Seven Steps for Implementing Data Discovery Tools
Expectations for what business intelligence needs to deliver are changing fast. At one time, BI technology demanded that users overcome steep learning curves, backlogs for applications, delays for batch queries, limited historical data, and static reports. Now users expect more. Tools and technologies are rapidly evolving to satisfy greater demands for ease of use, timely data access, and deeper analytics.
Data discovery is what many industry observers call the deeper analysis and more expansive visual presentation of data that many users desire and are pursuing with a new class of tools. Self-service features and functionality are critical to data discovery, as is ease of use. Leading tools for data discovery are enabling users to learn more about the data by asking iterative questions and following lines of inquiry rather than stopping with the limitations of standard BI reports and drill-down capabilities.
You will learn:
- What “data discovery” means and how it fits new user requirements for data analysis and representation
- How data discovery is different from classic BI
- Real-world examples of how data discovery is being used
- How data visualization, cloud computing, in-memory computing, and more fit into the data discovery vision