Wikibon research has demonstrated that most Big Data projects in 2014 actually lost money, returning an average of 55 cents on the dollar. However, a few companies stood out, with ROI of three times or more on their Big Data investments.

Interviews with those companies show that one thing their Big Data projects have in common is inline analytics driving algorithms that directly drive change in operational systems-of-record, writes Wikibon CTO David Floyer in “Follow the Money: Big Data ROI and Inline Analytics.” These algorithms were usually supported by data tables derived from deep data analytics from Hadoop systems and/or data warehouses. This allowed these organizations to improve operational processes across the company as a whole rather than just “enlightening the few with pretty historical graphs.”

Inline analytics are also the key to making real-time decisions, because they bypass the extract, transform and load (ETL) processes that can take anywhere from hours to weeks to move data into the database. The advanced systems the Big Data winners developed leverage advanced technologies including parallelism, data-in-memory, and high-speed flash storage.

The report includes a detailed comparison of the Aerospike, IBM BLU, Oracle 12c and SAP HANA. The key finding, Floyer writes, is that success of Big Data projects should be measured by:

• The time taken to create inline analytic algorithms to automate decision-making directly into operational systems-of-record; and,
• The effectiveness of supporting deep data analytics to reduce the cycle time for improving the inline analytic algorithms and finding new data streams to drive them.

The full report is available without charge on the Wikibon Web site. IT professionals are invited to register for free membership in the Wikibon community. This allows them to influence the direction of Wikibon research, comment on published research and post their own questions and original research for the community.

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