From Dashboards to Words
As humans, we think and communicate in narratives. Numbers and visualizations help, but taken on their own, they often don’t tell the full story. A good dashboard may be useful for showing trends and conveying metrics, but it’s words that resonate with decision-makers and provide them with important context and situational awareness — especially where complex, multi-source intelligence needs to be condensed to a concise and accessible narrative.
In many cases, this is accomplished manually, at great time expenditure, and subject to personal bias when determining what to put into the verbal summary. Starschema’s Inverba NLG™ solution is designed to use a combination of cutting-edge natural language generation (NLG) technology, saliency detection and effect advanced NLG realization (composing grammatically and syntactically correct sentences from a knowledge graph) to support the creation of actionable, well-written text-form reports directly from dashboards. Whether the purpose is creating verbal summaries or presenting dashboard data with a verbal explanation that puts matters into context, Inverba NLG™ is the optimal solution for converting enterprise dashboards into well-composed, informative prose.
How Inverba NLG™ works
Unlike other natural language report generators, Inverba NLG™ isn’t merely concerned with generating natural language reflections of data. Rather, using a combination of anomaly detection, pre-set values and a dynamic template, it creates a summary of data that points out what matters most. Instead of converting the data through a rigid template to a static, repetitive text output, a saliency model identifies what the most important results are, and adjusts syntactic, grammatical and compositional structure accordingly.
Templates provide overall sources as well as ‘saliency priors’ – that is, initial values that describe the saliency of a particular KPI – which is updated by the quantitative saliency metric of the KPI in question (thus e.g. a rapid spike in a KPI makes that metric more salient while no change makes the metric less salient). Inverba NLG™ then fills the dynamic template with data to prioritize high saliency data (e.g. unexpected/anomalous data, data that has exhibited a significant change or data meeting certain pre-defined critical thresholds). The result is a natural language summary of one or multiple dashboards that reflects the most important insights, does so in the most efficient way possible and puts data into context, thus guiding decision-makers’ attention to the most important facts.
Inverba NLG™ is designed from the ground up to transform dashboards into coherent, easy-to-read text that maintains sufficient consistency to support standardized reporting needs but offers enough flexibility to emphasize what matters most. Unlike purely template-driven approaches, Inverba NLG™ does not need a rigid template definition, and can guide the reader to the most important items first. A saliency model, driven by both pre-set business rules (such as reporting requirements) and data-inherent saliency detection (anomaly detection, trends, differentials), evaluates each metric and composes the output text in a manner that guides business users’ attention to the most important KPIs. Each of these saliency metrics can be provided by an external system through standardized APIs (REST, SOAP, RPC). Composition and sequential order are used to highlight the most salient changes, ensuring that even readers under time pressure get the ‘bottom line up front’.