Natural Language Generation Tools for BI: 4 Questions to Ask

Natural Language Generation Tools for BI

This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Yseop CEO Emmanual Walckenaer offers four key questions to ask when evaluating natural language generation tools for BI platforms.

SR Premium ContentOver 37 percent of businesses and organizations currently utilize artificial intelligence (AI) systems to help with data collection and analysis. Now more than ever, more businesses are looking to implement business intelligence tools to positively impact their bottom line. In fact, data-driven organizations are 23 times more likely to acquire new customers, six times more likely to retain them, and 19 times more likely to be highly profitable.

One way that enterprise businesses can create an efficient and effective workplace with AI technologies: Natural Language Generation (NLG) powered business intelligence add-ons. NLG utilizes advanced AI to turn complex data sets into high-quality written narratives accurately, quickly and at scale. NLG enables businesses to automate manual, data-driven processes, alleviating employees from generating tedious financial reports or clinical study reports.

Natural Language Generation Tools for BI

What do NLG Add-Ons Help Solve?

Identifying and collecting large amounts of data often bogs employees down. Rather than spending valuable time trying to make sense of endless data, employees should allocate time to better understand data and use that to take action and build strategy around it, which is where NLG add-ons can come in.

When automating expert-level commentary from  business intelligence platforms like Tableau, an NLG tool is able to take it a step further than the traditional graphs and columns that are usually generated while providing the ability to work in the same interface. They are able to do this by automating those insights through language models, which creates a more digestible narrative and written summary based on the substantial amount of data that is being collected, while also producing enhanced visuals.

Data analysts and data employees are required to create specific reports monthly, weekly, and even daily. NLG is able to automate those reports and allows skilled professionals to focus more of their time on higher-stakes research and analysis, ultimately reducing the massive administrative burden felt by analysts and support teams.

How Can NLG Complement Other BI Solutions?

Currently, the average adoption rate of business intelligence tools among employees in mid to large companies is just 15 percent. The NLG add-ons are meant to complement BI solutions, and aren’t intended to replace them. With the comfort of receiving more valuable insights on a platform that employees are already working with is a surefire way for more companies to adopt these solutions as they become a one-stop shop for all work.

NLG add-ons can complement BI solutions by increasing the accuracy and efficiency of the tool. Black box, native solutions don’t show us what they are doing and how they get from input to output, but NLG add-ons can control the precise output when narrowing down the data and use the most beneficial information throughout.

Additionally, features like Ask Data, Data Stories and Smart Narratives on platforms like Tableau and Power BI can now be expanded further with add-ons and produce more in-depth analysis compared to their original use. For example Ask Data only provides visuals but NLG add-ons can build on those visuals with more in-depth analysis and can even generate text in multiple languages.

What Industries Has NLG Been Used Effectively in?

As NLG and AI continue to advance and develop, the applications and market size will continue to expand. By 2025, the banking and financial services segments are expected to dominate in terms of the overall NLG market share.

Financial services is a clear-cut sector for NLG due to the nature of how much data is collected and reported. Having NLG technology to help automate accurate reports can help a financial analyst make better decisions and accelerate document creation. With this technology, an employee could generate credit risk analysis across a portfolio in seconds, which expedites the typical reporting process and provides additional clarity when it comes to decision making.

Aside from financial services, there are many other sectors where NLG is currently being used, one being the pharmaceutical industry. Similar to the financial services industry, employees are left with a myriad of data to interpret. In this case, time is critical for the pharmaceutical industry and this technology ultimately leads to life-saving drugs and vaccines getting to the market faster.

A specific example of NLG technology working effectively in the pharmaceutical industry is the task of automating parts of the clinical study report (CSR). Researchers and scientists can

sometimes take hours to decipher and make sense of their research, but NLG medical writers help alleviate monotonous reporting. Doing this allows researchers to seek the analysis that sparks clinical innovation while also helping finance and sales teams recognize value in which drugs should enter the market faster than previously possible.

What’s next for NLG add-ons?

By 2025, researchers found that data stories will be the most widespread way of consuming analytics, and 75 percent of these stories will be automatically generated using augmented analytics, proving the demand for this technology that helps companies process the copious amounts of data they collect.

It is evident that NLG add-ons are impactful and when implemented correctly, they can improve business performance, save time, and create a more efficient workplace. As AI and NLG continue to mature throughout these industries, the impact of this technology will continue to expand. As data continues to be a primary focus for many industries and organizations, NLG add-ons will be a crucial assistant in making sense of it all and the possibilities can be endless.

Emmanuel Walckenaer
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