Dashboards and reporting platforms offer limited views of data and even augmented search-based BI is daunting for most business users. Many users do not know what questions to ask, resulting in “blind spots” in the typical decision-making process. Meanwhile, AI teams are working on data science models consumed by a small fraction of executives.
Consequently, users are forced to base their decisions on static dashboards or reports that often do not drill down to every part of a business. Data doesn’t speak English and users don’t speak SQL – how do you bridge that gap to make data more consumable?
This paper highlights the future direction of analytics, including:
- Scaling BI and analytics tools beyond analysts to regular non-technical business users
- Leveraging a combination of home-grown and open source machine learning models to automatically surface and deliver business insights
- Interacting with data through natural language audio-visuals instead of creating more reports and dashboards