Becoming a Digital and Data Enterprise

Top line revenue growth in traditional financial services has been challenging. True, a series of modest interest rate increases, coupled with renewed consumer lending, has propelled some US retail banks to record profits in recent quarters, but the prospect of an economic slowdown, and an accompanying pause or reversal in Fed policy could weigh heavily on future profitability.

In Europe and Asia, pervasive low, or negative interest rates have not afforded banks the same growth as in the US. Even for some US-based institutions with a large overseas presence, the strength of the dollar has weighed on returns, as the relative value of dollar based expenses is greater than non-dollar based income.

At some banks, the cyclical exposure of rate-sensitive businesses is offset by less balance-sheet intensive, fee-generating businesses, like asset and wealth management, asset servicing and investment banking. However, in an environment of continuing fee and margin compression, those businesses too must address their own systemic issues.

Against this backdrop, the most significant threats to many incumbent financial institutions are the emergence of new non-bank “fintech” entrants, as well as quickly evolving consumer expectations and behaviors. Perhaps, the greatest fintech progress to date has been in the area of payments, but the pace, and sophistication in other domains – robos in investment management; retail and commercial lending platforms; branchless digital banks; new factoring services; online insurance channels; blockchain and distributed ledger technology; and electronic markets, to name a few – is tangible and should not be casually dismissed.

Digital and the Gig Economy

There are other factors that must be considered, too. Younger Millennials and Gen Z cohorts, for example, are sometimes referred to as the “renter” generations. They have largely grown up with the Internet, social media and smart phones. With Internet connectivity and an abundance of apps in their pockets, they are quick to share, coalesce, research and transact in a digital medium. They have also lived through the housing and banking crises, and form the backbone of the “gig” economy, valuing independence and making them less apt to be tied down. They are accustomed to the immediacy, flexibility and experience of services, like Amazon Prime, Uber, Airbnb and streaming media platforms.

What this means for the future of financial services is profound. A cultural shift from owners to renters, for example, implies less mortgage and auto loan origination, though perhaps an increase in payment processing and credit card transactions, both areas in which fintechs have thrived. As a digital-native demographic that values immediacy and self-service, the ready access to data and mobile tools is not optional: it is expected. Further, an expansion of the “gig” economy suggests growth in the ranks of the self-employed…who will still need ways in which to manage their finances, acquire insurance, build wealth and file taxes, albeit in a more streamlined, lower cost and simplified manner, consistent with the requirements of a sole-proprietor or other small business.

Even in other industries, the rise of online retail platforms, like Amazon, has disrupted brick and mortar retailers, many of whom were too late to recognize, or act upon the digital transformation underway. Ride-sharing apps, like Uber and Lyft, have dislocated the taxi and livery industry: in New York, taxi medallions once worth a million dollars as recently as 2014 are now worth roughly 15% of that. In just over 10 years, Airbnb now accounts for roughly 20% of the entire US consumer lodging market, recently surpassing Hilton Worldwide, one of the world’s largest hotel chains…all without owning any rooms. In manufacturing, 3D printing is yielding just-in-time production in a wide range of applications, including consumer goods, medical, automotive, aerospace and building construction. Clearly, we are increasingly becoming a digital and data-driven economy, and financial services is no different.

Transformational Change is a Ground Game

Incumbents, often weighed down by decades of legacy baggage and organizational rigidity, may ultimately benefit by refactoring, cannibalizing or simply abandoning stale business models. Leaders should seek to create an environment that promotes, and rewards agility and innovation. Tactical cost-cutting exercises that have become commonplace in the industry are usually structured to meet a target, and often bear little fruit beyond a few quarters of relief. Even worse, such actions typically demoralize staff and prove to be unsustainable in the long run. The pursuit of operational efficiencies should, instead, be a largely strategic exercise.

Discretionary investments in technology, too, should pursue a meaningful, forward-looking business transformation that reflects “where the puck is moving”, rather than “where the puck has been”. After all, the payback on introducing a better mouse-trap built for a legacy business model may be difficult to realize if the model, itself, is ultimately diminished.

By and large, transformational change is a ground game. Leaders should seek to articulate a vision without being overly prescriptive. They must demonstrate passion, engagement and empowerment: inspirational and collaborative leaders will likely fare best. If the rank and file don’t own the transformation, or managers instead prioritize status quo, it simply will not happen. Frankly, with the pace of change and emerging threats, these steps are no less than existential and should be treated as such by leadership.

Challenges notwithstanding, there are indeed fantastic opportunities awaiting those that are are both inclined, and prepared to pursue them. Large incumbent financial institutions, for example, are a virtual treasure-trove of data. In a digital and data-driven economy, that is perhaps the most valuable asset these firms have, and much of it remains untapped.

Consider, for example, that 4 of the top 5, and 7 of the top 10 global public companies, by market cap, are technology companies. Berkshire Hathaway, at #5, is the only financial firm in the top 10, and JPMorgan Chase falls just outside, at #11. Fundamentally, these technology companies derive their value from the networks, or ecosystems they’ve created; from the deep data insights they enable; and from the user experience, and loyalty they engender. In the case of Microsoft, Amazon and Alphabet/Google, representing 3 of the top 5, the growth of cloud computing has also been at the core of the digital, and data revolution.

Financial services, of course, is a highly regulated industry and the anonymity and protection of Personally Identifiable Information (PII) and other Non-Public Information (NPI) is a critical responsibility, and one that must be heavily guarded against any breach. Of course, many of the data policies and practices at financial institutions, today, already seek to obfuscate, or limit access to such information. The protection of such data is therefore not new territory, per se, though other governance and technical challenges remain if these firms are to truly transform into digital, and data enterprises.

Becoming a Digital and Data Enterprise

First, financial institutions will want to take stock of their data inventory, and how they want to utilize this data, both internally and externally. In many cases, this is easier said than done. In large, federated organizations, data often accumulates in less structured, more organic ways, and the state of data may be questionable. Integration is at the heart of any such data transformation, yet the ability to link data elements across system boundaries and without common keys, as may be the case, is a difficult task.

As such, cleansing data can itself quickly become the central mission, rather than actually using the data to drive operational efficiencies or derive valuable insights. Here, there is new opportunity to compliment, or replace some of the exhaustive, and largely manual data cleansing and maintenance tasks with AI and machine learning. At scale, such an approach can be tuned to automate data matching and learn from its successes and mistakes. Moreover, this can be applied as part of ongoing data maintenance, rather than as a one-time seeding of data.

Also, the proclivity to consolidate data in a separate warehouse, while perhaps creating a simpler interface for some SQL-based reporting requirements, introduces its own set of problems. Warehouses are highly structured and, as an inherently redundant data repository, can become stale and out of sync with source repositories. They may additionally suffer through change-management exercises, particularly as business processes change, or when multiple consumers directly access the warehouse and therefore depend on the consistency of the schema. Together, these limitations can sometimes exacerbate data maintenance tasks, and certainly do not provide for a nimble platform that is ideal for data scientists.

Data lakes have emerged as a more robust repository, better suited to handling the onslaught of multifarious data sources, both structured and unstructured, that have typified the digital, and data economy. Of course, this includes the rise of newer, alternative data sources used by investment managers to derive alpha. Still, as another redundant repository, data lakes may suffer some of the same shortfalls as data warehouses, becoming stale and out of sync with sources. Data lakes also do not obviate the need to cleanse and maintain certain production data sets. Without proper governance, context, lineage and cataloguing of data assets, data lakes can quickly degrade into unusable swamps. Further, treating data lakes as warehouses, or failing to newly create, redefine or operationalize business processes around them ultimately diminishes their enterprise value.

In any meaningful data undertaking, the need for business sponsorship, strong architects, information security and information risk specialists is clear. But, it’s equally important to engage areas, like legal, audit and compliance at the outset. Early engagement can help to assure appropriate attention to details, including risks. Even more importantly, it generates a shared ownership of outcomes across these governance functions, rather than resulting in potentially more antagonistic relationships.

Transformation, by its nature, is intended to change the status-quo, and may therefore be perceived as risky; in contrast, governance functions are designed to protect the organization, and are therefore more risk averse. This can be a healthy tension that results in better outcomes, and the two objectives are not necessarily mutually exclusive, but it requires that all parties approach any such undertaking with a clear understanding of, and desire to realize the aspirational vision. Remember, enterprise transformation is a ground game, and broad ownership is key. Amazon, for example, often refers to a “bias for action”: here, too, the disposition from all parties must be aligned with “how we do this”, instead of “why we can’t do this”.

Architecture, of course, is a critical consideration of any digital, and data endeavor. While the data, itself, is the lifeblood, a suitable architecture should nominally ensure access, scale, security, usability, and maintainability. Time-to-market, regulatory obligations and price considerations may also weigh, heavily. As is the case for many modern fintechs, a micro-service architecture may fit the bill, but it’s important to consider the interfaces, granularity, data structures, performance and access controls of such services. Data access should transition from tightly coupled database calls to more loosely coupled services. For retail consumers, in particular, mobile is king, but one should not underestimate the impact on institutional clients, either. Also, simply slapping a RESTful HTTP interface on top of a legacy batch system is usually not sufficient, and may even introduce additional operational risk.

Cloud, too, is a critical enabler. While not all enterprises will, or should operate 100% in the public cloud, the path to truly becoming a digital, and data enterprise runs directly through it. Beyond the oft-quoted infrastructure savings for most organizations, cloud fosters agility, productivity and innovation: must-haves for any organization with digital aspirations.

Moving from a cap-ex constrained procurement process to a just-in-time op-ex process also allows organizations to try new things without long lead times or expensive commitments. Further, the tools available on cloud facilitate the adoption of data lakes; mobile and web-based distribution; serverless, event-driven processing; and the kind of scalable data, and quantitative analysis that’s not only required for traditional risk and market analytics, but also for AI and machine learning. The continued growth in cloud has also heavily skewed investment by third-party application and tool vendors towards newer cloud-based solutions, suggesting that this is where the latest and greatest advances and capabilities are to be found.

Put the Customer First

Finally, there is the question of monetizing digital, and data assets. Interestingly, some of the most high-profile technology companies, including several at the top of the market cap list, have considered monetization as a byproduct of the customer experience, and services they create. In finance, the opposite has sometimes been true: the customer experience has been a byproduct of monetization.

Jeff Bezos, the CEO of Amazon, is notorious for having suggested that he doesn’t care about short-term profits. In an April 2013 letter to shareholders, Bezos wrote, “We will continue to make investment decisions in light of long-term market leadership considerations, rather than short-term profitability considerations or short-term Wall Street reactions.” Financial institutions may be able to learn something from Bezos’ philosophy. Specifically, if you focus on the customer journey, experience and value, first and foremost, you will markedly improve your chances at engendering greater loyalty and will likely be better positioned for long term success.

Increasingly, financial institutions are competing with the likes of Amazon, Google, Apple and others, including more purpose-built fintechs: this is true, not only for market-share, but also for talent and new ideas. To be successful, these institutions will need to think, and act like the technology leaders that are, today, principally driving the digital, and data economy.

Harvesting an existing client base with a current portfolio of services is certainly fine in the short-term, but margins are deteriorating and institutions must also chart a course forward that realizes the step-change associated with becoming a true digital, and data enterprise. Clearly, this is more than slogans, a Silicon Valley office, and press releases. The transformation should be led from the top by inspirational and collaborative leaders, but must engender ownership throughout the rank and file. It must consider new business models, and may need to refactor, or abandon old ones. It should also build pathways to new clients, and new opportunities that reflect the rise of the “gig” economy and the shift in consumer behaviors.

This is certainly not an easy journey, but one that may prove to be tremendously exciting and rewarding.