Effective Big Data Strategy

By CIOReview | Thursday, June 8, 2017
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There has been a significant increase in the use of big data analytics and the adoption is expected to grow even more. While web companies have already devised and deployed big data strategy, brick and mortar companies are still grappling with it. George Lumpkin, VP, Product Management at Oracle opines that enterprises must be quick in learning how to leverage big data as the amount of data being generated shows no signs of slowing down. In the high-octane technology landscape, implementing big data analytics can be cumbersome. Right strategy coupled with a thorough understanding of business imperative is a must.

Stay Focused

In order to avoid getting swayed by the buzz around big data, organizations should ensure that the end goals are cemented in their big data strategy. Organizations should utilize big data to satiate the real world business needs. Strategic planning for big data projects must be business-driven with IT leadership engaged and informed about the entirety of the process. Specific business problem, use cases, and market opportunities should be the cornerstone of any big data strategy.

Small Start

The biggest mistake that businesses make when it comes to formulating an effective big data strategy is going too far and fast. While organizations might succumb under the constant pressure of analyzing all the data at company's disposal, going fast makes big data projects lose its flare.

Adopting the 'crawl-walk-run' principle will prove fructuous to steer big data innovations in an organization. Short-term, well-defined, and measurable goals in conjunction with constant check on the growth velocity are the key. As the big data maturity of the organization increases, further expansion and long-term objective can be actualized.

Ensuring Better Data Quality

The massive datasets analyzed through the big data tools often raises eyebrows on data quality. Organizations should realize that not all data is perfect, and it is not unusual. Big data does not always warrant perfect data quality. The real imperative is to match the data quality with the application using them. 

Flexibility

The massive proliferation of big data has impacted everyone's workaday life. However, for organizations to reap most benefit out of this disruptive force, they need to harness it for the future. Strategies and methodologies must avoid constraints such as excessive reliance on single technology, among others. The big data driven transformation must be gradual and incremental ensuring value creation and augmenting product quality.

Traversing Beyond In-House Data Repository

Most businesses tend to confine the scope of big data within organization's boundary. However, to augment business values and reap most benefits, businesses have to dissolve the boundaries of in-house data repository. The interconnected world of data is much larger and certainly can be leveraged to derive maximum profits.

Data is increasingly becoming liquid. And while thinking about big data and its use, it is also imperative to ponder upon the data that an organization has access to, rather than the huge dataset that they own.

Adopt Outcome Oriented Approach

It is crucial for organizations to adopt an outcome oriented approach to formulate an effective big data strategy. Thinking beyond pilot is a must to ensure big data strategies are enforced in the right way without creating another data silo. A welcome step for most organizations is to fabricate a powerful and adaptable ecosystem that conjoins data discovery and data platforms ensuring long-term scalability and connecting to mission-critical external data sources at the same time.

Upcoming technologies that cater to big data would also empower organizations with the capability to do analysis that hitherto seemed unfeasible. By adding them to the mix, organizations can leverage best of structured and unstructured datasets. These developments certainty would ease off business impediments, and draw organizations closer to their customers.

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Oracle

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