The ability to give people the information they need when and where they need it is paramount for any enterprise. Solutions that deliver access to actionable insights based on trustworthy, real-time data are invaluable for business executives.
Providing decision makers with analytics presented in a format they can understand is an operational necessity; people ingest and process information in different ways.
Some people have the luxury to take as much time as they need to analyze an Excel spreadsheet and draw accurate conclusions from data. Many of us, however, do not have this luxury and require more than pie charts and graphs to get at the full story our data has to tell.
To achieve positive outcomes, management teams must fully comprehend the reasons behind key business metrics conveyed in corporate and financial reports. Understanding root causes for growth and retention – or loss and attrition – is the key to optimizing operations.
In finance, for example, adding natural language to reporting means you get expert, easy-to-understand written narratives that complement visualizations. This not only demystifies complex analytics, but also extends the reach of data intelligence across all levels of an organization.
With more insights into their data, management can make better-informed decisions, faster.
Currently, natural language generation (NLG) is combined with business intelligence (BI) and automation in myriad use-cases, such as enterprise financial reporting, investment portfolio performance, pharmacovigilance, clinical trials, compliance reporting, semi-automated journalism, and even weather reports. Gartner predicts that By 2022, 25% of enterprises will use some form of natural language generation technology.
In this blog series, we will examine how BI, natural language, and robotic process automation (RPA) are three driving forces behind intelligent automation.
This is part one of a multi-part series. Be sure to read parts two and three as well.
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