How to Use Generative AI for Data Analytics: 7 Examples

Generative AI, large language models, and ChatGPT have become so widespread in the past few years that it’s hard to think of a profession where they are not used. Using them for research or writing is fairly common, but with a bit of practice, you can successfully use them for data analysis and visualization.

But how do you get started? Are there any limitations to LLMs in data analytics? Let’s find out.

What is generative AI?

Generative AI is a subset of artificial intelligence that focuses on creating new content or data that mimics something a human would make. Traditionally, AI was able to merely analyze existing data.

In contrast, generative AI models can do that and create new content such as text, video, images audio, or code based on the analyzed datasets.

Practical applications of generative AI in data analytics

Enough with the theory: let’s take a look at practical examples of how to use generative models for data analysis.

Predictive analytics

AI predictive analytics is one of the most common use cases for this tech. You can feed datasets into GPT or similar tools and they can analyze it, looking for patterns.

The algorithms then predict future outcomes with that data based on the acquired information. For example, a healthcare institution can determine the busiest times in the year in terms of patients. They can then predict how many physicians to hire during those periods.

Anomaly detection

When a business works with huge sets of data, data analysts must employ automation to lighten their workload and generative AI fits the bill really well here.

For example, if you work in cybersecurity, you can use generative models such as Variational Autoencoders to create a model of the normal traffic flow in a network. 

Every time a new traffic pattern emerges that steps out of the “normal” range, the model flags it as an anomaly. This helps prevent attacks, leaks, misuses and other types of threats.

Data augmentation

You may collect data but not have enough information for data-driven decision making or creating dashboards for company stakeholders. Generative AI models can analyze your existing data and enhance it to improve the training of the model.

For example, you may want to use machine learning to detect rare diseases by looking at medical images. The problem is that there are not enough of these images since the condition is so rare. You can use AI to create synthetic images that look like real-world medical scans. This augments the dataset and improves the accuracy of the model.

Predictive maintenance

Let’s say you have a textile business that uses special equipment to turn raw materials into cotton and yarn that is ready to use. It can be hard to predict when complex machinery will need maintenance, and you want to avoid downtime as much as possible. Instead of staring at piles of new data in Excel sheets and weird formats, you decide to use machine learning models.

Your business can use AI to create simulations of what happens with this machinery in real life to determine which equipment is likely to fail and when. This can significantly reduce downtime and save massive amounts of time and money.

Creating personalized marketing campaigns

You run an ecommerce business and you don’t have a marketer on board, but you want to launch a solid email marketing campaign. You can employ analytics tools to consider…

• Your customers’ purchase history
• Their interactions with your store (emails, call transcripts, conversations with chatbots)
• Previous browsing history
• And any other data points you have stored in your CRM

Analytics tools can help you properly segment your target audience, so you can then create personalized marketing campaigns for each.

Synthetic data generation

Let’s say that you run a financial institution that manages huge volumes of customer data. You want to identify the spending patterns of your customers with an AI-driven analytics tool. However, you don’t want to reveal customer information and you need to maintain data quality at the same time.

You can ask an AI analytics tool to create a synthetic data set that has the same statistical properties as the original data but does not contain anything confidential that you may not want shared. You can now safely run an analysis without compromising any customer data.

Scenario analysis and simulation

You are now the head of an energy company that wants to maintain its position in the market. You want to streamline your predictive analytics without spending a fortune on research. 

You fire up OpenAI to create multiple scenarios with different market conditions, fluctuations in supply and demand, changes in energy policy, global events, and more.

Prerequisites for using generative AI for data analytics

It’s not enough to be familiar with AI technology to be able to successfully use it for data analytics. The barrier to entry is now significantly lower thanks to generative AI tools, but there are still some basic requirements you need to meet if you want to get valuable insights from AI data analytics.

Understanding of data science fundamentals

You don’t need to be a data scientist or data engineer to use AI for data analysis and visualization. However, being familiar with the basics (such as data preparation, cleaning, modeling, transformation, and some statistical analysis) helps out quite a lot. You’ll be able to understand the analytics process and how AI fits into it.

Familiarity with machine learning

Understanding the AI capabilities of machine learning tools is once again not absolutely necessary, but it helps. Understanding supervised, unsupervised, and reinforcement learning will give you some background knowledge of how AI workflows function.

Ethics and bias awareness

Before basing any decision-making on AI data analytics outputs, be aware that tools such as genAI are trained on data from the real world, which can be inherently biased and flawed. There are constant advancements in this field but make sure to check the data insights you get are unbiased.

Last but not least, every time you use sensitive data (e.g. your customers’ personal information) for analytics tasks, you’re sharing it with AI platforms. As the end-user, you should investigate how the tool you are using treats data privacy and consider implementing consent management solutions to ensure that there is proper data governance and protection in place.

Domain knowledge

If you want to do data-driven decision making in the field of healthcare, it goes without saying that you need domain knowledge in healthcare. The stakeholders presented with the data will expect that you understand the data sources, business objectives and the reasons behind doing analyses and visualizations.

Data visualization skills

Thanks to AI tools and natural language processing, many tools can turn data into visualizations that are easy to understand. But that’s just the tip of the iceberg. 

Not every visualization is good for every use case. For example, pie charts are not very good for showing parts of a whole and you should consider alternatives such as bar charts.

You don’t need knowledge in specific tools (e.g. Microsoft Power BI). In fact, there are plenty of materials online to teach you which visualization technique is best for each use case.

Wrapping up

Generative AI is not yet ready to fully replace data scientists and engineers. However, the barrier to entry into the world of data science and analytics is much lower. Thanks to AI tools, you no longer need to know how to clean or transform data or code in Python to create a simple dashboard.

And with Luzmo, you can bring AI technology and ease of use to your end-users. Luzmo allows businesses like yours to add embedded analytics dashboards to their software, with AI features such as a chart generator and Instachart.

‍Book a free demo with our team to learn more about pricing and how Luzmo can help you!