As many financial service organizations are discovering, there’s a new currency in town and it’s unlike anything we've ever dealt with before. The more of it you have, the more each piece is worth. And many banks and other financial institutions are sitting on huge stocks of it, and yet they have still failed to realize any returns.
This new currency is data. Today I’m continuing my exploration of the Third Industrial Revolution by taking a look at analytics. Because it’s not just about how much data you have, but whether you can extract real value from it.
Financial services is a data-driven enterprise. Banks manipulate and process data like a manufacturing company processes raw materials. It’s no surprise that almost every financial services organization I have spoken to in the last year has identified big data and analytics as their top priorities. While it's clear that understanding this data is critical, many still struggle with what to do and how to do it.
Learning to Manage Volumes of Data
Intel recently sponsored a report on Big Data Cases in Banking Securities, created with the STAC Benchmark Council, which looked at the big data/analytics use cases common in both investment and retail banking today. Among other things, the report revealed a mix of approaches with some organizations using big data to do old things faster or better, and others using it to do completely new things. Of the famous three Vs, volume was found to be the most challenging issue among participating financial organizations.
To avoid being overwhelmed, a good first step is to narrow the focus to the top two or three use cases that will provide the most value or impact on the business. In my view, these are the three pillars of big data/analytics workloads in financial services which represent the greatest opportunities for investment:
1. Risk management and portfolio optimization: A consolidated view of data across the enterprise that is required by regulatory requirements. This touches areas like enterprise credit risk reporting, securities fraud early warning, credit card fraud detection, and anti-money laundering.
2. Customer engagement optimization: Achieving a 360-degree view of the customer (both consumer and business) with personalized and contextual information to enable targeted cross-selling and up-selling.
3. Increasing operational efficiency: Using big data to improve internal processes and drive incremental innovation in areas such as modeling branch behavior or IT operations analysis.
When bringing big data analytics to one of these areas, there’s a lot to consider. How much will it, and should it, cost? How can companies hire the right data scientists? And how can financial services companies cope with the volume, velocity, and variety of data, and develop usage models that will help drive insight from it?
Empowering Customers to Leverage Analytics
Our goal when approaching these areas with our financial services clients is to help create an open, interoperable analytics infrastructure and data platform that will empower them to develop the solutions, approaches and processes that will work for them and their customers. In addition to core platform technology like CPUs, solid-state drives, networking, fabric, and security, we also encourage them to think about easier implementations and management (i.e. using analytic data management software such as Cloudera, which is based on the open-source software Hadoop). Using standards-based architecture helps with the recruitment challenge, and also helps to reduce up-front and ongoing technology costs.
As a data-rich financial organization, you need to think of big data, analytics, and enabling technologies as your new toolkit. They’re just as important as your online banking platform, your CRM software or your sales database. In fact, it's the piece that will bring all these disparate elements together and help you extract maximum value from your data currency.
Let's continue the conversation on Twitter: @blalockm
Global Sales Director
Financial Services Industry, Intel
This is the third installment of the Tech & Finance blog series.
Click here to view: blog #4
To view more posts within the series click here: Tech & Finance Series