Why Your BI and Analytics are Incomplete without NLG

Natural language generation adds a new level of intelligent automation to BI and robotics process automation, freeing up knowledge workers’ time and enabling higher levels of productivity.

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As we move forward into the digital age, we’re now creating 328.77 million terabytes of data every day. Smart companies and institutions rely on data to make informed decisions and create competitive advantages. However, with so much data to sift through, knowledge workers are taxed with extracting the essential nuggets and presenting their findings in an easily digestible format.

Technologies like BI (business intelligence) and robotics process automation (RPA) help alleviate some of the burdens, but they still require mining a vast pool of information in search of insights, which can be extremely time-consuming. As businesses try to get to the nirvana of real-time data analyses, it still takes several days for a person to convert charts, graphs and other data points into a meaningful summary.

To achieve positive outcomes, decision-makers must know the reasons behind key business metrics conveyed in corporate and financial reports. Natural language generation (NLG), an advanced form of artificial intelligence, converts structured data into highly valuable, easy-to-understand written content. This both demystifies complex analytics and extends the reach of data intelligence across all levels of an organization.

Put simply: with access to actionable intelligence from data, managers can make better-informed decisions, faster.

NLG is often embedded into business intelligence (BI) and automation platforms to communicate the full story from all underlying data. In many instances with traditional BI solutions, the lag time between acquiring the data, interpreting it and taking action is where competitive advantages are gained and lost. And In some cases, the consequences don’t just affect profit margins and market share, but the ability to save lives.

In pharma, for example, NLG can help analyze large sets of data for clinical trials, in addition to pre-clinical studies, health records, and genetic profiles, freeing up researchers to devote their time and energies to other essential tasks. It also allows researchers to more easily recognize trends and patterns, thereby allowing them to develop hypotheses and make decisions at a faster rate.

An international study published by the Center for Information and Study on Clinical Research Participation (CISCRP) showed how profound the need is for an easier way to provide clinical trial reports. The study of 12,451 representatives from pharmaceutical, biotechnology, contract research organizations, and investigative sites, found that an overwhelming majority (85 percent) of them would like to receive a plain-language summary of the clinical trial.

Yet, 61 percent said they didn’t receive any information on the findings from the trial in which they had an interest. Some experts noted that one of the reasons for this might be the difficulty in creating reports that can be understood by the layperson.

NLG can improve the integrity of the data collected during clinical trials and encourage trust in the trials and processes. Pharmaceutical companies that make use of NLG can streamline clinical safety reporting processes without compromising the integrity of their data.

The growth in the market shows that businesses recognize the value of NLG. Verified Market Research predicts a 19.52 CAGR for NLG, from $450.12 million in 2021 to $2.617 trillion by 2030.

The Need for Advanced Analytics is Everywhere

Whether we’re looking to financial analysts for retirement investment advice, life sciences experts for clinical trials summaries, retailers for personalized shopping experiences or a host of other sectors for their expertise, there are three things we always need: accurate information, timely information, and information that users can easily understand. Natural language generation technology can create a simple user experience that makes insights available to anyone, regardless of their IT expertise.

Explore this option that can help your clients democratize data and analytics insights to everyone in your clients’ organizations who needs them.

Jay McCall

Jay McCall is an editor and journalist with 20 years of writing experience for B2B IT solution providers. Jay is co-founder of XaaS Journal and DevPro Journal.


Jay McCall is an editor and journalist with 20 years of writing experience for B2B IT solution providers. Jay is co-founder of XaaS Journal and DevPro Journal.