Three Big “WHEN”s to Explore Big Data

Friday, July 13, 2012 | comments

There are more businesses are at the journey to debunk Big Data mysteries, as we are living in such a “petabyte” age, nobody can claim the Victory yet by conquering 3”V”s  + 1C about Big Data: Volume, Velocity, Variety and complexity. By exploring "5W +1H" Big Data scenario: Why, What, Where, Who, How, now, we are pursuing the last “W”: big “WHEN”s at Big Data adventure:

  1. When should you start take Big Data Journey?

    • Sooner than Later: big data like airline seat or perish food, it will expire soon, that said,  being able to process and analyze your data as soon as possible is critical to gain competitive business advantage;

    • Shorten “When” from starting point to “When” to get result: Reducing the time between when data is collected and when insight perceived from that data,  which you can access the resources and analyze the large scale data promptly and easily;

    • When - timing of data is at the heart of Big Data opportunity: it makes Big Data analysis difficult, besides the other characteristics of Big Data such as 3”V” + complexity, on the other side, the “timely” data provides unprecedented opportunities for business to win at era of uncertainty.

  1. Big “WHEN” need focus on the Future of Business

Traditional BI usually like a “rear-view mirror” to uncover the hindsight on the business, and tell you where you have been; Big Data deployment puts emphasis on foresight via predictive analysis, rather than historic ones, that said, the big WHEN in Big Data need focus on future of business:

  • The future trends of business: It is the data mining practices concerned with the prediction of future, the probabilities and trends, predictive analytics is to be as much an art as a science, it makes Big Data analytics both risky and rewarding;

  • When is crucial in Data-Driven decision making: Big data predictive analysis becomes central to business strategy and decision making, it's all about timing, the focal point is also about how to execute complex tasks and measure ROI in Big Data initiatives.

  • When means historical, real-time or “as soon as possible” analysis: Different data may go to different destination, the art of choices may depend on intent of usage and the quality of data.    

  1. When: enough is enough in the Iterative Big Data Management life Cycles?

  • When –means the full data life cycle management, including  making decisions on weather data is stored for short-term batch processing or long-term retention, in the petabyte age, where the amount of information available for analysis is accelerating exponentially, the Big Data information lifecycle management process should ensure a laser focus on data/information that matters: Spend more time on understanding the problem, less time on designing the solution. To quote Einstein: “if I have an hour to solve a problem, I will spend 50 minutes to understand it, and 10 minutes to solve it”.

  • When –means shorter window of business opportunities, organizations need get quicker understanding customers by analyzing customer data and responding to customers request;  faster time to market by reducing data latency, unleashing Big Data potential for big revenue recognition opportunities or putting business performance improvement effort.

  • When –also means the agile methodology to deploy Big Data , the emerging Hadoop open source project becomes popular solutions to manage data information life cycle, and address business and technology trends that disrupt traditional data process scenario, also embed information back into business life cycle more seamlessly via iterative communication and collaboration, the mantra to deploy Big Data is  “ Do it faster, better and cheaper”.

“WHEN” matters because Big Data transforms analytics from hindsight-oriented business intelligence into foresight driven diagnostic, predictive and prescriptive analytics, organizations now are also moving from inside-out orientation-where they analyze and report data from within the business boundary via historical perspective into the outside-in one-where data out of traditional business boundary flow in and bring the futuristic perception of business. It’s the new theme of contemporary business at the “always on” and hyper-connected new normal. 
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