What is Augmented Analytics? A Beginner’s Guide

Austin Chia, contributor to the CareerFoundry Blog.

As the field of data analytics becomes more mature, organizations are looking for new ways to improve the way we analyze our data. In recent years, a new term has emerged in the analytics world—augmented analytics.

This new area of analytics promises to revolutionize how we analyze data by combining the power of artificial intelligence (AI) and machine learning (ML) with traditional data analytics techniques.

So what is it exactly, and how does it work in practice? In this beginner’s guide, we will explore the basics of augmented analytics in these topics:

  1. What is augmented analytics?
  2. Augmented analytics tools
  3. Benefits of augmented analytics
  4. Wrap-up
  5. Augmented analytics FAQ

Let’s have a closer look at this new term below.

1. What is augmented analytics?

Augmented analytics is the use of artificial intelligence (AI) and machine learning (ML) techniques in the process of data analysis. It goes beyond traditional business intelligence (BI) by automating data preparation, insight generation, and even decision-making.

This means that augmented analytics tools can help users explore and analyze large amounts of data in a faster, more efficient, and accurate way.

With this new technique, users can uncover hidden patterns, trends, and insights that may not be easily visible through manual analysis.

How does augmented analytics work?

Augmented analytics uses AI and ML algorithms to process and analyze large datasets. These algorithms are designed to learn from the data, identify patterns, and make predictions or recommendations.

Essentially, many tedious technical tasks typically done by data analysts or data scientists are automated.

The data is first pre-processed by the system, which involves cleaning, formatting, and integrating different sources of data.

Then, the augmented analytics tool applies various statistical and machine-learning techniques to identify trends, patterns, and outliers in the data.

Finally, the results are presented to users in a visual and easy-to-understand format, allowing them to make data-driven decisions quickly.

Features of augmented analytics

To help you understand the capabilities of augmented analytics, let’s take a look at some of its key features:

  • Automated data preparation and integration: Augmented analytics automates the process of preparing and integrating different sources of data, saving users time and effort.
  • Natural language processing (NLP): Some augmented analytics tools use NLP to allow users to query data in natural language instead of using complex coding or query languages.
  • Data visualization: Results are presented in a visual format, such as charts, graphs, and dashboards, making it easier for users to understand and interpret the data.
  • Predictive analytics: Augmented analytics tools use predictive algorithms to analyze historical data and make predictions about future events or trends.

2. Augmented analytics tools

There are many different augmented analytics tools available on the market today. Some popular examples include:


Tableau is a data visualization tool that also offers augmented analytics capabilities. It uses natural language processing to allow users to query data using conversational language and provides advanced predictive analytics features.

For example, Tableau’s Explain Data feature uses ML to automatically analyze data and provide insights and explanations.

Microsoft Power BI

Microsoft Power BI is a business intelligence tool that has recently added augmented analytics features. Its AI-driven insights feature allows users to automatically identify trends and outliers in their data.

It also offers advanced predictive analytics capabilities through its Azure Machine Learning integration.


Qlikview is a data analytics and visualization tool that also offers augmented analytics capabilities. It uses natural language processing to allow users to ask questions in plain English and provides machine learning-powered insights.

For example, its Insight Advisor Chat uses AI to analyze data and provide suggestions for further analysis using conversational analytics.

IBM Cognos Analytics

IBM Cognos Analytics is a business intelligence and data analytics tool that includes augmented analytics features such as automated data preparation, natural language querying, and predictive modeling.

It also offers AI features, such as Watson Assistant, to enhance the user experience and provide advanced insights.

3. Benefits of augmented analytics

This new way of approaching analytics and BI can bring about many benefits, too.

There are many benefits to using augmented analytics, including:

Increased speed and efficiency

By automating data preparation and analysis, augmented analytics tools can significantly reduce the time it takes to generate insights. This allows users to make faster and more informed decisions.

For example, a data analyst may spend hours manually cleaning and preparing data before they can even begin the analysis process. With augmented analytics, this step is done automatically, freeing up valuable time to focus on interpreting the results.

Improved accuracy

With machine learning algorithms doing the heavy lifting, augmented analytics tools can identify patterns and trends that may be missed by manual analysis. This results in more accurate insights and predictions.

Data accuracy is crucial in making informed business decisions, so this benefit is significant.

Empowers non-technical users

Augmented analytics tools are designed to be user-friendly, with features like natural language processing, making it easier for non-technical users to query data and generate insights.

With these tools, anyone in an organization can make data-driven decisions without needing advanced technical skills or knowledge.


As augmented analytics tools automate many tasks that would typically require a team of data analysts or data scientists, they can help organizations save on labor costs.

For example,  a smaller team of data professionals can use augmented analytics tools to handle larger volumes of complex data analysis rather than hiring more staff.

4. Wrap-up

To wrap things up, here’s a quick summary of what we’ve learned about augmented analytics:

  • Augmented analytics uses AI and ML techniques to automate data preparation, analysis, and decision-making.
  • Features of augmented analytics include automated data preparation, natural language processing, data visualization, and predictive analytics.
  • Popular augmented analytics tools include Tableau, Microsoft Power BI, Qlikview, and IBM Cognos Analytics.
  • Benefits of augmented analytics include increased speed and efficiency, improved accuracy, empowerment of non-technical users, and cost-effectiveness.

Does this area of augmented analytics excite you? Then maybe you’ll be interested in our free, five-day data analytics short course.

Interested in some further reading instead? Here are some must-read related articles:

5. Augmented analytics FAQ

What’s the difference between augmented analytics and automation?

Automation refers to the use of technology to perform tasks that would typically be done by humans. Augmented analytics, on the other hand, uses AI and ML algorithms to assist with data analysis, including automating tasks like data preparation and integration.

What’s the difference between predictive analytics and augmented analytics?

Predictive analytics is a subset of augmented analytics. While predictive analytics involves using statistical and machine learning techniques to make predictions based on historical data, augmented analytics encompasses a broader range of capabilities, including automated data preparation and natural language processing.

What’s the difference between augmented analytics and self-service analytics?

Self-service analytics refers to the ability of non-technical users to access and analyze data on their own without relying on IT or data professionals. Augmented analytics, however, takes self-service a step further by using AI and ML algorithms to assist with data analysis, making it even easier for non-technical users to generate insights. 

So, while both involve empowering non-technical users, augmented analytics goes beyond just providing access to data and enables users to conduct advanced analysis without technical expertise.

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