The increasing availability of data to businesses has helped in making everything quantifiable. From the efficiency of small tasks to the potential outcomes of strategic decisions, everything can be measured in numbers.
And modern-day business intelligence tools help businesses visualize these numbers to make fact-based decisions -- but only to a limited extent. That’s because, while BI tools offer data visualization using interactive dashboards and reports filled with graphs, charts, and tables, decoding these visualizations remains a challenge.
Only data scientists, analysts, and a select few data-literate personnel are able to make sense of this data and derive actionable insights from it. And while these data scientists and analysts can help senior leadership make better decisions, the maximum value from data can be only realized if it is used by the entire organization.
However, expecting all the employees to be data-literate and interpret dashboards and reports accurately by themselves to make data-driven decisions is inviable. So, how can organizations empower their entire workforce with data? The answer is, by using natural language generation.
What is natural language generation?
Natural language generation is an advanced subset of artificial intelligence that can convert structured data from tables into natural language text. This technology has many applications in areas where there is a need to generate human-consumable content at a rapid rate, on a large scale, and with high accuracy. For instance, NLG is being used by e-commerce companies to write product descriptions in human-like language.
NLG technology can convert basic product features, specifications, and other product-related data from spreadsheets and databases into highly engaging product descriptions. The potential of NLG has also been explored in the field of healthcare, where it can be used to automate the generation of medical reports to help physicians save time on paperwork.
Similarly, natural language generation can be used to translate analytics data into concise reports written in everyday language. These reports can highlight the most critical facts that can help businesses make the best decisions. Anyone reading these reports can easily grasp the most vital facts without any possibility of misinterpretation.
Why BI needs natural language generation
Business intelligence tools are incredibly adept at processing large volumes of data and finding patterns and trends in them. They can also represent these trends in the form of captivating graphs, charts, and colored tables. However, business users may have a hard time interpreting these trends. Though the users may get some information from BI dashboards, they may end up missing out on the most noteworthy insights for solving their biggest problems.
On the other hand, adding the capability of text with natural language generation, to the BI Dashboards, will present the data in the form of easily understandable narratives written in a human-like tone. As a result:
businesses can easily spot hidden patterns and trends and understand the context surrounding each data-point,
and every member of the organization can utilize business intelligence and hence leverage the power of data to enhance their performance.
How BI tools can leverage natural language generation
Enterprises can use natural language generation technology to enhance their business intelligence dashboards with written narratives instead of just charts and graphs filled with numbers. As a result, employees will no longer need to perform analysis from BI dashboards and reports as ready-to-use insights will already be available in plain words.
Using advanced natural language generation-based business intelligence reporting, employees from all functions can be motivated to make data-driven decisions by providing them with relevant insights, written in any language they are most comfortable with.
This can lead to improved work methods in all departments, increased operational efficiency, and ultimately, increased profitability. And it all starts with leveraging natural language generation for business intelligence.