Beyond BI Reporting: Power of NLG

Natural Language Generation (NLG) has evolved in the last few years. It now gives the required boost for analytics and reporting solutions. Business Intelligence (BI) narratives generated by NLG platforms will help enable faster data analysis and interpretation.?

Boost the Pharma analytics solution using NLG

Today, most of the pharmaceutical enterprises face challenges in getting correct insights from huge volumes of data. They also face increased pressure to reduce costs and meet customer demands. NLG has the power to handle large amounts of data and produce useful insights at scale and speed.

NLG is gaining traction in solving many problems for the pharmaceutical industry where there’s a need to summarize large amounts and rapidly changing data and where audiences don’t engage well with data, even in visual forms.

NLG is the process of producing meaningful phrases and sentences in the form of natural language. It’s a subfield of Artificial Intelligence (AI), that transforms data into plain and simple content. NLG provides insights in clear, human-sounding language that empowers people to understand the data without being a data expert.

In the era of Big Data, where data volume is measured by petabytes and exabytes, computers have access to huge amounts of data. Until the last few years (even with the current trend), the focus is on getting more data into computers. Data processing and storage have advanced with its speed and size.

The problem has now flipped. Our computers have access to vast data repositories, but the problem is trying to get actual value and insight from that data. BI and NLG platforms are trying to solve this problem. Generating meaningful insights will help businesses and solve the ultimate goal of the Big Data era. This distinction doesn’t mean that NLP and NLG are entirely unrelated. Reading and writing are separate but related challenges for computers, like for humans. For many research groups, NLP and NLG are two separate workstreams under the AI umbrella, while many researchers consider NLG a sister workstream of NLP.

Pharmaceutical companies can use NLG for monitoring the performance of sales reps, writing clinical reports and effective patient awareness programs. NLG can simplify data preparation and analysis, reduce errors in reporting, automate clinical documentation and accelerate drug development cycles, which will save a lot of time and money for pharma professionals and increase their productivity.

NLG needed in era of Pharma BI solutions

Most medium and large-scale, global organizations have report- and dashboard-based analytics solutions. These reports and dashboards help with prescriptive and descriptive analytics. End-users access the reports as data-refresh and on-demand content, which are available on various access mediums like web browsers, iOS and Android devices.

Typically, BI solutions lack usage problems in case there aren’t many action-driven insights. End-users look for insights to make decisions on daily and weekly executions or to create long-term strategies. In absence of accurate insights, the end-user can lose interest, resulting in a decline in overall usage. NLG looks at nitty-gritty aspects of data and will be very helpful to generate action-driven insights.

Visualizations in User Interface (UI) and User Experience Design (UX) are integral components to sharing insight to the human brain. However, in lack of the right selection of visualization types or direct insights, human brain tend to ignore the benefits coming out of a visualization. For example, a bar chart indicating monthly-market-share distribution for a given product may not change frequently. In fact, over a few weeks or months, its trend may remain the same. To depict the same data set, NLG will do a better job providing the right insight into the highlighted pain points for any product in a given month. These NLG insights add value to the end-user and give a boost to existing visualization.

The beauty of an NLG narrative is the removal of misinterpretation of the data. It works well with both structured and unstructured data. Imagine applying NLG on hundreds of pages of clinical-trial data and converting it to a readable format for the majority of a pharma organization’s leadership team.?

Graphical representation of data may include the summary of the data, however it often misses data nuances. NLG insights put information in context, thus removing bias and misinterpretation.

Adoption of NLG functionalities for existing and new solution

Many organizations have heavily invested in BI solutions in the last few years. These solutions have been enhanced with functionalities provided by the BI platform and get richer with more data sets. Adding NLG to existing solutions will enhance usability. Gartner predicts that by 2022, 25% of enterprises will use some form of natural-language-generation technology.

Still, NLG is not magic. Strong output requires deep domain knowledge, analytics and storytelling skills.

There are many NLG platforms available that work smoothly with leading BI platform such as Tableau, Power BI, MicroStrategy and Qlik. Integration with NLG platform will require the key steps listed below:

1. Define the use case for existing and new reports and dashboards.?

2. Re-assess the UI/UX components of reports on dashboards. NLG narratives take real estate of your BI solution. Plan or reshuffle visualization accordingly.

3. Develop NLG narratives. While many platforms provide decent default narratives, it’s important to customize the language and metrics based on user group.

4. Integrate with existing BI solutions.

Conclusion

Adding NLG insights into reporting and analytics solutions will help increase application usability. The use of NLG can be planned for existing solutions, where pharmaceutical companies have made huge investments over the years, and to create new applications as well. In both scenarios, NLG will add value to smart insights, which will lead to better decisions.

Using NLG in a platform will remove the misinterpretation of data and give direct insights to the end-users. The combination of visualization and NLG narratives will enhance the overall usability and user-experiences of the applications.

By: Alok Choudhary & Akshay Jain