Related to: Natural language processing (NLP), natural language querying (NLQ)

Natural language generation (NLG) is a sub-branch of artificial intelligence that generates textual explanations, comparisons and summaries of business data in a human-like way. It combines contextualized narratives with analytical output to express the most important and interesting concepts that lie within data in a universally consumable language that is relevant and timely.

NLG uses automation and AI to provide answers to data at a speed far faster than a person can decipher and communicate manually. It helps both regular business users and analysts by automating routine tasks such as data-driven reporting or financial portfolios by providing an efficient way to intuitively communicate the most important data within with summary and detail-level explanations that the individual user can control and leverage based on their needs.

Traditional NLG once only translated data into text, but modern analytics solutions now use enterprise-grade natural language generation technologies so that their AI-enhanced analytical features can communicate critical concepts with conversational, expressional language that doesn’t just present the facts, but all the nuances and hidden patterns as well to explain the full meaning and highlight the key findings within our data.

What is natural language generation used for?

The practical applications for natural language generation are various. The most common use of the technology is for user analysis in self-service business intelligence dashboards and reports, as well as business reporting, data analysis and intelligent personal assistants. For analysts and specialists, NLG can be used to effectively reduce the cost and time of conducting repeatable analysis, such as for operational reporting. For users, it provides fast, instant and reliable answers to their queries from data that can enhance any manual exploration and discovery.

In modern analytics platforms such as Yellowfin, NLG is embedded within features such as Assisted Insights and Signals to further enhance business users’ insight generation and discovery efforts with easy-to-understand, instantly generated explanations and comparisons.