What is the "$ denting" Big Data use case ?

Many big data engagements are “Data forward as opposed to being use case backward”. It’s very important to fully understand the $ impact of the use case being instantiated and the business value of the new data pools which are being streamed for analysis. For example how much increase in revenue are we expecting when creating a recommender engine using Hadoop cluster to increase the breadth of purchase for online customers? If so a value tracker can track the incremental revenue attributed to the recommendations converted into a sale from the big data solution? In the digital big data use case catalogue tracking word of mouth analysis using social graphs is important because message virality is directly co-related to purchase behavior ... So to summarize ask yourself the most important question ' Is the use case I am instantiating on my hadoop cluster truly $ denting ?" or is it a nice to have ?

How to monetize from search data in Online travel ?

Every time you go to a travel agent to book a ticket on a flight, there are 2 broad kinds of transactions which are generated.
- Search request and response transactions 
- Booking transactions

While most travel organizations have mined their booking transactions data, not many insights have been juiced out of the search patterns for air booking transactions.
For example ,  If you are a price sensitive tourist looking for the cheapest tickets between Boston and Madrid in Nov on Economy class on a Friday evening. Or you could be a value conscious business traveler seeking Economy or Business class tickets at the last minute to ensure that you are on time for a crucial business meeting in New York. 
All the search requests and responses are captured in search log files and flushed out at regular intervals. These search logs which were traditionally seen as occupying a lot of disk space is suddenly viewed as a gold mine of interesting information. For example some interesting 
- Which are the heavily searched destinations from Bangalore on weekends / Holidays where an say Singapore airline has no service?
o An airline could use this information to expand its fleet of services to destinations which it currently does not serve and increase its share of market
Another scenario consists of segmenting customers based on price conscious search versus value conscious search behavior. Business users are typically convenience shoppers (correct timing and service excellence is important) whereas holiday shoppers typically are price conscious. (Getting the lowest price to Colombo is more important than catching the flight at a convenient time). A hadoop cluster consisting of about 6 nodes can be setup to ingest the search data and answer business questions which were previously unanswered

Why change? Does Big data = $? What is the Economics behind Big Data?

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.