3 keys to the Big Data fast-track

3 keys to the Big Data fast-track

3 keys to the Big Data fast-track

One measure of tech ROI is user adoption: software that is not incorporated into workplace processes is of questionable value. Recognizing this principle, creators of data integrity, integration and intelligence solutions Information Builders (IB) have built functionality that extends information – in the right formats – to different user types across the business, democratizing data to enable its incorporation into operational work flows. But beyond technology adaptation, Information Builders also works hard to develop awareness of the full potential of its product portfolio in sessions aimed at helping users understand the data value proposition – in new and emerging areas in particular. A good example of this is the “Accelerating Big Data” workshop presented recently by IB’s Canadian team, which focused on three implementation perspectives: the strategic requirements for set up, the technical aspects of moving from project concept to preparing Big Data for usage, and the actual visualization and use of the information by the business/technical analyst.

Promising “we’ll show you how to get it in, and how to get it out,” pre-sales solution specialist in the Information Builders Enterprise Software group Ganesh Iyer kicked off the discussion with a high level definition of the Big Data “4 Vs”: variety, velocity, veracity and volume, which each present different challenges for operations staff. Coming from disparate systems, from sensors and interactions in structured and unstructured formats, in real time or with latency, complete or in “snapshots,” and in terabytes or petabytes, Big Data introduces a number of issues that Iyer believes are largely associated with volume. But citing a 12:1 ratio of exploratory to production projects, he argued that the biggest problem for most organizations is finding value in Big Data. And describing a May 2015 Gartner US survey, which found that 57 percent of respondents cite the Hadoop skills gap as a primary obstacle to Big Data deployments, he noted that businesses continue to invest in Hadoop “though they’re not sure why, and they don’t know how to use it.”

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To support businesses as they begin the Big Data journey, Iyer offered “5 steps” to deployment as follows:

Align the needs of business and IT managers. Practical drivers for Hadoop implementation are likely to vary. While IT is typically motivated by need for a database upgrade, historical data archiving, large enterprise data warehouse initiatives (to migrate from legacy Microsoft or Oracle data storage), and cost reduction in maintenance and operations (software license and maintenance fees will drop dramatically), LOB managers are interested in another set of concerns. Deployment success will depend on mapping IT aspirations to the business user’s interest in analyzing unstructured data in an automated fashion, in tapping social media for new insights, or its need for continuous data and business process optimization to achieve additional savings.

Understand maturity of the organization. Iyer divided “maturity” into two components: infrastructure maturity, where the organization will have good understanding of its data and database needs, and skills maturity. “Do I have the ability to access and extract data from the systems?” is a good test question in infrastructure, to which Iyer added “do the business applications need more than traditional infrastructure,” and “do I want to have a data warehouse?” Answers to these questions will help frame requirements for the Big Data project.  For the second component, Iyer identified key questions as: are there business analysts on staff today, are they accessing the data, and do they know what to do it? “If your business analysts are not in the habit of mining data for insights, Hadoop’s not going to change anything,” Iyer argued.

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Estimate budget.Cost factors will include “startup costs,” including use case development, hosted vs.

 



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