Augmented Analytics: Why it’s the Future of Data Analytics

Artificial Intelligence
Dec 14, 2023
Augmented Analytics: Why it’s the Future of Data Analytics

At a time when everyone with internet access and a LinkedIn account is talking about artificial intelligence and language learning models, it’s natural to be a skeptic about the topic of AI. Is it really the answer to all of our life problems? Is AI going to replace our jobs?

The answer to both questions is - probably not. But AI can do something magical for data professionals: help you get actionable insights from data faster. This is called augmentative analytics.

Today, we’ll show you what augmented analytics is and why anyone who deals with data should be excited about it.

What is augmented analytics?

Gartner describes augmented analytics as “the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms”.

But in simpler terms, augmented analytics means using AI algorithms to get insights from data faster. Instead of taking hours to stare at tables, charts, and other types of visualizations, you can use AI and ask your favorite BI tool for help.

AI, machine learning, and automation can help in many ways in the analytics process.​​

ai comic by marketoonist

How augmented analytics can help

Automated data preparation and cleaning: AI can add missing values in your data fields, identify errors, clean up formatting, remove doubles, and much more. This eliminates a lot of the manual work involved in analytics.

Predictive analytics: based on previously available data, AI can make predictions for the future. For example, if you’re consistently selling a group of products during the holiday months, AI can accurately predict what those products are so you can make sure they are in stock and promoted properly.

Natural language processing (NLP): getting insights by analyzing charts can be… Tedious. And not only that but the person reading needs to understand the data. Instead, you can use language processing to ask AI about insights, in plain English. For example: “tell me the highest performing marketing channel in terms of cost per acquisition in the last three quarters”.

Anomaly detection: AI can quickly spot if there is a sudden spike or change in data. For example, our customer Enersee was able to uncover a sudden increase in electricity use for one of their customers, saving them 200EUR per day.

Personalization: imagine getting a data visualization dashboard from your product but you want even more personalized insights. You can ask AI to personalize that dashboard and highlight the data that is important to you as an individual user.

To explain the main advantage of using augmented analytics, think of a situation like this. 

You have a mountain of snow to plow and a single shovel. You could grab the shovel, get to work and finish the job in eight hours. Or you could hire a snow plow truck to remove the snow so you have the entire day to do meaningful work you love and enjoy.

Augment analytics is your snow plow truck - a device that helps with mundane tasks, freeing up your time so you can spend it somewhere else.

Augmented analytics is about to deeply change the way we collect, analyze and use data. If a business aims to stay competitive, getting familiar with augmented analytics would be a smart investment in the future.

Benefits of augmented analytics

No matter the business intelligence tool you’re using, there are certain augmented analytics benefits you’re going to find in each one. 

Let’s look at a few ways augmented analytics can help anyone to get more from their data faster.

Speed of insight

With predictive analytics capabilities, you spot problems before they arise. AI can take a look at your existing data and make accurate predictions, but also spot inconsistencies and drastic changes in values.

You can also go from raw data to meaningful insights much faster. AI can take your data, clean it up and properly format it so that it is ready for visualization sooner. It may not always remove ETL processesout of the equation entirely, but it can make things more speedy.

Depth of insight

No matter how knowledgeable someone is about data, they cannot possibly process thousands of rows of it quickly. Or even if the visualizations are already done, it’s not always easy to make logical conclusions all on your own.

AI models are trained on massive volumes of training data, which means they can get to deeper insights in the same time it would take your BI tool to do basic data analysis.

Thanks to natural language processing, there is another major advantage that is not mentioned very often. AI models can not only take commands in plain English but also explain the conclusions from your data in that same language that is easy to understand. It’s the true democratization of data literacy.

Last but not least, thanks to these enriched insights, stakeholders can make important decisions with more confidence.

More accuracy

As mentioned, AI is trained on massive amounts of data and with complex machine learning models, the predictions and insights you get are guaranteed are generally more accurate.

When you set out to make conclusions yourself, you not only risk invalid and inaccurate data, but you also approach each data set with unconscious bias

Some research shows that AI too can be prone to human-like bias. This is why it’s important to always do a sanity check on any of the data analysis that an AI model gives you. While artificial intelligence can do the heavy lifting, it’s always best to do a final check with an actual human being in charge.

While AI is better at crunching numbers, it still lacks the nuances of interpreting context behind the data.

Simplifying complex workflows

You no longer need a team of data scientists to collect, clean and visualize data. With AI taking care of the repetitive, tedious tasks, your data science team can focus on finishing more work that matters.

For example, you could get data from five different sources. Some of that data may be missing fields or have different formats. Instead of tasking your data science team to run the data through a tool, you can ask AI to clean the data up according to your instructions so it’s immediately ready for analysis.

Better customer experience

You now have access to more data in real time, allowing your predictions to be based on real-life use cases. Augmented analytics tools can help you make the kind of decisions that impact your customers’ lives as they use your product.

For example, you could unearth that most customers get the highest value from your product after a week of use and then the number of daily active users suddenly drops off. Thanks to using an analytics platform powered by AI, you can discover that investing in additional onboarding can help improve those numbers.

Example use cases of augmented analytics

What does augmented analytics look like in practice? Here are some ways you can harness AI and analytics to make data-driven decisions for yourself and your customers.

Supply chain analytics: you can use AI and machine learning analytics to predict supply chain disruptions. Using historical data, AI can predict the impact of weather conditions, public holidays, supplier logistics and more to tell you if your supply chain will be disrupted.

Healthcare analytics: medical professionals will be able to detect health issues early by analyzing patient data and benchmarking it against training data.

Social media and customer feedback analysis: analyzing textual data from social media, blogs, reviews and other online platforms at scale. AI can quantify qualitative feedback and get recommendations for improvements in your product, support or customer service.

How will augmented analytics change business intelligence (BI)?

At Luzmo, we’re just about to launch our AI chart generator. Most other BI tools and analytics software are going the same route, with the ranks of Tableau and PowerBI adding AI features to their tools.

Here are some of our predictions on how augmented analytics will impact the world of BI.

Data analytics through natural language

Democratization is the name of the game. Up until recently, only data scientists had the privilege of collecting data from various data sources and making sense of it through ELT and finally, visualization.

Thanks to augmented analytics, anyone who can type a simple questionith the basic knowledge of English will be able to explore data and KPIs on their own terms. Imagine asking your BI tool something like “Show me the top sales performers in the last four quarters stacked against their tenure in the company”.

A query like this would show you if there is a correlation between how long someone has been in your business and how well they are able to sell. Getting to that data with traditional BI tools would take considerable skills and knowledge

Increased occurrence of embedded analytics

Having complex data by your side to make complex decisions is amazing as is. But having that data embedded in your product? Even better.

With increased ease of use and natural language generation (NLG), more software and web development companies will start adding embedded analytics features to their tools. This will democratize data as more casual users will be able to explore graphs and visualizations showing their key product metrics.

This functionality will lead to a self-service model where any product user can do the analysis process on their own terms. They can type out a natural language query and unearth insights on their own, without getting in touch with someone from your team.


Even top experts can make mistakes in predicting future behavior. AI is not perfect, but with the increasing amount of data you feed it, and its tendency to look past biases in favor of statistical significance, it can make great forecasts.

In general, the more data your analytics tool is trained on, the more accurate the outcomes will be. That leads to better decision-making and actionable insights, and eventually, a significantly reduced workload for business users.

Data literacy

The typical AI tool will come trained on massive amounts of data out of the box. You then feed it more of your own historical data and magic happens. Well, not exactly magic, but something like it.

Augmented analytics will allow users to stop looking at data in isolation. Instead, multiple data points can be used together and put in a wider context. This leads to better benchmarketing of industry standards, but that’s just the tip of the iceberg.

The end result is that ANYONE will be able to get to the data they need with just an analytics solution by their side. Business decisions of the future will be data-backed and not based on gut feeling.

What the future holds for augmented analytics

Data analysts and scientists, business owners, product managers - should all be happy that AI and ML are advancing at such a rapid pace. Augmented analytics won’t kill any jobs. It will, however, make data discovery and data visualization available to everyone. AI apps are your data helpers, not your competition.

And if you want to learn more about how AI and data analytics can work together, download our whitepaper on using ChatGPT for data analytics!

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