Artificial intelligence is quickly getting widely adopted across countless applications. But how is it be of service in data analytics? In this blog article, you’ll review some unique ways to use AI in data analytics. Each of these ways includes unique AI tools widely used in the field, so you can apply them quickly and easily.
Keep reading to discover what these unique methods are!
What is AI in data analytics?
At its crux, AI in data analytics is the application of artificial intelligence (AI) to analyze large sets of data. This enables data analysts to unfold trends and gain insight into the behavior of consumers or other datasets. With the use of powerful machine learning algorithms, AI can help make sense of large amounts of information promptly and with amazing accuracy.
Below are explained some of the ways to use AI in data analysis:
Generating code & debugging errors
Firstly, you’ll most likely be using AI for generating code or debugging errors in data analysis. This is especially useful for complex tasks, for example, visualizing large datasets and developing machine-learning models. Some popular ai tools you can use include DataCamp Workspace AI, Anaconda Assistant, Jupyter AI, and GitHub Copilot.
For generating code, you’ll employ these tools to generate code blocks for analysis. For example, you can prompt the Jupyter AI within your programming environments in Python providing that it runs the IPython kernel.
Explaining analysis & insights
In data analytics, explaining insights and digging into the data is at times necessary to glean true business insight. That’s where an AI tool can assist. Using AI tools for data analysis such as Tableau GPT, you can at once explain – a specific data point on a chart is, behaving a certain way. And provide comprehensive insights into it.
Creating synthetic data
Another valuable application of AI when it comes to analytics is the generation of synthetic data. According to a Gartner report, the future AI models will be mostly trained by synthetic data by 2030.
This can be specifically handy for machine learning engineers and data scientists. For example, training datasets may be created and inputted into machine-learning models. This could be executed through either free tools like ChatGPT or paid tools like Mostly AI. This simplifies testing out different models and ascertaining how they perform with the generated data.
Creating dashboards & reports
AI can even be used to develop interactive dashboards and reports. For example, you may use popular AI tools like Tableau GPT to quickly aggregate data from different sources into a user-friendly dashboard or report.
The Final Words
Finally, for all the data analysts who use Excel or other spreadsheets, AI powered tools can be used to automate the process of data entry from images. This is very handy for collecting large chunks of data from images or documents, enabling the user to quickly enter the required information without doing it manually. AI-powered tools such as ‘insert data from picture’ feature on Excel can allow for this function. By making use of computer vision on the backend, it will convert images of tabular data into digital datasets thus saving you a lot of time and effort.
