Is Augmented Analytics the Next Frontier in Business Intelligence?

Learn how augmented analytics leverages AI to enhance business intelligence, enabling faster insights, improved decision-making, and increased productivity.

Business intelligence platforms are now firmly established in the corporate world. However, many decision-makers are exploring how to combine BI platforms with artificial intelligence. Those efforts have led to the rise of augmented analytics. 

What is Augmented Analytics? 

Augmented analytics involves using AI and machine learning to enhance how BI tools work and how people interact with them. The industry is relatively new but consists of tools and software that help users prepare data, gain insights, or gain more context about trends. 

How does Augmented Analytics Improve Business Intelligence?

Augmented analytics products support those wanting to do more in less time while working with their business intelligence platforms. People do not need extensive data analytics tools, AI expertise, or tech skills to benefit from tools in this category. 

However, seasoned data professionals also appreciate how augmented analytics can save time by allowing them to uncover valuable insights much faster than they could manually. One example involved using augmented analytics to review the outcomes of hundreds of German marketing campaigns. People then found that a specific one significantly increased the average order value in younger customers. 

It would have taken people days or weeks to sift through the data by hand. However, they could reach that conclusion quickly and confidently by combining BI and AI through augmented analytics.

Augmented analytics tools also help people perform thorough analyses, reducing the chances they will heavily focus only on the findings that support their hypotheses. These tools can analyze more information faster, allowing individuals to make clearer, more accurate situation assessments.  

Some products also integrate with a company’s existing tools, making it easier for people to work with data from various sources or find out specific things about it without switching back and forth between multiple interfaces during workdays. Then, they become more productive and can see internal information within a richer context.

Also Read: Explained: Graph Data Science

What must People Consider When Implementing Augmented Analytics? 

A recent research report indicated that the 2023 value of the augmented analytics industry was  $16.60 billion, and the compound annual growth rate is 28% from 2024 to 2030. Another finding was that augmented analytics software accounted for an 80.5% market share in 2023. That suggests people have a numerous and ever-growing assortment of vendors and products to consider. 

Those interested in implementing augmented analytics should begin by asking themselves which problems the technology will help them achieve and which problems they hope to overcome. They should also list and categorize the tech products they currently use and note the ones they wish to integrate with augmented analytics platforms. 

It is also essential for them to consider their budgets and whether they would prefer a large, one-time payment or to spread the costs by finding a software-as-a-service company offering augmented analytics tools. 

People must verify that their chosen vendors follow security best practices. That is essential if they use augmented analytics platforms to study customer data. Statistics indicate individuals feel 20% more satisfied when they have positive service interactions. That finding illustrates the importance of giving lasting, favorable impressions. It could become easier to do that if company representatives compile data that customers give freely. However, they will only willingly and eagerly provide it if they believe businesses will keep sensitive data safe. 

What is the Future of Augmented Analytics?

BI tools are so widespread that many executives can hardly remember days at work without them. Could the same thing happen for augmented analytics, too? Although it is too early to say, that possibility seems likely. 

Many people are already comfortable using AI in other work-related use cases, especially if they believe it will save them time or optimize their results. Some executives have explored forward-thinking ways to combine business intelligence and generative AI tools to benefit from augmented analytics. In one example, representatives from a lending platform needed a quick and trustworthy way to assess risks connected to business owners seeking financing. 

They developed a methodology to apply risk adjustments to internal data and then created a proprietary search tool that uses generative AI to summarize metadata. Finally, machine learning models give underwriters increasingly accurate, risk-adjusted determinations about clients. 

Additionally, the company found that this approach was a significant productivity booster. Although some searches previously took employees several minutes to perform, they can now do some in 30 seconds by automating some steps. However, those involved with this deployment emphasized the need for employee involvement with training. That engagement reduced inaccurate AI responses that could mislead users. 

Besides the likely rise of generative AI in augmented analytics, people can expect that current and future tools will integrate with more products. That improvement caters to how many businesses have dozens of data streams and sources to analyze. Smooth and highly functional integrations facilitate examining them. 

Also Read: Is Your Data Talking? AI and ML Can Help You Listen

Is It Time to Use Augmented Analytics?

This overview may have convinced you to explore how your company could capitalize on augmented analytics. You will get the best results by staying focused on your goals and viewing tech platforms as options that facilitate their achievement.