For instance in the banking industry which has been operating in a tough economic environment, under increased regulatory pressure and managing changing customer behaviour, Big Data is both a challenge as well as an opportunity. Optimal use of technology will be the differentiator. Two examples of Big Data use which SAP will be launching in 2013 are:
- Liquidity Risk Management - Greater than 250 million cash flows can be processed in a second. This enables the bank to manage their liquidity risk in real time. Decisions can be made based on facts at any time; there is no longer a need to wait for hours or days for data.
- Fraud Management - Fraud can be detected as it happens in real time so that appropriate actions are taken immediately.
In addition to meeting regulatory requirements, banks are also under pressure to meet the changing expectations of customers. Customers expect their bank to know what to sell to them at the right time. With information available readily via smartphones and tablets, customers compare prices, products, services and know their options. Banks have to continuously look to their Big Data for answers to retain and gain profitable customers.
Deploying Big Data technology and the ability of handling massive amount of data in both structured and unstructured form is not what Big Data is about. The value of Big Data lies in the use of it by people in different levels of the organisation to make the right decision at the right time. Now that we have the technology to handle Big Data, the push in 2013 is finding the value hidden within it.
We see banks doing sophisticated customer segmentation and campaign monitoring to get maximum value out of their marketing dollars. With the technology to process Big Data now accessible, here is an example of how one bank had taken this to the most granular level in real time.
A customer walks into a branch and deposits a large cheque (over two standard deviations larger than any other deposit in the last one year). As the deposit is recorded and processed, real time analytics triggers an alert that this deposit represents an opportunity to make a product offer. An alert is sent to a call centre or a relationship manager to call the customer to make the offer within two hours. The customer accepts the saving bond offer, retains the money in the bank and gets a greater wallet share.
The bank in the example has found a use of Big Data they never considered before. The banks who can find good use cases first will have the advantage of capturing the customers. Finding good use cases cannot be left to an analytics team or to data analysts. More people in the organisation need to have access to Big Data and the tools for analysis:
- Granular risk information in market, credit and liquidity risk
- Granular information on customer, products, transactions
- Easy to use analytical tools, enabling business users to do analysis, simple predictive, hence freeing up the data analysts/scientists to focus on complex modelling
- Highly visual ways of viewing and interpreting data using visualisation and geographical mapping tools
- Information available on the go on mobile devices
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