Top tips is a weekly column where we highlight what’s trending in the tech world and list ways to explore these trends.
When you think about generative AI, what instinctively comes to your mind is content and image generation. But, in this week’s Top tips column, let’s look at a less-explored facet of generative AI: data analytics.
There are a lot of conversations about data and its benefits. Today, virtually every company knows that data-driven decisions are more likely to produce the intended outcomes and can generate more revenue than decisions not driven by data. But, sadly, the abundance of data has not really made much of a difference in data literacy. Generative AI is here to change that.
Here are some tips on how you can use generative AI to analyze data easily, effectively, and more accurately.
1. Get your data insights quickly and easily
How easy would it be if you could ask an AI program for the latest revenue trends for the last quarter, and it could provide the information within seconds? This is possible by integrating generative AI tools into data analysis processes.
Your prompts can be as simple as asking the AI program to give you the sales reports for the last month, or as complex as asking it to analyze the company’s revenue patterns over the last 10 years. Generative AI can sift through your company’s data and provide you with accurate, insightful results in a few seconds.
Let’s say you want a summary of your company’s performance with a report highlighting the key metrics, trends, and anomalies. You can input text-based questions and commands, referred to as prompts, and get your report with your requirements in seconds.
2. Visualize your data
Analyzing large datasets can be intimidating and time-consuming. But thanks to generative AI, it has become easier than ever. Large volumes of data can now be presented in visual formats such as tables, charts, and graphs.
Often, missing or incomplete data will disrupt the data visualization process. But with the help of generative AI, this can be mitigated. Generative AI can fill in the missing data and derive the desired visual outputs from it. This is especially useful in financial data analysis as missing or incomplete data points could disrupt accurate visualization of the data.
3. Streamline your data analysis processes
By now, we’re all aware that automating any task can drastically reduce the time and resources required to complete it, and the same applies to data analysis. When you manually sift through scores of data, there is a chance of miscalculation and misinterpretation. AI can eliminate these issues.
According to Forbes, “one of the strengths of AI is that it can process vast amounts of data with precision and help you identify trends, patterns, and anomalies that might be challenging to spot manually.”
Choose the right generative AI tool
With all that said, it’s not easy to find the right generative AI tool for your business. But if you keep the following things in mind, this will become easier.
Understand your data needs: Understand your data needs, including the volume, variety, and veracity of the data you need to process.
Find out the complexity of your tasks: You might require a simple descriptive analytics tool or a deep-learning-powered predictive analytics tool. Assess your business requirements and select the tool that aligns with them.
Know your budget: Some generative AI tools are open-source platforms, while others are premium programs—with varying costs. Choose the tool that aligns with your budget and desired capabilities.
Find a user-friendly tool: Select a tool with a simple, user-friendly interface, especially if your team members have varying levels of technical expertise.
These tips merely scratch the surface of the capabilities of generative AI in data analytics. There’s so much more to uncover and explore when it comes to data, so it’s only wise to find new methods of unlocking its true potential, such as generative AI.