Inside American Express' Big Data Journey
CIO Central Guest, Contributor
Photo: Jacob Kepler/Bloomberg
In 1979, the aging British-American stage actor John Houseman appeared in a legendary television ad for the investment firm E.F. Hutton. Gruffly summing up the firm’s investment approach, Houseman hisses: “They make money the old fashioned way… they earn it!” The same can be said when it comes to Big Data these days. You don’t just build a Big Data system, and claim success. You earn it! As more and more leading firms go “all in” on Big Data, companies must demonstrate a path to achieving successful results and business benefits that justify Big Data investments.
There have been few public stories that illustrate the trajectory of Big Data initiatives, reflecting the opportunities and successes as well as the challenges and dead ends that tackling Big Data presents. American Express (AMEX) is one of America’s iconic brands. Ash Gupta, President of Global Credit Risk and Information Management for AMEX, is responsible for deploying data and analytics to enhance customer experience and drive marketing innovation for the firm. For AMEX, customers are card holders and merchants. Mr. Gupta points out that the AMEX Big Data story starts at the top of the firm. Long-time CEO Ken Chenault quickly grasped the transformative impact that Big Data holds for a business like American Express, where data and analytics are core to the firms historic DNA. While AMEX executives recognized the transformative opportunity, they understood that putting Big Data into action meant embarking on an ambitious journey with potential hazards along the way.
Now five years into its Big Data journey, AMEX’s Gupta highlights three challenges the organization has faced along the way. First, adoption of new and immature technologies requires significant organizational adaptation and cultural transformation. Old processes become obsolete. New approaches require fresh skills and approaches. Second, AMEX needed to recruit new talent, with skills in Big Data solutions and approaches. This challenge is complicated by the scarcity of Big Data talent, and compounded by two additional factors: a) the need to always understand “business context”, which comes from experience, and b) the tendency for millennial Big Data talent to continually seek new challenges, creating a retention challenge. Lastly, Mr. Gupta cites the “marketing process journey”, which he characterizes as a process of continuous improvement intended to consistently refashion customer experience in a positive way. For American Express, this has meant employing the same kind of “test and learn” techniques and learning-through-iterative-improvement approaches that the firm uses to refine its customer marketing models.
The AMEX team has learned, and continues to benefit from, valuable lessons and insights gained along the way. The company began its Big Data transformation by focusing on the low-hanging fruit of risk management. The ability to process larger volumes of data has transformed the process of credit risk by allowing the firm to make credit decisions based on a considerably larger window of data. One machine learning system was able to evaluate thousands of data points on each transaction in less than 2 milliseconds, resulting in processing of fraud and credit risk on $1 trillion in charge volume across the AMEX network annually. To build its team, the firm opened a Big Data and Cloud Computing technology center in Silicon Valley to be closer to the new talent and skills, and to create a culture with “greater latitude”. Several of its competitors have done the same. Retention of in-demand talent in areas as ‘red hot’ as data science and Big Data remains a major challenge for any firm. This challenge can be ever greater in traditional industry sectors such as financial services, which must compete with Silicon Valley start-ups for talent.
American Express has taken further steps to build a Big Data culture. The firm initiated an internal push to “democratize” data by putting Big Data tools and techniques into the hands of business decision-makers. The goal is to empower business people to “act locally”, where they are closest to the customer. An example of marketing process improvement is the ability to provide “one-click cross-sell” which increases the options for customers within selected marketing channels. This transformation is unfolding amidst a heavily data-regulated industry, where respect for information privacy is paramount. Against a backdrop of creating new systems and Big Data transformation, AMEX is learning to extinguish systems and processes which have become obsolete. Mr. Gupta characterizes this as a process of “winning hearts and minds” at each stage of the transformation journey.
The journey has not always been seamless however. Mr. Gupta points to false starts and course corrections along the way, noting, “The transformation was larger than we imagined. It was a bigger challenge than we initially expected.” Today, AMEX is in year five of what was conceived to be a ten-year journey. Gupta continues, “We thought it would take us ten years, because we didn’t know what to expect. It has taken us just five years to get the type of results and outcomes we wanted to achieve initially. The benefits have exceeded our expectations, so we have accelerated our investments in Big Data”. The AMEX team now comprises 800 data scientists globally. American Express claims the lowest fraud loss rate on their records, and among the lowest in the industry. The company states that benefits from fraud improvement alone have paid for their investments in Big Data.
What are the lessons for traditional businesses looking to Big Data to transform their businesses? First, as AMEX illustrates, sponsorship from the top remains essential. Long-term journeys require staying power and commitment. Having support from the top is needed to weather periodic storms. Second, firms must be patient and show a willingness and toleration for experimentation and trial-and-error. There is not one single tried and true path. Each firm must chart the course that conforms to their business vision and strategy, and stick to that path. Finally, firms embarking on a Big Data journey will be doing so amidst a highly dynamic and evolving business landscape with increasingly fluid competition. Today’s competitors may not be the competition tomorrow. The ability to move fast, and adapt with speed and agility will be critical to success. “Big Data is a mindset” concludes Gupta, “we see Big Data as a fundamental driver of the future of American Express”. This is the promise and the ambition of Big Data.
Comments..
As said at the last class
of International marketing, find data becomes much easier than on the past.
Nowadays the principles differentiations between the companies are the analysis
and the system of Big Data companies has. Some of them created a data analysis department that
just do analysis and information capitation between the systems and news,
companies as Cappra Data Science, of Ricardo Cappra, is a Brazilian boutique
specialized on doinformation cross-checking methodology to big companies, in
other hands, has some companies that decided to internalize this type of
service, as AMEX.
According to Randy bean “you don’t just build a Big Data
system, and claim success. You earn it! As more and more leading firms go “all
in” on Big Data, companies must demonstrate a path to achieving successful
results and business benefits that justify Big Data investments.”
Taking the American Express as an
example, they considerate big data so important that are creating a culture
between their employees and customers.
The financial marketing is one of the
most important sectors that has been utilizing new systems to prevent and
decided about investments. American Express, as an example, opened a Big Data
and Clud Computing technology center to be closer to the new talent and skills
of the consumer to make the risk and credit decisions based on analysis. The
investor learned to mixed the informations that the bank shows, with the others variables as stock
marketing and news to make them decitions. To sum up, people are mixing
internal and external source, to have and do an analysis of their decision.
