Legacy Companies Need to Become More Data Driven — Fast
Five tactics for legacy companies. They should consider these five tactics to focus their efforts and avoid wasting time, effort, and resources: prioritize the data that’s most important to their business; link investments in technology to high-value objectives; centralize data infrastructure and decentralize customer management; educate C-suite executives on the value of machine learning and AI; start small and look for measurable wins, and stay realistic about how long transformational change takes.
The ability to deploy data as a competitive business asset is what has distinguished a set of well-established, data-rich companies who have reigned as market leaders over the course of the past several decades. However, business conditions evolve, and today, these companies face a new set of challenges that threaten their hard-won leadership positions. How do these well-established data leaders transform from excellence in traditional data and analytics — of the kind that they have deployed in recent decades — to leadership in a new era of Big Data, AI, and machine learning driven decision-making? What do companies that have excelled at disciplines like database marketing, CRM, one-to-one marketing, and advanced analytics need to do to continue to stay on top?
Data and technology are driving business change. As data volumes and computing power increase, and as new AI, machine learning, and big data capabilities rise, established leaders need to adapt and grow. When JP Morgan CEO Jamie Dimon was recently asked about whether there was much to fear from the potential threat of a “Bank of Amazon,” “Google Bank,” or newer entrants like PayPal, Square, Stripe, and Ant Financial, his response was, “Absolutely, we should be scared,” adding, “I expect to see very, very tough, brutal competition in the next 10 years.” The insurance industry is beginning to making the transition from traditional data and analytics to machine learning, AI, and Big Data driven analysis, but is also dealing with new competition. In contrast to traditional insurance companies, which have been data rich but have customarily relied on actuarial approaches, startup competitors like Lemonade and Traffk are employing machine learning analytics and drawing upon thousands of data elements to provide personalized analysis and drive insurance purchases.
As coauthors of this article and as long tenured industry executives, using data to drive better decision-making and more personalized customer service has long been our focus during the decades long course of our respective business careers — Randy Bean as an advisor to large companies and a chronicler of the industry, and Ash Gupta as former President for Global Credit Risk and Information Management during a 41-year career at American Express. Our view is that to retain their leadership positions, traditionally data-rich companies must adapt their data and analytics processes to incorporate the latest techniques, or risk falling behind those companies that embrace Big Data, AI, and machine learning.
Leaders should consider these five high-value tactics:
One of the greatest assets that any business maintains is its unique set of customer data — customer interactions, transactions, and behavioral history. Whether this is information about your customer’s behavior, habits, or transactions, or other information, it is essential to understand what unique insights your particular data gives you. Knowing that, and how to combine it with external data sources, allows you to build and maintain a uniquely competitive business asset for your organization.
Highly successful companies distinguish between the quality and quantity of data that they maintain. A typical online customer interaction produces more data than is captured in a lifetime of offline customer interactions. One financial services institution captured 50,000 data elements, of which 48,000 were never used.
