Data in most banks is taking a life of its own and transforming into a proverbial dragon. The dragon is growing, and growing fast. Most analysts agree that it is growing at an astounding rate of 40 percent to 50 percent annually. Interestingly, the growing data dragon is multi-faceted; both in terms of volume as well as variety.
Customers are also feeling the effect of the “data deluge.” They have access to a plethora of comparative information of product offerings across competitors and through multiple channels. This has put tremendous pressure on banks to react quickly and provide solutions/offerings that are more personalized.
Thus, there is a very clear and obvious need for the banks to tame the data dragon and leverage it towards providing better products and services to its clients. The key to taming the dragon is to understand it. Analytics is a useful tool, which will aid in the process of utilizing data to understand the customers’ actions/behavioral patterns and to convert that data into actionable insight.
Most successful banks have been leveraging analytics to some extent, whether being utilized for campaign response modeling, churn prediction, social CRM and web analytics, for example.
However, given present day technologies, there are many additional areas where data analytics would prove beneficial. The process of effectively taming the data within an organization consists of three major phases, which are, “prepare” (the process of gathering relevant data across the various sources, both internal and external), “process” (applying various analytics techniques on the data) and “present” (process of presenting the results of the analysis to facilitate decision making).
Prepare: There is data available within an organization, which is specific to its customers and to the interactions between the organization and its customers. There is also data available which is external to the organization. This data, for instance, includes stock quotes, customer credit ratings, and customer sentiment data from social media.
Banks first need to identify the sources of data that they would like to analyze. It is important to understand that not all data available within or outside of an organization can be consumed directly. There is a need for organizations to validate that the data being used is correct. In order to validate the data, there are multiple data quality tools and technologies available to the organization. This includes tools from vendors like IBM, Informatica, Trillium and Dataflux.
Other than the conventional Extract, Transform and Load (ETL) tools, that are currently in extensive use, there are tools to help consolidate data across multiple disparate sources in real-time. These data sources can be as diverse as databases, mainframe files, web services, information on the Internet and data on desktops within Microsoft Excel. This type of technology is known as Information-As-A-Service (IAAS) and is supported within ETL offerings by vendors like IBM and Informatica; there are also others like Denodo and Composite who have support for these capabilities.