Putting Your Money on Big Data and Analytics for Guaranteed Wins
- Rahul Singh, President, Financial Services at HCL Technologies
- 02.12.2016 09:00 am big data
Banks and financial services have always had a voracious appetite for data. They have a finger on the pulse of company balance sheets, interest rates, money supply, exchange rates, inflation rates, stock market movements, bond yields, customer transactions…you name it, they are tracking it. But most of their data management and analytical systems have historically been geared for daily, weekly, monthly and yearly trend analysis and forecasting. What they need today is real time customer visibility and response based on a continuous and growing stream of rich data. But, real time data at current volumes, variety and velocity is so overwhelming that even financial institutions weaned on data can be intimidated by it.
Today’s anytime, anywhere availability of financial services—over the web, on mobile devices, at kiosks and ATMs—means that a single misstep or loss of time in response can translate into missed business. Take the case of a US-based client who found that although traffic to their site was growing, the sale of online auto insurance products was on the decline. Besides, thanks to a poorly-performing web property, customers were falling back on contact centre executives to assist with questions and purchase, resulting in an increase in the cost per customer. It was clear that their existing data and analytical tools were inadequate to identify the problem and arrest the decline in online sales.
When we looked at the problem we wondered, “What is the reason for the success of the call centre that digital lacks? What can digital learn from the contact centre?” We installed a Big Data and insights solution for the client, following customer journeys across touch points, acquiring quick-stream data to uncover the problem. We analysed customer behaviour at every point, what they did, what they needed, where they fell off, what intervention the call centre may have produced at this point, how to serve the same solution online in real-time, and so on.
Ultimately we solved the problem by analysing the data. Online sales increased by 50% (and spiralling call centre costs were controlled).
At the heart of the solution was a Unified Information Architecture enabled by Next Gen capabilities such as Big Data Lakes that ensure a vast amount of data can be acquired and quickly analysed.
Big Data Lakes differ from a traditional Enterprise Data Warehouse (EDW) in that they store raw data with no definition or schema. Each user creates the schema for data that they need, when they need it. In other words, while a traditional EDW will extract, transform and load the data, the Big Data Lake will first load the data and then transform it to deliver accurate, personalised, real-time customer insights.
For our client, this has become a foundational project, a starting point for their Big Data journey that will inevitably make it simpler to deploy cognitive technologies for higher levels of insights at a future point in time. Not only are Big Data platforms cost effective in comparison to the relational systems, they can scale to multi petabytes which is a requirement when you start introducing web clicks and other unstructured data as an added benefits.
Big Data and Analytics are being used to unlock better views of the customer in the US. The priorities in Europe are a little different. Here, we are using our experience in Big Data and Analytics to bring banks up to speed on what is possible, using our US experience. However, in Europe the hot target use case is regulatory. Compliance is a priority in Europe, followed by crime analytics and then marketing.
In the US, financial institutions are asking questions such as, “How can I make my P&C customer buy my wealth management services?” In Europe, they are asking, “How can injecting petabytes of data improve risk management and regulatory compliance?” The answer to both lies is Big Data Lakes and Analytics.
Simply put, organisations need to focus on:
- Agility to ingest, process and analyse any data, any time, at any level of complexity
- Flexibility to correlate, combine and consolidate all data for rapid delivery
- Ability to easily and quickly publish data for end-user reporting and analytics applications
- Expertise to gain the insights needed to grow market share, improve operations, and increase customer satisfaction
Regardless of use case (customer visibility, improved sales/ personalised product creation, reduced cost of customer service, regulatory compliance, crime analytics), getting ahead in today’s dynamic business environment means being able to innovate and operate at the speed of digital commerce. In turn that means becoming a data and analytics-driven enterprise delivering on-demand Agile Analytics services to business users and customers.
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