The Smart Data Blog

The Evolution of Insight: Exploratory Analytics

Posted by Patrick Wall on Mar 29, 2016 10:20:32 AM

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Exploratory analytics represents the evolution—and perhaps culmination—of conventional analytics and business intelligence options. It’s a combination of real-time data discovery and ad-hoc, graph-aware analytics that provides automatic, self-service insight for end users on all their data.

Unlike other means of generating data-driven insight, exploratory analytics enables users to parse through all of their data for enterprise-wide results regardless of structure or lengthy data modeling concerns that frequently delay the data integration process.

Such analytic insight, speed, and autonomy is optimized via Smart Data Lakes, and leveraged through any variety of intuitive visualizations and dashboards—including those organizations already use such as Tableau and Spotfire.

Smart Data Lakes

The full potential of exploratory analytics depends on Smart Data Lakes, scalable repositories that can encompass all enterprise data regardless of schema or structure. The underlying semantic model of these platforms and the semantic graph on which it operates facilitates seamless integration of any data, which typically consists of historic strategic data (which may be structured) and timely, tactical big data (unstructured or semi-structured). These technologies discern the context and relationships between elements to deliver profound insight of data-driven inquiries. Best of all, they do so quickly enough so that users can encompass all of their data to ask more questions that yield increasingly specific information for individual usage.

Empowering Visualization Tools

After uses have defined and analyzed the data from within the Smart Data Lake, it can be pushed out to end users. Most importantly, they can do so while leveraging self-service visualizations they’re already familiar with including Tableau, Qliksense, and any others. Exploratory analytics results from the Smart Data Lake are pushed out to these tools so it can be distributed across the organization. Because they can utilize dashboards and visualizations they’re already familiar with, end users ensure that the focus of exploratory analytics remains on results, visualizations, and manipulating them, not technology.

Overarching Value

The value in exploratory analytics lies in the ability of users to facilitate real-time data discovery and analytic insight on all enterprise information assets, so they can continually parse through and refine those results. They can access the results of analytics with the same visualization and dashboarding tools that they’re already using now. By leveraging the Smart Data Lake, end users ensure that they can continually refine their questions and the answers that data provides them.

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Topics: Big Data, Data Lake, Anzo, Analytics