Leveraging Generative AI & Data Analytics: Tips and Tricks

know about the tips and tricks to leverage Generative AI and Data Analytics that can Boost Your Business

Generative AI is a powerful technology that can create new data and analyze existing data at a large scale. It can help to improve data analytics and business intelligence for various enterprises. Many generative AI vendors are integrating their models with data analytics solutions, while many generative AI startups are developing unique solutions for data analytics and data management. This guide will show some best practices for using generative AI in data analytics operations and some top tools that can be used for AI-powered analytics in different enterprise use cases.

Generative AI and Data Analytics: Tips and Tricks

Generative AI is a powerful technology that can enhance data analytics tools for businesses. However, it requires good data and standards to work well. Businesses that want to use generative AI for data analytics should follow these tips and tricks:

High-Quality Data Should Be Sourced and Used:

The quality of the data that is used for generative artificial intelligence technology affects the quality of the data analytics outcomes. This applies to both the data that is used to train the generative model and the data that is fed to the model regularly.

The best way to ensure high-quality data is to use first-party data, which is the data that is collected by the team or the business. This way, the team or the business can easily track the source of the data and identify any issues with their resources and users. However, sometimes it is necessary to use third-party data, which is the data that is obtained from external sources. In those cases, it is important to use data from reliable and trustworthy sources, preferably ones that are transparent about how they collect and secure their data. In all cases, the data should be checked for quality, bias, ethics, and compliance with any relevant regulations.

Determine KPIs, End Objectives, And Use Cases Proactively:

Generative AI models are amazing tools for automating and scaling data analytics, but they need to be used with the right purpose and the right tool. To get the best results, it is a good idea to define the KPIs and data analytics goals before starting. The following questions can help to set the right goals for the organization:

• Users: Who are the users of the data analytics tool? Are they mainly professional data analysts or less-technical team members?
• Industry requirements: What kind of data analytics solution is needed for the industry? Is it a generic or an industry-specific tool? Does it comply with any compliance regulation(s)?
• Cross-department use cases: Which department(s) will use the tool? Does it work for different use cases in the organization?
• Quantity: How many insights are planned to be generated daily, weekly, and monthly?
• Data presentation requirements: Who are the recipients of the insights? How should the results be displayed, such as in a dashboard or a visualization format? How often should the results be presented?
• Speed: How fast should the insights be generated?
• Accuracy: What level of accuracy is the goal for the data analytics results?
• Training: How much training will employees need to use the tool effectively?

These questions can help to establish relevant KPIs and choose the best data analytics tools to achieve those goals.

Many teams may not realize that generative AI models may not be necessary for some projects. Doing this research beforehand can prevent wasting time, energy, budgets, and other resources on generative AI technology that is not needed.

 Pick Data Analytics Applications That Work with Generative AI Models:

A good way to use generative AI in data analytics operations is to use a data analytics platform that has generative AI capabilities built-in. Some data analytics vendors have integrated generative AI, mostly ChatGPT, into their software to help users with data analytics tasks. Some of the tools that combine generative AI with advanced data analytics are:

• Microsoft Power BI: This is a BI platform for enterprises that includes Microsoft Copilot, ChatGPT, and Azure AI Services. Users can use natural language inputs and queries and get relevant data visualizations.Tableau GPT: This is a tool that uses Salesforce Einstein GPT and ChatGPT to automate data analytics tasks, such as data preparation and governance. Users can also use Tableau Pulse, a Tableau-GPT-powered solution that gives personalized data insights to employees.
• Qlik Sense: This is a cloud analytics solution that has the OpenAI connector, which allows users to access generative AI features. Users can get AI-generated data insights and natural language explanations of data.
• Notion AI: This is a new product in the Notion project management platform that uses ChatGPT technology to summarize meeting notes, generate next steps, and help users with writing tasks.