Mastering Copilot: A Deep Dive into AI-powered Data

What is Microsoft Copilot?

Generative AI is transforming how we work. With generative AI being at the forefront of technology hot topics right now and the release of tools like, ChatGPT and DALL-E, Microsoft have launched a conversational generative AI assistant called Copilot. It’s designed to increase productivity and create original content. In this blog we explore how Copilot integrates with familiar tools and how it can integrate with Fabric and Power BI and the considerations for successful adoption.

Copilot: your AI co-worker

The chat interface allows users to complete tasks such as searching for information, summarisation activities, and generate intelligent content like coding solutions or documents. The technology that underpins this is AI and Large Language Models (LLM). It’s Natural Language Processing allows you to ask questions in a casual and informal way, as it can extract the intent behind your search so you can receive human-quality responses.

The search capabilities utilise pre-existing models like GPT-4 and Bing Search which perform the web search and layers this with summarisation models to make it user friendly.

Where is Copilot available?

Copilot is being released across the Microsoft Suite of technologies so it’ll be able to aid almost anyone who uses these technologies:

  • Microsoft Fabric: the introduction of Copilot though within Fabric is arguably the most interesting as this is the first wide-spread use of Generative AI across data engineering and analysis, which unlocks a whole new suite of capability. The capabilities within Fabric allow for the generation of intelligent code implementation, automation of tasks, design templates for orchestrating data via data pipelines and creating complex analytical models.
  • Power BI: for Power BI Copilot means instant generation of reports and the smart generation of suggested KPIs to explore.
  • Office Suite: some interesting integrations include the availability within Office including generating email responses in Outlook or producing Power Points or Word documents by simply giving a prompt.

Considerations for successful Copilot adoption

While these capabilities sound like they may change the way data analysts and engineers work, there is a lot to consider to get to the point of utilising Copilot correctly, accurately and in a secure way. Here are some key considerations to ensure Copilot empowers your data team.

Aligning with your current data strategy

Copilot isn’t a magic bullet. It may seem like quite an easy task just turning Copilot on in Fabric and starting to generate logic from it, but how it aligns with what your organisation is trying to achieve is a key question to ask. Looking at the current data platform you have in place and the areas you’re trying to develop will help you make informed decisions about where to invest in the use of Copilot.

Questions to consider include: Are there any skills gaps in my data team? Are there any repetitive tasks that could be unlocked with the assistance of Copilot? Importantly, remember that reliance of Copilot, shouldn’t replace your data team’s expertise. For example, if you have a large and very skilled Power BI team creating many similar reports, Copilot can accelerate these tasks as there are team members who can identify and validate the valuable content from Copilot. If you have a smaller data science team and try to solely rely on Copilot to alleviate this gap you run the risk of developing analytical models you can’t confidently say solve your needs or fully manage.

The key here is to first understand your business challenges. Then you can explore how Copilot can empower your team to solve these problems, not replace your solutions to these challenges.

Data quality is paramount.

There is also a perception that generative AI like Copilot can simply just be turned on and the insight generated will immediately be correct, which is not accurate at all.

“Put rubbish in and you’ll get rubbish out” applies to Copilot as well.

If your data is not in a cleansed, well-defined state, the insight you generate will only be as good as this. For example, if you have poorly labelled tables and data that doesn’t reflect the business terms any questions you ask of Copilot have a high risk on not identifying an accurate solution.

Successful Copilot implementation: a collaborative approach

Integrating Copilot requires careful planning for optimal results. Here are some key aspects to consider:

A roadmap to adoption

The first step in the development of these solutions is creating the roadmap with defined stages and clear outcomes. These should be in sync with the strategic goals of your organisation. This is where you’ll be able to see the impact of AI solutions like Copilot.

We can help deliver this through discovery sessions with key stakeholders and technical teams to identify these goals and the gaps where Copilot could be used. We also can conduct assessments on your current data platform and data and provide recommendations or support to get your data in a state that will best unlock the value of Copilot.

Security, governance and responsible use

A commonly overlooked aspect of using generative AI is not considering the security and governance implications which can lead to unintended consequences.

Utilising Microsoft Copilot relies on well governed and managed data. Not implementing appropriate data classification, user permissions and access control poses a significant risk, as users might inadvertently gain access to organisational sensitive or confidential information.

Recognising this, Simpson Associates has developed processes and activities as part of their Copilot implementation service to help mitigate these risks, ensuring organisations can benefit from Copilot whilst remaining compliant and further safeguarding organisations against unauthorised access to sensitive information. Our Copilot readiness assessment includes a comprehensive evaluation of potential privacy risks associated with implementing Copilot within your organisation. Based on this evaluation, we provide tailored recommendations to safeguard your privacy throughout the Copilot implementation process.

Additionally using Copilot responsibly will also increase the success and sustainability of your implementation, we can help ensure that it’s adhering to the intended use, what it means for you to use AI responsibly, and how to identify potential risks.

Custom AI generative solutions

During our assessment you may also discover that you could be utilising generative AI in a more extensive way. It is possible to design and create your own bespoke AI front door to a suite of AI capabilities and make it personal to your requirements. We can both provide the guidance and implementation of any custom solution you’d like to consider.

Specialist support and guidance

Our specialist consultants can be used as resource to answer any questions about Copilot. This can be used as development time to work with your teams in a skills-transfer approach, helping qualify your data readiness and any broader data questions.

Monitoring usage

When rolling out Copilot to your organisation, we can recommend how to best monitor you consumption so you understand how well Copilot is being adopted and understand the cost breakdowns.

Copilot key take aways

To summarise the key takeaways. Copilot can be a valuable asset to your data platform in many ways, boosting efficiencies and cost saving solutions.

This does not replace the need for data specialists or is a shortcut to creating secure, responsible, and sustainable solutions. The need for a high quality of data is crucial to gain meaningful impact and a clear vision for AI strategy is how you best unlock its value.

Need help? We’re here!

If you’d like to explore Copilot further or just like advice on any areas discussed in this post, we can support you with either our Copilot Assessment or solution discovery services. Feel free to chat with us live. We’re happy to answer any questions you have about the areas discussed in this blog.

Blog Author, Serena Samra, Data & AI Technical Presales Consultant