What’s Behind the Growth of Augmented Analytics

Written By Alla Levin
September 25, 2019
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What’s Behind the Growth of Augmented Analytics

Augmented analytics is an extremely fast-growing segment of the business intelligence world. It’s estimated that it will grow by over 32 percent annually from 2017 to 2026—reaching a value of over $52 billion by that time.

But what has augmented analytics? And what’s behind its rapid growth?

What Is Augmented Analytics?

When you hear the term augmented analytics, your mind might envision some kind of augmented reality BI tool. While this might be something that exists in the future, it’s not what’s being referred to when people talk about analytics now.

So, what is augmented analytics, then? It’s a type of analytics that incorporates elements of machine learning and natural language processing.

By combining these two elements, augmented analytics is poised to provide a few benefits to businesses. First of all, it allows for better data processing.

Augmented analytics lets people query data using natural language via conversational and search-driven analytics — rather than requiring them to use technical language or Structured Query Language (SQL).

Augmented analytics tools also harness the power of artificial intelligence (AI) and machine learning (ML) to automate the process of discovering insights and sharing them with users.

What Are the Benefits?What Are the Benefits of Augmented Analytics

There are several reasons why this kind of analytics is something your organization probably wants to have in its toolbox.

At its core, this technology is all about making operations more efficient by reducing the work required to get insights from data. There are a few significant benefits that come with this:

  1. It can make data analytics more accessible to organizations with fewer resources. If you have data coming in from a host of independent sources, it can be tough to make those all compatible with each other. You would typically need a data scientist to work through this. But it’s expensive to hire these employees. And their value is not best utilized by having them condition data. Augmented analytics streamlines this process—allowing for fast, accurate insights more economically.
  2. More people within an organization can contribute to data insight discovery thanks to augmented analytics. This is because this form of analytics can pull data directly based on natural language processing. The ability to search for answers in this straightforward way makes it possible for people without a background in data to perform self-service BI. In this scenario, insights are accessible to all.

This concept works in a similar way to a search engine like Google. The user simply has to type in their request, and they can receive instant results based on that query. It’s even possible to build custom dashboards with this method. Stronger dashboard analytics is the foundation for organizations wanting to get more out of their data on the user end. ThoughtSpot is one BI tool that’s incorporating these interactive elements into its platform.

  1. More employees will be interested in adopting data analytics tools following the implementation of augmented analytics. As mentioned in the previous point, the underlying theory behind augmented analytics makes it possible for people who aren’t data professionals to pull insights.

When your whole organization can play a role in the process of data analysis, it shifts the culture to be more data-centric. This has enormous advantages since data tells the true story of what’s happening. The more people refer to data for decisions, the better those decisions will be.

  1. Actual data experts won’t have to spend their time working through a backlog of low-level data requests. If analysts and scientists have to spend a large portion of their time working on queries from other employees, they can’t dedicate as much energy to higher-level data work. While all these things are essential, augmented analytics frees up time for data professionals so they can work on more complex problems.
  2. It can improve the financial health of organizations by giving employees the insights they need to improve operational efficiency and drive customer satisfaction. A company’s ability to make data-driven decisions impacts its bottom line.

Data analytics is going to continue playing an essential role in shaping the business world. Implementing augmented analytics can help a business get more out of their BI tools.

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