Using AI for Data Analysis: The Ultimate Guide (2024)

Artificial Intelligence
Jul 17, 2024
Using AI for Data Analysis: The Ultimate Guide (2024)

It’s hard to scroll through your social media feeds without seeing a post about AI or ChatGPT. How to create sales email templates, blogposts, debug code,... The list of use cases for generative AI tools seems endless.

How about using AI for data analysis? In this article, we’ll explore why AI is great to speed up data analysis, how to automate data analysis each step of the way, and which tools to use. Let’s jump in.

What is AI data analysis?

As your data grows, data exploration gets harder and more time-consuming. AI data analysis uses different artificial intelligence techniques to get valuable insights from large amounts of data. Here are just a few examples of these techniques.

  • Machine learning algorithms: extract patterns or make predictions on large datasets
  • Deep learning: use neural networks for things like image recognition, time-series analysis and more
  • Natural language processing (NLP): derives insights from unstructured text data

Imagine you work in a warehouse that stores and distributes thousands of packages daily. To run your warehouse more efficiently, you want to know:

  • How long does inventory stay in the warehouse on average?
  • How much space in your warehouse is (un)occupied on average?
  • Which items are low in stock and need to be replenished?
  • What’s the average time needed to replenish stock items per product type?
  • Which items have been in the warehouse for longer than 1 month/quarter/year?

AI algorithms will search for patterns in large datasets to find an answer to these business questions. By automating this difficult task, businesses can make better, data-driven decisions quickly. Data scientists have been using machine learning for years to analyze big data. And nowadays, a new wave of generative AI tools empowers anyone to analyze data, even if you don’t know anything about data science.

Benefits of using AI for data analytics

AI can feel daunting if you’re not familiar with it. But if you consider the benefits, it’s more than worth dipping your toes into it.

First off, artificial intelligence can seriously reduce your operational costs. 54% of businesses say implementing AI led to cost savings. Imagine you’re paying a data scientist to spend 8 hours on manual data cleaning or processing. What if they can complete these repetitive tasks in less than an hour using machine learning models? They’ll have 7 hours left to analyze other data or interpret the results.

That leads us to another big advantage: time savings. AI can analyze large volumes of data far quicker than humans. It’s much easier to scale data analysis, and get insights in real time. Especially in industries like manufacturing, healthcare or finance, real-time data monitoring has an invaluable impact. Just imagine how many life-threatening accidents you could avoid if machine malfunctions were reported before they happened.

AI analytics: a threat for data analysts?

With the rise of tools like ChatGPT, people immediately started raising concerns about job security. Think of our data scientist again, who now gets the job done 8 times faster. Should they fear AI will take over their jobs?

If you know that 90% of the world’s data was generated in the last 2 years alone, and that data growth is expected to increase by 150% in 2025, there’s no cause for concern. Data will only become more important in the next coming years, and the world needs data analysts and data scientists to make sense of it.

While AI tools may transform job roles and workflows, data analytics experts will be even more crucial in data-driven companies. Organizations that invest in corporate data analytics training courses equip their teams with the skills to leverage AI-driven insights effectively, ensuring they stay competitive and innovative in the evolving landscape.

If you familiarize yourself with AI tools now, it can be a huge driver in your career. You’ll be able to solve more complex problems faster, which is critical for innovation.

How to use AI for data analysis?

From raw data to decisions, let’s look at the role artificial intelligence plays in each stage of the data analytics process.

1. Data collection

If you want to get valuable insights from your data using AI, data collection is the fundamental first step. You’ll need to extract data from different data sources to feed your AI algorithm. Otherwise, it won’t have input to learn from.

You can train AI systems with any type of data, whether it be product analytics, sales transactions, web tracking, or automated data collection through web scraping.

2. Data cleaning

The cleaner your data, the more valuable your insights will be. However, data cleaning is a tedious process and prone to human error if you need to do it manually. You can use artificial intelligence to do the heavy lifting. For example, use AI to identify outliers, handle empty values, normalize your data, and more.

3. Data analysis

After you’ve trained AI models with clean, relevant data, you can start analyzing the data and generating actionable insights. AI models can identify patterns, correlations, anomalies, and trends in the data.

A new wave of generative BI tools is revolutionizing this space. Instead of having a data analyst work on manual reporting for days or weeks, you can now get an answer to your business questions in minutes.

Generative BI in ChatGPT
Example of a Custom GPT that lets you interact with your data in Luzmo's BI tool

The way these tools work is through conversation. You can ask a simple question, for example: “How much revenue did we generate last month?” The AI will answer in plain English, so you no longer need to sift through spreadsheets full of data. You don’t even have to look at charts or visualizations.

As with any technology, always be careful about accuracy and system bias. AI learns from its training data, so if your source data contains biases, it can creep into AI algorithms as well.

4. Data visualization

Once you’ve found interesting patterns in your data, you want to present them in an easy, understandable format. With the help of AI-powered business intelligence tools, you can start building visual dashboards to support decisionmaking. Interactive charts and graphs let you explore your data deeply, and drill down into specific information to improve your workflows.

If you’re looking to automate simple data visualization tasks, you can use some of the many custom GPTs for data visualization in ChatGPT. For something more powerful, we recommend using business intelligence tools that offer AI-powered capabilities in their suite.

Especially if you’re building data visualizations for a customer-facing application, you want to make sure the user experience to analyze and visualize data is seamless. For example, with Luzmo’s AI Chart Generator, anyone can type in a simple prompt, and get interactive data visualizations as a result.

Example of AI data analysis: prompt-based charts in Luzmo

5. Predictive analytics

Compared to traditional business analytics, artificial intelligence excels in forecasting. Based on patterns in historical data, it can run predictive models to make accurate predictions about the future. Just think of forecasting inventory based on past stock levels. Or setting sales targets based on past sales and seasonality.

A fun example is this soccer app that predicted the match results during the 2024 European soccer championship. While an LLM summarizes the predictions, it also contains a bunch of interactive visualizations to compare the metrics of each team head-to-head.

Predictive analytics example in Luzmo

6. Data-driven decision-making

If you’ve used artificial intelligence in the previous steps, you’re bound to get better insights as a result. Leveraging these powerful insights, you’ll make better decisions faster and improve your status quo. With powerful predictive analytics, you can even avoid issues before they happen.

Example of proactive data-driven decisionmaking with AI data analysis

The dangers of using AI for data analysis

While AI analytics tools significantly speed up the analysis process, there are some pitfalls, too. While these tools can streamline the process, it is only as good as the person using it. Here are some of the challenges that may arise with AI.

Data quality

Trash in = trash out. AI data analytics tools use the data you feed into them and give you adequate results. If your data is poorly formatted, has errors and missing fields or has outliers, AI analytics tools won’t be able to spot it as many don’t have anomaly detection.

In other words, before doing any data analytics, you should invest time and money in data cleaning and formatting.

The decision-making processes only work if the data is accurate and up to date, and this is where a human being comes into play.

Data security and privacy

In April 2023, Samsung employees used OpenAI to help them write code. The result? They leaked classified code for measuring superconductor equipment. As OpenAI states on their website, the data you feed into it is used to train the language learning model and teach it more things about the world.

If you ask an AI tool to analyze or summarize data, it means that in most cases, someone else will be able to access that data. Be it the people behind the powerful AI analytics tool or the users wanting to learn, your data is not always safe.

Be mindful of the data you feed into AI analytics tools, especially if you have to comply with strict laws and regulations.

You’ll still need personnel on board

Even if you have the best data analysis tools in the world, you’re still going to need someone to man the ship.

First, you may need a data scientist to help you with unstructured data, as well as choosing the right ETL and data visualization platform. You may be able to do a lot of the work yourself, by using a tool such as Luzmo’s chart generator. However, you’ll still need someone to steer you in the right direction.


If you’re looking to build client-facing data applications, Luzmo is your pick. This user-friendly embedded analytics platform helps SaaS products add powerful data visualizations to their platform in days, not months.

The implementation is quick for any developer with just a few lines of code. But the ease of use doesn't mean it's any less robust. With multi-tenant support, localization, and advanced interactivity, you can tailor dashboards to different users, making sure they only get to see the data they are allowed to see.

In terms of AI capabilities, Luzmo allows SaaS builders and their end-users to create charts using nothing but plain English. With AI-powered recommendations, picking the right visualizations for your data and making better decisions becomes a breeze.

Example of Luzmo's embedded analytics


Instachart is a free tool, developed by Luzmo, which lets you create fully functional dashboards with barely any input needed. Just upload one of the following, and it will generate an interactive dashboard in seconds:

  • A text prompt
  • A UX mockup, Figma design, or a dashboard screenshot
  • A hand-drawn sketch of a dashboard

You don’t even need a dataset to use it. Instachart will auto-generate mockup data, related to the dashboard you are trying to achieve. Perfect for generating interactive mockups for your upcoming projects! However, if you have a Luzmo account, you can also connect it directly and build dashboards on top of your own data.


Tableau’s AI features are aimed at data scientists, so you need a little more experience to get started. Data analysts can do AI-powered predictions, what-if scenario planning, and other data science techniques. For data scientists, they offer statistical modeling directly in Tableau with R, Python, MATLAB, and more.

Example of predictive analytics in Tableau

Microsoft Power BI

Microsoft has infused AI capabilities directly into Power BI to handle text data. You can now enrich your data through for example sentiment analysis, key phrase extraction, language detection and more.

AI sample for Power BI


KNIME is an open-source data science platform, and a great entry tool if you want to start experimenting with AI tools. Data experts can use an intuitive, drag-and-drop interface to design, train, and apply machine learning models.

Example of AI model workflow builder in KNIME


Databricks unifies data, analytics and AI all under one platform. It combines elements of data lakes and data warehouses, making it a great infrastructure for developers to run AI applications or machine learning algorithms.

Databricks architecture


AnswerRocket is like an AI assistant for data analysis. You can connect data from different data sources, ask questions in plain English, and get proactive insights and recommendations based on the underlying data.

Example of Answerrocket's prompting interface

Getting started with AI data analysis

Data anlysis is no longer a privilege for data analysts and data scientists. You no longer need a PhD in data science, or ample experience writing SQL. With AI-powered tools, anyone can make data-driven decisions without understanding the complex data structures behind it, and it doensn’t have to be time-consuming.

If you’re building a software product, now is the right time to explore adding AI into the mix. With Luzmo, you combine quick deployment with a superior experience for your clients. With AI-powered analytics, you’ll speed up time to insight for your customers, and boost engagement of your software.

Get in touch with our experts for a product demo, or sign up for a free trial to try it yourself!

Mieke Houbrechts

Mieke Houbrechts

Content Marketing Manager

Mieke Houbrechts is a long-time blog contributor and content marketing expert at Luzmo. Covering anything from embedded analytics trends, AI and tips and tricks for building stunning customer-facing visualizations, Mieke leans on her background in copywriting, digital marketing, and 7 years of industry knowledge in the business intelligence space.

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