Sunday MBA provides ideas on running better businesses and succeeding in the modern workplace, this week from MIT Sloan Management Review.
For large companies saddled with complex legacy systems, finding the opportunity for disruption can be daunting. Unlike new economy firms, which have had the benefit of being able to build their businesses and infrastructure from scratch in a “green-field” environment, most large corporations are saddled with disparate and fragmented operational and analytical environments and processes that limit the ability to operate with agility, flexibility, and insight across customers and lines of business.
While institutional reputation, customer reach, and operational scale provide many advantages to large corporations, these firms can sometimes be challenged when it comes to innovation and responsiveness. But Big Data approaches developed by new economy firms are being adopted by mainstream corporations to introduce greater corporate agility and speed.
A 2014 survey of 125 senior corporate executives, representing 59 Fortune 1000 companies, showed that more than two-thirds of executives reported that their organization had a Big Data initiative in production. These same executives reported that investments in Big Data are projected to grow dramatically in the coming years.
One such firm benefiting from new Big Data approaches is the financial services giant American Express, which is harnessing the power of its data to innovate and to streamline complex operational processes. American Express has taken steps to optimize and streamline its operational and data processes end-to-end by migrating many traditional processes from legacy mainframes to Big Data processing environments, resulting in dramatic improvements in speed and performance, significant reductions in cost, and notable increases in responsiveness to customer needs.
A few examples:
American Express has been able to dramatically reduce the time it takes to process the thousands of variables and billions of calculations needed to match customer card offers to card member interests. It previously took years of processing time just to sift through these massive volumes of data, but now it can be done in hours.
American Express has made similar strides in connecting merchants to card members on personalized offers, taking a three-day process and reducing it to 20 minutes on their Big Data platform.
The company uses Big Data analytics to detect and prevent fraud in milliseconds and with greater precision, using machine-learning models that leverage billions of historical transactions across millions of card members, resulting in instant fraud alerts for customer protection.
Leading companies can learn from the example of American Express and other mainstream corporations that are adopting Big Data solutions and approaches with success. Here are a few suggestions:
Assess opportunities to migrate operational business processes from mainframe environments to faster and cheaper Big Data approaches.
Create a Big Data Center of Excellence or Analytical Sandbox as a testing ground to identify suitable applications for Big Data.
Adopt a “data first” approach that focuses on rapid identification of data insights, rather than investing in large data warehouse initiatives.
Develop a “culture of data” staffed by a next generation of analytics professionals, who are conversant in new Big Data approaches and analytics techniques.
Mainstream companies will drive the future of Big Data investment. But they must demonstrate a willingness and flexibility to tackle their complex legacy systems.
For those firms that make the commitment, as evidenced by the example of American Express, the payback will probably be high. Their ability to compete for customer success in the coming years might depend on it.This article is adapted from “Overcoming Legacy Processes to Achieve Big Data Success” by Randy Bean, chief executive of consultancy NewVantage Partners. Copyright 2015 MIT Sloan Management Review. All rights reserved.