Using AI for Data Analysis: The Ultimate Guide (2023)
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By OpenTaskAI profile image OpenTaskAI
5 min read

Using AI for Data Analysis: The Ultimate Guide (2023)

But what about employing AI for data analysis? This article delves into why AI is excellent for expediting data analysis, the steps to automate this process, and the best tools for the job. Understanding AI Data Analysis Handling data exploration becomes more complex and time-intensive as your data expands. AI

But what about employing AI for data analysis? This article delves into why AI is excellent for expediting data analysis, the steps to automate this process, and the best tools for the job.

Understanding AI Data Analysis

Handling data exploration becomes more complex and time-intensive as your data expands. AI data analysis employs various AI techniques to extract meaningful insights from vast data sets. These techniques include:

  • Machine Learning Algorithms: Identify patterns or predict trends in large data sets.
  • Deep Learning: Utilizes neural networks for tasks like image recognition and time-series analysis.
  • Natural Language Processing (NLP): Analyzes and derives insights from unstructured text data.

Consider a warehouse managing thousands of packages daily. To enhance efficiency, you might want to know inventory turnover rates, warehouse space usage, low-stock items, average restocking times, and products stored beyond certain time frames. AI algorithms can quickly sift through large data sets to provide answers to these questions, aiding in swift, data-driven decision-making.

Advantages of AI in Data Analytics

AI might seem intimidating at first, but the benefits are undeniable:

  • Cost Reduction: 54% of businesses report cost savings after implementing AI. Tasks that took hours can be reduced to minutes with AI, freeing up time for more in-depth analysis.
  • Time Efficiency: AI can process large volumes of data much faster than humans, offering real-time insights crucial in sectors like manufacturing and finance.
  • Enhanced Job Roles: Despite concerns about AI replacing jobs, the growing volume of data ensures a continued need for data analysts and scientists. AI changes job roles but doesn’t eliminate them.

The Role of AI in Data Analysis

AI plays a pivotal role at every stage of the data analysis process:

  • Data Collection: The first crucial step is gathering data from various sources to feed the AI algorithms.
  • Data Cleaning: AI can automate and refine the data cleaning process, ensuring better data quality.
  • Data Analysis: AI models can quickly identify patterns and trends in data.
  • Data Visualization: Business intelligence tools, enhanced with AI, can create interactive and insightful dashboards.
  • Predictive Analytics: AI excels in making forecasts based on historical data trends.
  • Data-Driven Decision Making: Improved insights from AI lead to better, faster business decisions.

How To start AI Data Analysis

1. Generating code & debugging errors

First up, you’ll most likely be using AI for generating code or debugging errors in data analysis. This is particularly helpful for complex tasks, such as visualizing large datasets and building machine-learning models.

Some common AI coding assistants you can use include DataCamp Workspace AIAnaconda Assistant, Jupyter AI, and GitHub Copilot.

2. Explaining analysis & insights

In data analytics, explaining insights and diving deeper into the data is sometimes necessary to extract true business insight. That’s where an AI can help.

Using AI tools for data analysis like Tableau GPT, you can quickly explain a specific data point on a chart is behaving a certain way and provide deeper insights into it.

For example, you can ask straightforward questions such as:

“Why did the sales decrease in March?”

The AI chatbot will then scan through your datasets to identify trends and correlations that could provide you with answers to your questions.

This function could also be used for other purposes, such as exploratory data analysis when encountering a new dataset or database in your data analytics project.

3. Creating Synthetic Data

Another useful application of AI in the field of analytics is the production of synthetic data. In fact, according to a Gartner report, it is predicted that future AI models will be mostly trained by synthetic data by 2030.

An infographic from the Gartner report — source

This can be especially useful for machine learning engineers and data scientists.

For example, training datasets can be generated and fed into machine-learning models. This can be done through either free tools like ChatGPT or paid tools like Mostly AI or Gretel AI.

This makes it easier to test out different models and see how they perform with the generated data. This is especially useful if you’re in need of some training datasets that are harder to obtain, like various forms of media, including images and videos.

Another way that AI can help with data analysis is through automated data imputation. Automated imputation can help fill out datasets with missing values or outliers more accurately and quickly.

4. Creating Dashboards & Reports

Next, AI can be used to create interactive dashboards and reports.

For example, you can use an AI-driven tool like Tableau GPT to quickly aggregate data from multiple sources into a user-friendly dashboard or report.

You don’t even need to have any prior experience in the field of data visualization. All you need to do is select the data you’d like to include in the visualization, and the AI will automatically format it into a user-friendly chart or graph.

In addition, another unique way to use AI to assist with creating beautiful charts is to use the Midjourney AI to generate some eye-catching ideas for dashboards relevant to your analysis. Check out our guide on how to use Midjourney for more details.

5.Streamline Data Entry from Images with Automation

For data analysts and professionals who regularly work with Excel or similar spreadsheet tools, AI offers a valuable solution to automate the tedious task of data entry from images.

This capability becomes especially handy when dealing with extensive data extraction from images or documents, eliminating the need for manual input.

Tools such as Excel’s “insert data from picture” feature harness the power of computer vision to convert tabular data present in images into digital datasets. This automation not only saves significant time but also reduces the effort required for data entry, making your workflow more efficient.

Top AI Data Analysis Tools

  • Luzmo: A user-friendly analytics platform, ideal for SaaS products, offering a no-code dashboard editor and extensive API integration capabilities.(
  • Tableau: Suitable for data scientists, offering advanced AI-powered predictions and integrations with R, Python, MATLAB, etc.(
  • Microsoft Power BI: Infuses AI for handling and enriching text data with sentiment analysis, language detection, and more.(
  • KNIME: An open-source platform perfect for beginners in AI data analysis with an intuitive interface.(
  • Databricks: Unifies data, analytics, and AI, offering a robust infrastructure for developers.(
  • AnswerRocket: Acts like an AI assistant for data analysis, offering insights and recommendations in plain English.(

Embarking on AI Data Analysis

In summary, leveraging AI for data analysis not only speeds up the insight-gathering process but also enables teams to concentrate on higher-order tasks, marking it an essential tool in the modern data landscape.

By OpenTaskAI profile image OpenTaskAI
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