Regardless of how large an enterprise’s operations may be in size and scale, it is made up of hundreds or even thousands of employees at different levels and in different functions doing small tasks. And each of these tasks performed by the employees contributes to the overall performance of the enterprise.
Delivering business intelligence data to these employees in the company leads to improved decision-making at every level. Due to these improved decisions, all the employees can work using the most optimal methods and be more productive in their functions. The contributions of these employees add up and compound to massively enhance the overall business performance.
Thus, achieving a quantum leap in operational performance begins with the democratization of business intelligence. And a key enabler of BI democratization is natural language generation. Here’s how natural language generation-enabled business intelligence reporting can make data accessible to all employees of an organization:
Real-time business intelligence reporting
Traditionally, analytics and business intelligence have mostly been used by top business leaders for strategic decision-making. These business leaders get their business intelligence reports periodically prepared and curated by data analysts.
The reports — accompanied by visualizations and analysts’ comments and recommendations — make it easy for business leaders to gain crucial insights and make decisions. However, manually creating such detailed yet simple reports for every other employee in the organizations, such as those working in operations, is not practical.
Unlike the top leadership at enterprises, operations personnel have to rely on raw data, tables, and charts to make sense of real-time analytics and make decisions. They need to be aware of data that changes on a daily or even hourly basis.
And generating reports or explaining dashboards so frequently proves to be exhausting for both analysts and executives. And the organization as a whole misses out on achieving operational excellence.
With the help of natural language generation, the information in business intelligence dashboards and reports can be translated into easy to understand written narratives. These dashboards contain the most vital real-time data pertaining to the enterprise’s operations.
For example, a logistics supervisor can use a real-time dashboard to know details about the location and performance of delivery vehicles. Given the right information, they can identify deliveries that are running late and optimize the routes to speed up the process.
Using such dashboards and reports, other operations personnel can make decisions in response to real-time changes. Since every employee is updated with real-time information in a simple format, the enterprise as a whole gains agility in its operations.
Function-based customization of business intelligence reporting
Using AI and NLG, data-driven narratives can be created for and tailored to different functions. These reports help employees performing different functions to understand business intelligence within the context of their function. This leads to an increase in the adoption of BI tools throughout the organization, leading to an improvement in the overall business performance.
For instance, while the sales head of an enterprise can find enough actionable insights from an overview of the enterprise-wide sales performance report, regional sales managers may not find value from this data. They may require reports that focus on the sales performance in their region, with a detailed breakdown of the performance of individual sales employees working in that region.
And most large enterprises have hundreds or even thousands of sales personnel working in different regions. Generating customized performance reports for all these people is impractical for a team of human analysts.
In such a case, natural language generation-based business intelligence reporting can help to create customized performance reports or dashboards. Similarly, managers of different bank branches can receive performance reports customized to their branches, giving them insights that are relevant to them.
Businesses are beginning to realize the value of democratizing business intelligence and the role of natural language generation in the process. As a result, NLG adoption is on the rise.
Gartner has predicted that NLG will become a standard feature of 90% BI and analytics platforms by 2020. Thus, to stay competitive and relevant in an increasingly data-driven business landscape, enterprises will have little choice but to adopt business intelligence reporting powered by natural language generation.