Overcoming Legacy Processes to Achieve Big Data Success

For large companies saddled with complex legacy environments that were developed over many decades, finding the opportunity for disruption can be a daunting challenge.

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, characterized by business and data silos 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 larger firms can sometimes be challenged when it comes to innovation and responsiveness. Yet it’s still these large corporations that continue to be the bulwark of the economy, accounting for the bulk of business and consumer transactional activity. Now, Big Data approaches that were developed by the new economy firms, which enabled the flexibility and rapid growth of these firms, are being adopted by mainstream corporations to overcome legacy challenges and introduce greater corporate agility and speed.

A 2014 survey conducted by NewVantage Partners 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 — with 75% of executives reporting investments greater than $10 million, and 28% reporting investments exceeding $50 million by 2017.

For these corporate giants, operational process optimization can have a huge business impact.

One such firm that’s benefiting from new Big Data approaches is the financial services giant American Express (AMEX), which is harnessing the power of its data to innovate and to streamline complex operational processes. AMEX has opened its own new technology hub in Palo Alto, led by former Google and PayPal executive Nik Sathe. The new tech center “will focus on innovations in Big Data, cloud computing, and mobile infrastructure,” according to AMEX CIO Marc Gordon.

AMEX has taken steps to optimize and streamline its operational and data processes end-to-end by migrating many traditional processes from legacy mainframe environments 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.

Ash Gupta, chief risk officer and president for Risk and Information Management for AMEX, comments, “Fact-based analytics have always been in our company’s DNA, yet I have never seen such an analytical leap as the one we have made with Big Data.” He cites several areas where AMEX is focusing its Big Data efforts, notably in addressing service excellence, generating billings and receivables growth, and risk management. A few examples:

• Using Big Data platforms, AMEX 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.
• AMEX has made similar strides in connecting merchants to card members on personalized offers, taking a 3-day process and reducing it to 20 minutes on their Big Data platform.
• American Express 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 AMEX 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 professional, whom 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 environments and data infrastructure. For those firms that make the commitment, as evidenced by the example of American Express, the payback is likely to be high. Their ability to compete for customer success in the coming years may depend on it.