The integration of artificial intelligence (AI) into dashboards and dashboard creation has created new ways for businesses to visualize and interpret data. Explo's new AI Copilot for Dashboards enables users to always have a data assistant embedded in their workflow.

Understanding Copilot for Analytics

Using natural language processing (NLP), AI Copilot transforms the users’ questions into visualizations and insights. With just a simple request, a user can easily pull a data report with all of the relevant dimensions and metrics in the format they want. This process involves sophisticated machine learning algorithms that understand context, manage queries, and present data in useful formats like graphs, charts, or reports.

While previously these types of tools and analyses were reserved for data professionals, the intuitive interface and simplified interaction model of the AI Copilot not only speeds up the process for data analysts but puts that power into the hands of less technical end-users. This accessibility helps foster a data-driven culture across all types of departments in an organization.

Getting Started with Copilot for Analytics

Explo’s AI Copilot simplifies report creation for your users by providing a chat interface for interacting with data directly in your application. By inputting a simple prompt into the tool, like “show me PTO trends over time,” Explo will pull the correct data and give the user multiple visualization options. 

Creating your initial Report Builder as a canvas for using the AI Copilot is easy. The first step in creating a Report Builder is deciding what type of data you’d like to display. 

All you have to do is create and format datasets that your users and the AI Copilot will have access to. Write SQL to define the dataset, and then use our interface to format the data so it’s easily understandable by your users.

Once you’ve created the datasets you can begin creating some standard reports for all your users.

Lastly, simply embed the Report Builder interface into your application, flip on the AI switch, and you’re good to go. Your users have full access to Explo’s Copilot for analytics directly in your application.

Within minutes your end-users can simply use the chat prompt to engage with the Copilot and get instant access to their data needs.

Best Practices for using Copilot for Analytics

While Explo’s AI Copilot for Analytics makes Explo’s already easy process of creating visualizations even easier, choosing and creating the correct datasets is key to the success of the product and for your users. 

Start by determining how to split the accessible data into datasets. How would your users expect to find data and see it organized? Reports that are generated are backed by a single dataset, so it’s crucial to set up datasets that have all the relevant information in a single dataset, as your end users will not be able to join datasets.

After the datasets’ data is defined and the underlying queries are written, then you’ll want to format the data so it’s easily understandable by your users. Explo allows you to change the column names, format numbers, format links, color code labels and add descriptions to fields. This cleans up the data so any non-technical user and the Copilot can understand the data.  

Lastly, set up any custom aggregations that you expect your users to perform. Basic aggregations such as sum, average, min, max are all supported, but custom aggregation on fields and aggregations involving multiple fields can be defined based on exact use cases.

End-users can choose their chart or graph type, add filters to it, and then share with others.

Copilot for Analytics Features and Tips

Once you’ve set up the datasets for your Report Builder, you can explore more features using Explo’s AI as your Copilot:

Data Insights with AI Integration:

• Visualization Suggestions: The AI can automatically recommend the best charts and visualizations based on the underlying data.
• Data Model Optimization: Copilot helps users organize and refine data models underlying the dashboards, making it easier to quickly query the right data.
• Interactive Data Exploration: The interactive exploration of data is aided by the natural language input of Copilot, which makes it easy for users to drill down into specific questions or zoom out for an overview.

Real-World Applications of Copilot for Analytics

Businesses that have deployed Explo’s embedded analytics platform are able to use AI to yield numerous benefits. 

Nash leveraged Explo to cut down dashboard development time from several months to a few days. This rapid deployment allowed them to quickly gather and implement user feedback, enhancing customer satisfaction.

The operating system for the grocery industry, Vori, used Explo to embed real-time analytics into their inventory management system. By adopting Explo, they were able to provide a customized analytics experience that natively aligned with their existing interface. In addition to major ongoing savings, Vori’s end-users reported major increases in satisfaction using the software.

For SafeBase, they used Explo to offer dynamic dashboards to track user activities for their software, eliminating the need to build out custom software from scratch. With this white label solution able to be easily customized to their needs, SafeBase estimated they saved $75k in development costs. 

Start Using Copilot for Analytics

Using Explo’s AI Copilot helps many organizations leverage the power of embedded analytics to put data in the hands of their users. 

If you’re interested in seeing how Explo’s AI tools can save your business money and help keep your organization competitive, schedule a demo with our sales team.

Looking for more? Discover more about how Explo can transform your data analytics practice by exploring additional resources and guides on the Explo blog.