Flutura has been constantly asked a series of questions by hardnosed practical customers. These questions are from real world decision makers considering Big Data solutions in London, Dallas, Chicago, Bangalore, Dubai, Singapore and Riyadh. The breadth of industries spans large auto giants to telecom companies to health care service providers. All of them agree on the need to extract intelligence from the torrent of data from their ‘process exhausts’. All of them agree that the faster they do it the greater their competitive edge. But they have
questions regarding the approach and solution.
1.· Why not continue with a traditional BI infrastructure for dicing and slicing?
2.· What’s wrong with my ‘as is’ current customer scoring or risk scoring data mining process in SAS?
3.· Why not live with my current SIEM solution for log management in telecom?
4.· Is there really a need to look at new age Big Data solutions?
5.· Isn’t there more hype than substance?
These are genuine and valid questions. Perfectly valid question because since the industry has a history of dramatizing the need for new technologies, buzz words and solutions.
So how do we stay grounded and examine if there is TRULY a need to adopt big data solutions?
Here is a simple checklist of questions the answer to which can serve as a guiding compass to make the decision to implement a Big Data Solution or continue with existing BI solutions.
•What are the new business use cases enabled by processing big data? For example can U customize the next best product to buy recommendation by having a deeper understanding of the micro clicks a user does on digital channel? It’s difficult to implement this on traditional low latency data solutions
• What is the impact of the new business use cases on reducing cost or enhancing revenue ? For example having a real time sense and respond infrastructure to respond to search behaviour within the session information increases repeat visit and purchase
• What is the reduction in annual statistical license cost fee if I migrate the analytical scoring process from my current solution to an open source statistical package like R ?
•What is the reduction in storage cost if I migrate data from my current SAN based solution to a Hadoop cluster consisting of a necklace of commodity hardware?
We should put not put the technical architecture ahead of business use cases and these questions can show the way.
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