Nowadays, it’s hard to scroll through your LinkedIn feed without seeing a post about ChatGPT. How to create sales email templates, blogposts, debug code,... The list of things you can do with 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.
As your data grows, it gets harder and more time-consuming to do data exploration. 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.
Imagine you work in a warehouse that stores and distributes thousands of packages daily. To run your warehouse more efficiently, you want to know:
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.
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 that task in less than an hour using machine learning models? They have 7 hours left to analyze other data or interpret the results.
This leads us straight to another huge 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, construction or finance, real-time analytics have an invaluable impact. Just imagine how many life-threatening accidents you could avoid if machine malfunctions were reported before they happened.
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 change their job roles and the way they work, these roles will still play a pivotal role in data-driven companies.
On the contrary, 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.
From raw data to decisions, let’s look at the role artificial intelligence plays in each stage of the data analytics process.
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.
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.
After you’ve trained AI models with clean, relevant data, you can start analyzing the data and generate insights. AI models can identify patterns, correlations, anomalies, and trends in the data. Doing it manually would cost your data analyst a few days or weeks. Now, they can do it in mere hours!
As with any technology, always be careful about system bias. AI learns from its training data, so if your source data contains biases, it can creep into AI algorithms as well.
Once you’ve found interesting patterns in your data, you want to present them in an easy, understandable format. With the help of business intelligence tools, you can start building visual dashboards to interpret insights. Interactive charts and graphs let you explore your data deeply, and drill down into specific information to improve your workflows.
You can automate data visualization tasks with ChatGPT or other tools. Some business intelligence tools have added AI capabilities to their software. Cumul.io’s AI-powered dashboard builder is a great example.
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.
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.
This embedded analytics platform focuses on user-friendly analytics. It helps SaaS products add powerful client-facing reporting to their platform in days, not weeks. With their no-code dashboard editor, anyone can build interactive charts in minutes. Hook up their extensive API to ChatGPT or other AI tools, and use it to autogenerate dashboards with simple prompts.
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.
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.
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.
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.
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.
Do you want to unburden data analysts and data scientists from cumbersome, time-consuming data analysis tasks? Then now is the right time to start exploring the power of AI. It can seriously speed up time to insight, and help your team focus on more complex tasks.
The tools above are a great starting point to start fidgeting with AI data analysis. If you’re working on a customer-facing analytics offering for your SaaS product, Cumul.io is tailor-built for SaaS use cases. Get in touch with our experts for a product demo, or sign up for a free trial to try it yourself!
