Artificial intelligence (AI) is pushing the boundaries of every industry, but few industries are as tailor-made for AI as data analytics. Analysts rely on massive datasets that require them to spend extraordinary amounts of time processing, cleaning, and interpreting information. With the help of AI, data analysts can now perform functions that used to take days in minutes.

It still takes human intelligence to direct the AI’s predictive models and machine learning (ML) algorithms. But by working with AI, you can automate data reviews and generate predictive models like never before.

Learn how analyzing data with AI takes advantage of what AI is best at: performing repetitive tasks, detecting patterns, and outputting data-driven predictions.

An overview of AI in data analytics

AI consists of numerous ML algorithms that take in massive quantities of data, extract insights from it, and create a model that simulates an understanding of it. After training AI this way, data analysts can feed it new information and direct it to perform tasks, like checking for errors or merging two datasets.

If given enough time and resources, AI can begin deep learning, which enables it to do more than simple tasks. Using simulated intelligence, AI can create predictive models that, with the help of analysts, output data-driven suggestions.

As more teams add AI to their workflows, they’re seeing that AI improves the data analysis process in the following ways:

Streamlines efficiency

AI tools excel at automating all the repetitive tasks that data analysts must do to set up and maintain a database. With a properly trained AI, you can simply feed it your data, tell it what to do, and turn to other tasks while it processes.

This efficiency offers new freedom because AI assumes tedious tasks and lets you focus on more meaningful work: interpreting data to discover insights.

Validates data

Validating data involves auditing it for possible typos, missing information, or unusual outliers. While a human could review a database in hours or days, an AI can perform this function in minutes. All it needs is an understanding of what these errors look like to detect and report them — and it’s quite precise, often finding even the tiniest issues that humans could overlook. That precision is integral to good data science, where your output is only as good as your input.

Improves ease of use

Most AI analysis tools offer natural language processing (NLP), which lets you interact with your data using natural language. For example, you might write, “Search this dataset for the five most purchased products” or “How have in-person and online sales changed over the last two years?”

The simplicity of NLP offers your team more freedom in how they interact with your database. It also lowers the barrier to entry for new analysts by fast-tracking much of the learning curve.

AI in data analytics: 3 core terms to know

Here are three AI terms used in data analytics along with what sets them apart.

1. Machine learning

Machine learning is the backbone of any AI. Developers write ML algorithms that work together to detect and extrapolate patterns. They train these algorithms on increasingly complex datasets until they form a sophisticated framework called an AI model. When the AI model is sufficiently trained, it can reliably discover patterns in similar datasets without human intervention.

A trained model no longer needs to learn how to analyze data but still makes adjustments to refine its output. As you feed it input, its algorithms work together to analyze data, output data-driven conclusions, and then rewrite itself according to those conclusions.

2. Neural networks

Neural networks aim to improve an AI model by organizing its ML algorithms into layers that share information. This setup allows the AI to look deeper into the data instead of just identifying simple patterns. Doing this can uncover more complex relationships, leading to better and more precise predictions.

Imagine you need to analyze sales data to recognize purchasing trends. The first layer of a neural network identifies basic features like sales volume and time periods. Based on these patterns, AI distinguishes between peak and off-peak seasons in the next layer. A final layer recognizes further data, such as demographics and product preferences, to improve the accuracy of trend predictions.

3. Deep learning

Deep learning is the pinnacle of AI sophistication (for now). It layers multiple neural networks in a way that simulates human intelligence. Developers train them on massive datasets, often several terabytes in size. These programs also grow rapidly by learning new patterns and rearranging their information architecture to better complete tasks.

This ability to adapt allows them to analyze unstructured data. Structured data is organized into searchable databases, but unstructured data has no predefined format, making it difficult to analyze. That’s what makes deep learning technology so valuable — for instance, self-driving cars use this type of AI to identify other vehicles on the road and evaluate changing conditions.

How AI can improve your data analytics process

AI makes handling and interpreting data simpler than ever because it seamlessly fits into many analytics processes. Here are some suggestions on where to integrate AI into business data strategies.

Data cleaning

The first step in analyzing a database is checking it for errors. Analysts spend significant time scanning through hundreds of lines in a spreadsheet or writing detailed SQL queries to uncover anomalies. Thankfully, AI can perform this task much faster. Simply feed the AI your database and use NLP to form a query, like “Check this database for missing information and errors.”

Data analysis

AI eases the burden of analysis for data professionals without replacing the need for human intelligence. You can use AI to search the data, but it’s up to you to determine what’s important.

For example, if you feed your data to a deep learning algorithm, it’ll churn out dozens of trends, predictions, and correlations you might never have found. But you’ll need to manually sift through these results to discover the most relevant, informative bits for your business.

Data visualization

Another common task is creating visuals for the information you analyze. You might use data analysis tools like RapidMiner and Tableau to do this, which feature AI-generated data visualizations to speed up the process. Tableau Pulse, for instance, uses AI to generate customizable data visualizations based on predefined key performance indicators (KPIs).

Predictive analytics

ML algorithms rely on identifying data patterns, so it’s no surprise that AI analytics platforms excel at predictive analysis. AI simply plots data points, finds the trend, and identifies the most probable outcome. A human data scientist could also do this, but the best AI tools can process hundreds or thousands of statistics faster and more accurately.

Industry use cases of AI data analytics

AI offers a vast range of benefits to nearly every industry. Here are examples of how AI supports data analysis processes in different industries, from basic data collection to complex predictive analytics.

Healthcare

The healthcare industry generates enormous datasets every day, like X-ray images and blood test measurements. AI algorithms consume this data to find connections between data points sooner than a medical professional might.

These advancements assist with early detection of cancer, diabetes, and Alzheimer’s, improving healthcare outcomes and potentially saving lives by helping doctors and patients make informed decisions.

Finance

With its convoluted regulations, the financial industry benefits from any advancement in AI technology that simplifies data analysis. Financiers can use this tool to advance their portfolios by speculating on stock prices and analyzing economic conditions.

Say you want to find economic signals that indicate a financial strategy’s potential success or failure. You could use a financial platform like OneStream to accomplish this task.

Marketing and customer insights

Interpreting critical user data lets businesses optimize products, services, and marketing campaigns. This information allows companies to reach goals like increasing conversions or driving engagement. AI tools that integrate with website platforms make this information significantly easier to access.

For example, Webflow Analyze tracks detailed page insights and allows you to see how users interact with each part of the website. Using this data, you can update underperforming pages to improve click-through rates.

Manufacturing

This industry requires many moving parts to work together in unison. Manufacturers must order raw materials, orchestrate assembly lines, and send out finished products according to a precisely optimized schedule. AI can improve this process by using learning models to detect potential bottlenecks and optimization opportunities.

Warehouses and supply chains

Warehouses and supply chains greatly benefit from using an AI tool to manage their inventory, coordinate with shippers, and track market values. AI models can quickly detect inventory gaps or long shipping times simply by learning patterns and reporting when the data deviates from them.

If given enough data to learn from, AI can even predict how market values might change in the short or long term. This allows you to make informed decisions about when to hold a product and when to sell it.

The challenges of using AI for data analysis

For all the benefits AI offers, it does come with the following limitations your team must navigate carefully.

Data quality

The adage “Put garbage in, get garbage out” has never been more true than with AI. Even a misplaced comma in a CSV file or a poorly formatted column in an Excel spreadsheet could severely impact the output.

And AI learns from everything it ingests, so the more errors you feed it, the more you’ll corrupt its learning model. To maintain an AI tool’s usefulness, ensure all the data you send it is accurate and formatted consistently.

Data security and privacy

If you handle secure or confidential data, be cautious about which AI tool you use and their data controls. AI systems can store data fed into them to refine their learning models, so any data you give them could resurface if another user asks a relevant query.

Unlock site analytics and optimization with Webflow

Data analysis helps optimize everything from workflows to sales, and it can also enhance your website’s performance. Integrating an analytics tool with your website platform lets you gather insights about which designs are effective and why. With this information and A/B testing tools, you can make targeted improvements that boost user engagement and conversions.

Incorporate AI-driven data into your website strategy with Webflow. Webflow Analyze, our native data analytics tool, provides a unified view of user behavior at every level. And Webflow Optimize takes it a step further by enabling AI-powered A/B testing and personalization, which dynamically personalizes your website experience for every visitor to increase conversions.

By using these tools, you can transform data into actionable insights. Get started with Webflow today, or contact our team to learn more about Webflow Analyze and Optimize.