Big data is the recent buzz word in the market and many organizations are looking at it to understand what kind of benefits it can provide to their business. Logically speaking, the amount of data generated by any business is huge and analyzing that should provide more insights in to data about your business.
It is important to understand and create use cases relevant to your business before you start embarking on the big data journey as every vendor has a big data product and charging hefty license fees for using the software. Initially big data came in to the picture when search engine providers like google, ask.com needed to store vast amount of web page information to enable searching. When social media started to become mainstream websites like Facebook, twitter & you tube started generating terabytes of data and to store that kind of information some different type of databases were needed. That is how big data came in to prominence and now it is an important technology to solve many different types of issues.
The typical use cases for big data
Big data alone can't do anything apart from providing an infrastructure to store huge amount of data but together with analytics solutions, it can make a big impact. The typical usecases i saw are
For Retail segment
How customers use the business website. For example, websites like amazon.com, continuously monitor how the users use their website and if the customer quit the buying process they need to know why & what happened. So they track each and every user, the demographic area of the user, the browsing history for their website etc and this information is analyzed to see the trend. Say for example, more people are getting in to the buying process after seeing a promo on shoes on the first page but during payment, they are not able to pay with Visa card so they are quitting the process. So amazon can find this out by capturing, storing and analyzing this information.
Personalized content
By tracking the customer usage of the website, they can change the content, look and feel of the website to customer's preference. For example, if customer is searching for books mainly in the website, when he visits the site next time, all the books promos will be made visible to the user so that he can be enticed to buy the books.
Cross selling and up selling
Cross selling is selling different type of products to the customer related to the one he already bought. For example, if the customer is searching for TV and bought it, showing all the accessories related to TV (for example, TV stand, DVD's etc) and making the customer to look at them by providing compelling cost reductions.
For Banking and Financial segments
The above use cases mentioned for retail segment also applicable for banking and financial sector as well. However I am not really convinced that we can bring same kind of impact like a retail business because many of the banking products has to be still bought in branch and not in online. However I do see the below use cases
Risk assessment and fraud detection
Banks generate huge volumes of transaction data and for fraud detection it is important to analyse this data. The current model is to use standard data ware house based solution but with increase in data volumes it will become necessary to use different type of technology and big data and analytic for sure is going to be the future technology stack for this kind of analysis.
How to choose big data vendor or solution provider
This field is still emerging and there is no set of standard best practices on choosing a big data vendor. These are the points I think should be considered for selecting a big data solution provider. The important consideration is always about the licensing costs and support. The other factors to consider are
1) Define the important use cases for the business and check ability to support these use cases
2) Maturity of the solution stack
3) How easy is to integrate with other technology investments
4) Case studies and other implementations
5) Future road map and support
6) Ability to support multi channel and integration with social media
It is important to understand and create use cases relevant to your business before you start embarking on the big data journey as every vendor has a big data product and charging hefty license fees for using the software. Initially big data came in to the picture when search engine providers like google, ask.com needed to store vast amount of web page information to enable searching. When social media started to become mainstream websites like Facebook, twitter & you tube started generating terabytes of data and to store that kind of information some different type of databases were needed. That is how big data came in to prominence and now it is an important technology to solve many different types of issues.
The typical use cases for big data
Big data alone can't do anything apart from providing an infrastructure to store huge amount of data but together with analytics solutions, it can make a big impact. The typical usecases i saw are
For Retail segment
How customers use the business website. For example, websites like amazon.com, continuously monitor how the users use their website and if the customer quit the buying process they need to know why & what happened. So they track each and every user, the demographic area of the user, the browsing history for their website etc and this information is analyzed to see the trend. Say for example, more people are getting in to the buying process after seeing a promo on shoes on the first page but during payment, they are not able to pay with Visa card so they are quitting the process. So amazon can find this out by capturing, storing and analyzing this information.
Personalized content
By tracking the customer usage of the website, they can change the content, look and feel of the website to customer's preference. For example, if customer is searching for books mainly in the website, when he visits the site next time, all the books promos will be made visible to the user so that he can be enticed to buy the books.
Cross selling and up selling
Cross selling is selling different type of products to the customer related to the one he already bought. For example, if the customer is searching for TV and bought it, showing all the accessories related to TV (for example, TV stand, DVD's etc) and making the customer to look at them by providing compelling cost reductions.
For Banking and Financial segments
The above use cases mentioned for retail segment also applicable for banking and financial sector as well. However I am not really convinced that we can bring same kind of impact like a retail business because many of the banking products has to be still bought in branch and not in online. However I do see the below use cases
Risk assessment and fraud detection
Banks generate huge volumes of transaction data and for fraud detection it is important to analyse this data. The current model is to use standard data ware house based solution but with increase in data volumes it will become necessary to use different type of technology and big data and analytic for sure is going to be the future technology stack for this kind of analysis.
How to choose big data vendor or solution provider
This field is still emerging and there is no set of standard best practices on choosing a big data vendor. These are the points I think should be considered for selecting a big data solution provider. The important consideration is always about the licensing costs and support. The other factors to consider are
1) Define the important use cases for the business and check ability to support these use cases
2) Maturity of the solution stack
3) How easy is to integrate with other technology investments
4) Case studies and other implementations
5) Future road map and support
6) Ability to support multi channel and integration with social media
