Friday, September 11, 2015

How to Ask the Right Questions about Big Data

 The data is very important, but the interpretation of those data is equal of importance.

Big Data continues to emerge as one of the top priorities in executive’s agenda though data itself is not necessary fun, what’s the big story of Big Data? Often Big Data is a hammer looking for a nail. One needs to know how to ask the right questions before deploying Big Data processing capabilities. But how to ask the right questions about Big Data?

Asking the right questions helps clarify what you are trying to achieve. Asking the right questions using any sized data is a given. It's how we answer the question with Big Data, and that's the big challenge. Big Data is like a big hammer, but how we swing that hammer is what will produce the best results. Large amounts of data become an issue to the extent that its usage confirms an entrenched mindset or methodological bias. Quantity begets authoritative confirmation, but this does not in itself extend the boundaries of knowledge. However, the enormity of data does not necessarily confine those boundaries as well. This is why it’s so important to start with the right questions. If you are clear about what you are trying to achieve, and then, you can think about the questions to which you need answers.

The right initial questions get you started; while the answers to those questions often lead to other good questions as well; and ultimately the epiphanies which make the biggest differences. Analysis of big data, little data, transactional data, etc, combined with a deep and wide knowledge of the "business" being analyzed, and the ability to do something with the results is where we need to go. This requires strong collaboration from all sides of businesses, and leadership with vision. We need knowledge-based problem-solving; Operations Simulation Analysis tracking the causes, consequence of these applications for goal, mission, performance tracking operation improvement, supporting process/ product/technology/market innovation, strategic changes, and management implementation.

The data is very important, but the interpretation of those data is equal of importance. Therefore, the good questions help discover the pattern behind the data, and capture the business insight from it. Every process will try learning about data; it will be about supervised mining than analyst digging through the data. Engineering will change from stateful perfection to adaptive cohesion. On the one hand, the IT-business connection is still preoccupied with making sense out of low-level transactional data from their CRM and other IT application systems. Analytics is typically a large-scale multi-year effort to support faster financial close, reporting, information management and a host of other things. Hadoop-based applications have less involvement with the rank-and-file and drive the latest technologies for digital marketing, customer experience management, a host of sensor-based apps, etc.

Analytics is making an evolutionary journey from capturing hindsight to insight and foresight, it is becoming a significant tool in decision making via answering questions. However, it is still the early days of machine learning and big data engineering, possibilities are immense...It is perhaps more crucial to ask the right questions than just hunting for answers. Be curious to ask questions, particularly the great questions. You will find more surprises in this journey.


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