Becoming a Data-Driven Organization
This article originally appeared in the case study “A Data-Driven Approach to Customer Relationships.”
Even organizations outside banking can likely see some of themselves in the Nedbank case study:
• Silos where information doesn’t flow well between parts of ostensibly the same organization.
• Different sales channels eager to claim revenue but reticent to own expenses, leaving the sum different than the parts (data dictator).
• Encroaching competition due to changing market conditions or strategies that haven’t panned out.
• Staff that are reluctant to embrace analytical results or have not yet mastered the new skills required.
Your organization may not be in South Africa or in banking or even at the same analytical maturity, but some of these issues probably feel uncomfortably familiar.
In this case study, Nedbank offers candid insight into its efforts to take advantage of its vast data resources. While everything hasn’t been smooth, the bank is making progress. It is becoming data-focused, and this illustrates several tensions organizations face.
Nedbank has had a long history of success with its current matrix structure, in which each unit has its own P&L. But increasing demands for holistic views, particularly of customers, strain this independence. Myopic focus on a single product, for example, doesn’t reflect the current or future value a customer might bring to the organization. The availability of data, shared across units, provides better indicators of the overall potential value of a seemingly unprofitable deposit customer. This potential value may be current, predicted based on other customers, or even based on the diversity the customer adds to the overall portfolio. But this cross-unit view represents a significant change for many.
Conceptually, human beings like the idea of classifying people into groups in order to vary offerings by type. Nedbank, for example, “transitions clients from one segment to another as their wealth or business circumstances change.” Increased data about customer behavior should allow Nedbank to take a far more granular approach in the future than its current segments allow — ideally, individually tailored interactions. It won’t be long before everyone expects this level of personalization, and cognitive technologies that use artificial intelligence will likely be necessary to achieve personalization at that scale. This will require the data infrastructure Nedbank is building now.