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Big Data Analytics – The Big Picture

I am happy to start writing these blogs. Enough ink (rather keystrokes) has been spent writing about Hadoop, NoSQL and various Big Data Technologies. However, most of the uses of Big Data cannot be realized without its applications in the real world. Our customers constantly ask us to provide not only value articulation but value demonstration and value realization.

  1. Is larger volumes of Data really an effective differentiator?
  2. Would the large variety yield significantly better Insights?
  3. Does real-time analytics offer value as promised?

Besides this, understanding the underpinnings of Data Generation and our own ability to understand such data is important. In a world where the data is increasingly captured through a process called “datafication” the brick and mortar is becoming “digitized” providing a significant opportunity of interpretation of various aspects and behavior of the systems around us through Big Data Analytics and Technologies.

A lot of us heard about statistical sampling…. if a sample of the data can reasonably yield a highly probable outcome, is there a need for processing every bit of data? Under what circumstances would big Data processing yield good results? Is data unreasonably effective, as claimed by Google researchers?

The question is not about having a large amount of data but having the right kind of data, and more importantly, it’s about the ability to extract actionable insights for effective decision-making.

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Variety of data generally is the availability of Data in various format of the same subject at hand. Just as a human uses his/her 5 senses of Touch, sight, taste, smell and hearing, machines/sensors also datafy some of these by digitally storing information in terms of Audio, Video, Text, etc.

The Technical confluence of processing each of these types of data has yielded an opportunity to put all the data in a cohesive context. The possibilities of analyses are near infinite, however, the insights that are useful for the business outcome are rare.

I akin finding insights to a needle in a haystack. Essentially, the data will help modeling the real world as closer as it can as possible.

As the speed of information increases, so does the need for speed of analytics, particularly in Industries like Retail where, the Geo-position is important for delivering context-sensitive advertisements etc., Real-time analytics may have several use cases in various verticals.

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