Analyst Report: Part 7 of 9, Business Intelligence on Big Data/Data Lakes: Removing Silos & Operationalizing Your Data

On Big Data

This is part 7 of a 9-part report, access all other parts here:

Part 1 – Data Connectors
Part 2 – Virtualized Data Layers
Part 3 – Data Integration
Part 4 – In-Memory Database/Grid Platforms
Part 5 – Data Warehouse Platforms
Part 6 – Business Intelligence (BI)
Part 8 – Big Data/Data Lake Platforms 
Part 9 – Data Management and Governance

Strategy & Business Considerations

Organizations with a preference for BI’s optimized, sematic models, who are running up against data volume and scale limitations that are present in most BI platforms, will make ideal customers for BI on big data solutions. BI on big data solutions don’t embrace the big data tenet of “schema on read” (storing data in raw form and adding structure to it at query time), and many enterprises won’t either. Those who prefer the schema-on-read paradigm and who want their analytics to produce not just answers, but also investigate the right questions to ask, should look at data lake solutions, which we cover in the next section.

Table of Contents

  1. Summary
  2. Strategy & Business Considerations
  3. Technical Considerations
  4. Technology Evolution & Landscape
  5. Key Players
  6. About Andrew Brust
  7. About GigaOm

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