarizing patterns requires complex modeling and data science, but the insights from individual files is also valuable.
• Energy companies optimize oil & gas production by forecasting future events and improving flow methods using AI. Combining these complex models with natural language prompts enable greater access across the business unit, increase the time to insights, and identifies patterns for demand forecasting, oil exploration, and predictive maintenance. These datasets often reach petabyte scale and include multiple different file types.  

The above industries often struggle with analyzing billions of unstructured files due to limitations in existing tools:

• Legacy Search Tools: Designed for structured data, these tools are inefficient for unstructured data and involve costly, time-consuming data migrations.
• Open Source AI Solutions: These require sharing data with unregulated third-party companies, raising significant security concerns and potentially creating a risk of data leakage to competitors.
• Manual/OCR Methods: Slow, inefficient, and often failing to provide new insights despite reformatting data.

Finding Insights with Microsoft Copilot

Microsoft Copilot combined with Azure Native Qumulo provides a seamless solution for reading and analyzing unstructured data in place, without requiring duplication or alteration of your data. Custom connectors allow Copilot to handle various file types, from PDFs and spreadsheets to text files. With this integration, Copilot helps integrate large data stores with the daily workflow of high value team members.

Flexibility and Scalability:

• Customizable Connectors: Engineers can develop custom connectors for various file types, allowing Copilot to analyze virtually any data type stored in ANQ.
• Petabyte-Scale Analysis: Microsoft Copilot is capable of handling extensive data sets, without the need for data movement or migration by using ANQ.

Security and Privacy:

• Azure Tenant Integration: Copilot operates entirely within the secure confines of your Azure tenant, ensuring that data remains protected.
• Exclusive Organizational Access: Analysis results are accessible only within your organization, maintaining strict data privacy and integrity.

User-Friendly Interface:

• Seamless Integration: Works with the standard Copilot search interface, providing a familiar user experience within the applications already most used for daily productivity, e.g., Teams, Outlook, M365, etc.
• Natural Language Processing (NLP): Users can submit and execute queries using plain language, making sophisticated data analysis accessible to non-technical staff.

Outcomes

In the industry scenarios we described earlier each customer benefits from giving more decision makers access to their data without requiring data scientists or highly trained personnel to serve as middlemen. Both new and legacy data become useful for comparisons and pattern analysis without spending costly hours trying to find the right files for analysis. Lastly, their Copilot implementation is no longer limited to OneDrive or SharePoint, and each business can take advantage of the data stored across the entire network attached storage estate.

The cost savings in terms of increased productivity are profound.  

Getting Started

Qumulo has published their Azure Native Qumulo Copilot Connector on GitHub at: GitHub - Qumulo/QumuloCustomConnector

Launching Azure Native Qumulo in the Azure Portal takes 12-15 minutes, and the connector can be set up within an hour. You can begin securely interacting with data using natural language as soon as the connector is established.

Embrace the Future of Data Management

For technical teams ready to revolutionize their data management strategy, the integration of Microsoft Copilot with Azure Native Qumulo offers a cutting-edge solution. Unlock the full potential of your unstructured data and stay ahead in the data-driven world.

Explore the possibilities today with Microsoft Copilot and Azure Native Qumulo and transform your approach to data analysis and management.