A Bank On the Edge of a Deep River

South Africa’s Nedbank highlights the challenges for companies seeking to leverage vast data assets.

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Becoming a Data-Driven Organization

Organizations have begun to capture, create, and use data in ways that are changing how we work and live. The following case studies tell the stories of organizations tackling the opportunities and challenges associated with becoming data-driven.
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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.

Incentive structures are critical in order to align employee actions with organizational goals. In banking, the current scandal at Wells Fargo & Company shows how well incentives work; employee benefits tied to account creation resulted in many new accounts. But vast quantities of token accounts was not likely the underlying organizational goal. Nedbank has a 2020 goal of five million “main bank” clients. Analytics can help make sure that the accounts created in pursuit of this goal are actually contributing value to the bank, not just new account numbers.

Nedbank’s current infrastructure hasn’t yet developed to the point where it can open access to all employees. However, technology advances relentlessly, and I expect it won’t be long before they do. Rather than being a nirvana, things might get rough. Widespread availability of data puts pressure on employees to use that data well. But these skills are currently not possessed by many of its employees. What is Nedbank doing to develop its employees’ analytics skills? Making data available is just one step. While the employees may be empowered, it takes experience to know what do with that power. (By analogy, there is a reason that hospitals don’t give everyone a scalpel.) Just being in the water doesn’t mean people automatically know how to swim. The gap between the organization’s ability to produce analytical results and its ability to consume those results may grow as people struggle with the pace of increasing sophistication.

Relatedly, privacy will be difficult to layer on after the fact as well. The potential for trouble is ever present; indeed, “[t]he very nature of the banking business means Nedbank has access to vast amounts of transactional and personal data about its customers.” Currently, only a handful of managers have access to this detail. But the tools Nedbank is building will increase access across the organization to detailed data about its customers. Technology will no longer be a limit. Employees will have newfound ability to do things that they should not.

As in many other organizations, the transition to a data-oriented approach can call fundamental aspects of the business into question. For GE, data is radically changing how the company creates value. Nedbank may soon face a similar transition with its Market Edge initiative. If it is able to scale this product — which is still far from certain — the organization will face some existential questions. In order to fuel Market Edge’s data needs, should it give away card and payment services — or even pay customers to allow Nedbank to process payment transactions for them? Reduction in silos should help organizations develop holistic views, but within these holistic views, it may no longer be clear which parts of a business should produce revenue and which parts should be in supporting roles.

Nedbank’s focus requires that its “executives understand customers’ behavior.” Data and analytics enables that. Building on, as the bank becomes more data-oriented, it should be able to turn its developing prowess on itself. As the bank dives deeper into analytics, data can help Nedbank understand more than its customers — it can better understand its own organization, employees, suppliers, and more.


Becoming a Data-Driven Organization

Organizations have begun to capture, create, and use data in ways that are changing how we work and live. The following case studies tell the stories of organizations tackling the opportunities and challenges associated with becoming data-driven.
More in this series

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Comment (1)
Prasanna Kumar
I liked the points - ….“executives understand customers’ behavior.” Data and analytics enables that. In my view, such transition calls for cultural change and time. What is level of maturity the Data and Analytics enabled banking systems currently have? Securing the processed data, in my opinion, may need well advanced technologies.