Tableau is a well-established and widely used analytics and visualization platform. Its natural language inquiry tool, Ask Data, allows users to explore visualizations without requiring coding skills. It understands temporal and qualitative terminology so it can generate time intervals and look for “high” or “low” values. Explain Data allows for further discovery and provides dynamic visualizations; a user can select a particular data point in a visualization and obtain a narrative explanation for that point. For example, if an extreme value or outlier is found, Explain Data can suggest a reason for the value, based on statistical models it develops on-the-fly. “Data is no longer just a competitive advantage. It is critical to the health—and often survival—of an organization regardless of industry or field,” said Richard Tibbetts, VP of product management, Tableau, at Salesforce. Making data more broadly accessible and understandable will strengthen departments throughout the organization.
Trends for the near future
Visualization provides a more engaging user experience, is better at illustrating patterns and relationships in data, and can provide better insights from large amounts of data than quantitative analytics. Interactive visualizations, automated insights, natural language responses to visualization queries, and other intelligent forms of analytics are categorized as “augmented analytics” in that they are augmented by AI and go beyond basic visualizations.
Gartner predicts than within three years, the majority of results of analytics will be presented and consumed via data storytelling, and that 75% of these stories will be generated automatically by augmented intelligence and machine learning. Storytelling has a long history as a means of communicating knowledge. It often entails simplifying information and personalizing it for a specific audience. By providing information in context, stories become more meaningful and more memorable. Storytelling and visualization work together to combine the best of both worlds, creating a data-driven and engaging user experience.
Similarly, visualization and so-called democratization of analytics go hand in hand, because visualizations are so much more accessible to non-technical users. In the past, data scientists and analysts were needed to interpret complex data for users, but with a visualized interface, business users can explore the data and use it to make decisions. Data scientists and analysts are needed to collect, manage, and analyze the data, but when the results are presented in visual form, they can be used by a much wider audience.
As with many enterprise applications, mobile access has grown to be essential in analytics. The trend toward presenting visualizations on mobile devices rather than on the desktop is certain to continue. According to the World Advertising Research Center, nearly three-fourths of internet users will be “mobile only” by 2025. Already, smartphones and tablets account for a majority of media time. However, putting visualizations on a mobile device is not as simple as just making them smaller. The ability to navigate, zoom, and get to additional details needs to be tailored specifically to mobile devices.