Business Intelligence

What Are Augmented Analytics, and How Can They Help Your Business?

Aug 3, 2022

Augmented analytics aren’t magic, but they can boost the effectiveness of your analytics team. Learn how in this article.

Andrew ConradSr Content Writer
What Are Augmented Analytics, and How Can They Help Your Business?

As a chief data officer, business intelligence manager, or other analytics leader in your growing organization, you’ve probably felt at times like you could use one or two more data scientists on your team. Imagine the problems you could solve with just a few more resources.

Unfortunately, hiring new data scientists takes a lot of time and effort, especially with demand for these skilled position skyrocketing in 2022.[1]

But what if you could adopt software that boosts the effectiveness of your existing data analytics team, allowing them to uncover more valuable insights, create more reports, and analyze more data in the same amount of time and with the same number of data scientists?

This software is called augmented analytics software, and it can take your analytics team to the next level. And, whether your team is ready to take advantage of augmented analytics tools now or in the future, it can be beneficial to familiarize yourself with the software, its features, and the type of problems it can help solve so that you’re fully prepared to make the most of it when the time is right.

In this article, we’ll take a deeper look at augmented analytics, along with tips on how you can use this technology to boost the effectiveness of your analytics team.

What are augmented analytics?

Augmented analytics are an AI-boosted form of analytics that uses software to automatically prepare new analytics reports based on historic trends, scan billions of data points automatically, use machine learning to anticipate analytical queries, and even explain new data insights for end users.

Generally, augmented analytics software features two layers:

  • A visual layer, which is how the user interacts with the software and sees data visualized as charts, graphs and reports.

  • And a backend layer, which is where the AI and machine learning algorithms work to process and analyze data, make suggestions, and more.

If this sounds like magic, it’s because of the improvements in AI and machine learning over the past decade.

“AI innovation is happening at a rapid pace,” said Shubhangi Vashisth, senior principal research analyst at Gartner.[2] “Innovations including edge AI, computer vision, decision intelligence and machine learning are all poised to have a transformational impact on the market in coming years.”

In fact, Gartner predicts [3] that the worldwide AI software market will reach $62 billion this year, an increase of more than 20% over 2021. The widespread adoption of this groundbreaking technology is well underway, but Gartner estimates that only 10% of analysts are using augmented analytics technology to its full potential. That means that organizations like yours have a golden opportunity to pull away from the competition just by making better use of augmented analytics.

Is your team ready to use augmented analytics? Ask yourself these questions:

  1. Do you have an analytics team in place? Augmented analytics can support an analytics team, but they can’t replace an analytics team. Before investing in new software, first make sure that you have an analyst or two to use it.

  2. Do you have enough data? Augmented analytics (and all analytics tools for that matter) work best with lots of clean data. If your analytics team is still working on gathering data, you’ll be better off waiting to upgrade your analytics software.

  3. Do you need augmented analytics? Augmented analytics help analytics teams do more with existing resources. But depending on the size and needs of your organization, your current analytics tools might be sufficient and you can hold off on upgrading.


It’s understandable if you’re still having some trouble wrapping your mind around augmented analytics, as this is truly a technology of the future. Here is a short video that may help clarify this concept and show how augmented analytics can support human decision makers:

How Augmented Analytics Enhances Decision-Making (Source)

Examples of problems that augmented analytics can solve

Now that we’ve dug into the concept of augmented analytics a bit, let's take a look at some examples of how this technology can help businesses like yours.

It’s worth reiterating here that augmented analytics can’t do all of the work for the user. It’s not like tax preparation software that guides the user through every step of the process. You’ll still need a data scientist or skilled data analyst who knows how to use your analytics platform.

  • Too much data, too little time. Say your organization has just come across years of previously undiscovered sales data. It might take your small analytics team months to clean and prepare all of this data for analysis using traditional methods, but augmented analytics can shave the data preparation process down to a fraction of the time.

Tip: If you have unstructured data that needs to be processed, start gathering it in one place so that it’s ready to go once your new augmented analytics tool is up and running.

  • Empowering citizen data scientists. As we pointed out above, you still need trained data analysts to oversee your analytics software. But augmented analytics can lower the barrier to entry, enabling less experienced employees to create reports and ideate queries, for example.

Tip: Consider having your data analytics team hold regular workshops to encourage data fluency throughout your organization.

  • Naturally speaking. Say you have an executive who wants to use your analytics tool to compare sales data from Q3 of each of the past five years. In the past your analytics team might have had to program this query into your business intelligence software. With augmented analytics and natural language processing (NLP), this executive could just ask the software to “compare Q3 revenue for each of the years from 2016 to 2021” for example.

Tip: Survey team leads to find out what they’d like to learn from your data. That way you’ll have a list of queries ready to go when you upgrade to an augmented analytics tool.

Augmented analytics in the real world

To help you visualize augmented analytics at use in real-life scenarios, and to help you ideate ways that augmented analytics can help your business, here are a few examples of how augmented analytics is already being used:

  • Social media marketing teams use AI-driven image recognition to parse millions of social media posts to help gauge sentiment about their brand.

  • Retailers use augmented analytics to scan sales data and produce actionable inventory forecasts much faster than using traditional analytics tools.

  • Customer service teams use augmented analytics to analyze countless hours of call transcripts, allowing them to predict what customers are having trouble with and how to best respond.

Augmented analytics software features

We’ve talked a lot about the power and magic of augmented analytics, but let’s pull back the curtain to examine some of the common software features that actually make augmented analytics work.

  • Data recognition. AI-powered data recognition can scan massive sets of big data and pull out relevant information. For example, revenue numbers, dates, or expenses.

  • Data preparation. Cleaning and organizing massive sets of raw data can be a very tedious and time-consuming task using traditional methods. But AI-powered data preparation can do a lot of this work for analytics teams automatically, allowing data scientists to focus on higher-value tasks.

  • Forecasting. The more you use your analytics tool and the more data it processes, the more it will learn about the history of your organization. Using data modeling, your augmented analytics tool will eventually be able to not only tell you about what has happened, but what might happen five or 10 years from now.


Forecasting in SAP Analytics Cloud software (Source)

  • AI-powered recommendations. Just like your favorite streaming service can recommend the next TV show or movie for you to watch, augmented analytics can use AI and machine learning to suggest the next data query for your team to investigate.

  • Natural language query. As discussed above, natural language processing allows anyone to interact with their analytics tool using natural language rather than coding or highly technical data queries.

  • Natural language generation. This AI-powered feature takes complicated data insights and converts them into a narrative format. For example, natural language generation might take a set of financial numbers and turn it into a sentence like “Your organization has increased revenue by 2.5% in rural markets since this time last year.”

There’s not always a solid border between traditional analytics software and augmented analytics software, and your current business intelligence tool may already include some of the features listed above. Ask your vendor what kind of augmented features your software has, and what your options for upgrading are. It might be a lighter lift than you realize.

Choose the right software to enable your augmented analytics

Unlike some other business applications, there’s no such thing as a low-tech way of enabling augmented analytics using a spreadsheet or pen and paper. Augmented analytics are still an emerging technology and require cutting edge software boosted by artificial intelligence and machine learning. The good news is that we live in a time when we have access to this kind of transformational software.

To start your search, you can browse our business intelligence (BI) software directory and filter for software with augmented analytics features like forecasting, projections, and real time analytics.

You can also browse our Category Leaders for business intelligence software to explore 15 different business intelligence software products that stand out in the market based on verified user reviews.


GetApp’s Category Leaders in Business Intelligence for 2022 (Source)

And if all this talk about the magic of augmented analytics has you excited to learn more, we have you covered with our library of business intelligence resources. Here are a few recent articles to read next:


1. Employers are desperate for data scientists as demand booms, ZDNet

2. Gartner Identifies Four Trends Driving Near-Term Artificial Intelligence Innovation, Gartner

3. Gartner Forecasts Worldwide Artificial Intelligence Software Market to Reach $62 Billion in 2022, Gartner

Note: The applications selected in this article are examples to show a feature in context and are not intended as endorsements or recommendations. They have been obtained from sources believed to be reliable at the time of publication.

About the author

Andrew Conrad

Sr Content Writer
Andrew Conrad is a senior content writer at GetApp, covering business intelligence, retail, and construction, among other markets.

As a seven-time award winner in the Maryland, Delaware, D.C. and Suburban Newspapers of America editorial contests, Andrew’s work has been featured in the Baltimore Sun and PSFK. He lives in Austin with his wife, son, and their rescue dog, Piper.
Visit author's page