Big Data Analytics and its importance in the BFSI Industry

Big Data Analytics in BFSI industry

Big Data Analytics is not a new subject but it is gravitating attention from businesses across various industries.

So, what is Big Data? The term is used to describe large data sets that cannot be stored, managed and analysed using traditional databases. Reports suggest that big data has improved the performance of businesses by an average of 26%.

Not going deep into the underlying technologies of big data, this blog talks about why, what and how big data technology can team up with BFSI industry to create an ecosystem.

Big Data Technology in BFSI industry

BFSI industry is witnessing explosive data growth. Due to the technology advancement, the tools used by the customers for transactions are growing leading to unstructured variety of data flowing from multiple sources. The need to service such huge amounts of data on a real time basis has created enough space for big data technology to showcase its power.

 

For BFSI, big data technology is no daily bread

Big data technology is not a daily bread for BFSI industry, many think they can well survive without it and some say it is a costly affair. But as we say “Not gaining, is also losing”.

With growing the ocean of data from multiple sources like social media, web, voice, Video etc., being technology savvy has become a need more than an option and BFSI industry is not an exception to it. BFSI industry in India is under immense pressure to sustain their competitiveness.

With too much availability, selection of proper solution has become a challenge

With the boom of big data, availability of it is no concern. But the real challenge today is to decide which data driven solution can solve their specific problem.

Storage and usage are two pillars of big data technology. For certain industries such as the insurance industry, where the size and variety of data is not a concern; predictive analytics has a lot more to do than Hadoop or any HDFS implementation. For industries such as banking and capital market, the size and variety of data is humongous. Hence to be able to fetch hidden knowledge from data, proper storage is necessary.

Big data technology such as Hadoop and Vertica offers solution for storage which no doubt increases efficiency as compared to traditional data bases. Storage of data has been in talks in industry for quiet some years now because of which data was always cursed as “Liability”.

Store so as to use

The basic difference between data warehouse solution for data storage and big data solution, such as Hadoop or Vertica, is that the latter stores data so as to use it and to create insights which can drive business decisions.

The need to store and effectively use data has bridged the gap from data being liability for an organisation to an asset.

Create your problem statement           

Big data technology is surrounded by hundreds of fancy, loosely used words that can easily confuse business folks and waste time in doing non-productive campaigns. Creating a business problem, scoping its bits and parts and then realizing which parts of the problem can be solved by mathematics and which cannot, is the step zero of a successful campaign.

 

Set a practical and measurable target

All the artificial intelligence, business intelligence, predictive analytics etc. constitutes only 50%, the other half is how we use the solution. A screw driver can open a big machine, only when the usage is understood and effectively used. So, invest reasonable time in design of solution as well as effective usage of the same.

Set the ball rolling with MVP (minimum viable product)

Time is money, utilize it wisely by resorting to data based solutions rather than Google search engines.

The first solution might not be the best. Hence, learning’s from the iteration should be incorporated not only into further analysis but also the Business execution strategy. Continuous monitoring and feedback thus become the key elements.

Some examples of solutions offered by big data analytics: Customer churn analysis, cross-sell and up-sell, database consolidation, portfolio risk management, etc. These are just some snapshots from a never ending list.

Big data technology is the “enabler” and business is the “executor” together we create an “ecosystem” which fosters transparency and rich user experience.

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