UPDATED 15:43 EDT / FEBRUARY 26 2015

Big Data winners using inline analytics to derive large ROIs from their investment

Inline_AnalyticsA recent poll of Wikibon community members revealed that many are implementing initial Big Data systems. The vast majority of these, however, focus on an IT operational expense reduction in which large amounts of older company data is archived out of more expensive database engines into Hadoop. The unstructured data can be added to support deep-dive analytics, often accessible only to a relatively few people in the organization. These applications provide experience for IT, but on average have only yielded an ROI of 55 cents on each dollar invested.

However, writes Wikibon CTO David Floyer, a small number of respondents are realizing much higher returns. These companies have several things in common. They target business issues rather than internal IT organizational cost savings. They use inline analytics integrated into operational systems and designed to improve organizational processes. These algorithms are often, but not always, supported by data tables developed using deep dive analysis of Big Data in Hadoop. They often use databases that run either on DRAM or flash to provide real-time responses. And they eliminate traditional extract, transform and load (ETL) processes, which are too slow to support real-time analysis of large volumes of streaming data.

These new systems have radically changed the way Big Data is monetized and managed. Rather than providing insights to a few individuals, they are accessible to a large population of users who use the analytics to guide business decisions at all levels of the organization. They allow users to change the way operational systems work, and because they are real-time, they are forward-looking and predictive, in contrast to traditional backward-looking analysis of what the organization did in the last month, quarter or year. The extended use of data-in-memory and flash technologies to avoid magnetic media bottlenecks is an essential component of these systems. A small team of operational experts and data scientists use deep-data analytics to improve the algorithms.

Floyer takes a more detailed look at four of the leading in-memory database engines: Aerospike, Inc., IBM BLU, Oracle 12c, and SAP HANA. He says that based on interviews with customers and analysis of alternatives, IBM DB2 with BLU Acceleration appear to be the most mature, relatively low-cost and high-performing. But each of these systems has its own strengths and weaknesses, and these are only four of a larger group of in-memory database engines. Organizations should pick the technology that best fits their needs, skill sets and underlying technologies. Floyer also includes two case histories that illustrate how inline analytics has solved major operational business issues for two companies, eXelate, Inc. and an unnamed bottling company.

Graphic courtesy Wikibon.org

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