The IMD Wrap: No more turf wars, or why CDOs should heed the Voice of the CTO

Here at WatersTechnology, editor-in-chief Anthony Malakian recently took on an ambitious assignment. Dubbed the Voice of the CTO, Tony interviewed eight chief technology officers from tier-one international banks between October and December last year. The result was a series of five articles looking in depth at the challenges and opportunities facing chief technology officers at large financial firms, and how the CTOs and their teams are tackling them.

The themes covered were: assigning budget to AI versus cloud replatforming; change versus continuity; striking a balance between AI innovation and cloud migration; data management and the role of the chief data officer; and the challenges—technical and human—of implementing enterprise architecture.

And while the focus of the series was on how CTOs are addressing these, any data professional should also immediately notice that many of these are data issues or involve technology issues that directly impact a firm’s data and its data consumers.

Now, I don’t know how much CTOs and CDOs talk—or perhaps more importantly, listen—but it’s clear that both should be involved in discussions around these issues, and should be defining the requirements and contributing to the solution. So, since these are topics that should also keep data professionals up at night, just in case you missed them first time around, allow me to offer some relief in the form of summarizing how some top-tier CTOs are addressing them. 

The first article in the series examined a topic that I’d previously covered to a lesser degree in a previous IMD Wrap: that in today’s data climate, you can’t talk about budgets without talking about AI, which has consumed much of the fintech conversation over the past 18 months and has the potential to consume much of firms’ budgets also.

Here’s what one interviewee, the head of engineering and technology at a global systemically important bank (G-Sib), told Tony: “I would have to say that for the year of 2023, AI was almost exclusive to everything we did. Of course, we’re delivering on regulatory obligations and long-term projects, but in tandem with that, we’ve put an uber amount of focus on leveraging that data and taking in the advanced side of AI—i.e., generative, large language models, and cognitive compute aspects. We have accelerated that significantly in 2023, and it’s going to be a big story for 2024.”

This push towards AI—including greater interest, if not full understanding—by companies’ boards is explored further in the third article in the series. 

Overall, this accelerated investment across the industry may come at the expense of other technologies, such as blockchain, which another CTO described as “a solution looking for a problem,” and ultimately, for most use cases, “a complete waste of time.”

As Tony observed, “This insight illustrates the challenge CTOs face when allocating dollars to not falling behind on the innovation front, while not wasting resources that could have gone toward modernization, automation, data management, and regulatory need.”

For an organization navigating this budgetary tightrope, the balancing act requires teamwork—CTOs and CDOs working together—and for those leading the charge and responsible for translating business strategy into practical IT deployments and data initiatives to be on the same page and speaking the same language. 

That doesn’t mean they need to agree on everything—indeed, different opinions can prove critical in shaping which projects get prioritized over others. So long as you agree on the destination, you can argue over the route—whether it’s fastest, most fuel-efficient or most scenic, or in our case, delivers the fastest time to market, is most cost-effective, or delivers the most features and capabilities. 

Indeed, with AI and cloud migration a priority for most firms, and cloud migration being one of the stepping stones towards making better use of infrastructure and enabling AI, that journey of innovation and tech renewal requires both data and technology experts. You can’t embrace the benefits of AI if the data you want it to provide insights into is residing in decades-old legacy platforms. 

Another CTO describes their firm’s goal as getting to the cloud, because that enables many more benefits. As Tony describes it, “If you can break down data silos and therefore connect systems in the front, middle and back offices, you have data that is more consistent, of a higher quality, and that can be used for multiple purposes—whether that’s for trade analytics, risk management, or regulatory reporting needs.”

Or, as the CTO puts it, “We have tried [getting budget] in terms of getting those data lakes/warehouse and data enterprise budgets—data governance, data quality—those big pieces whereby we need to clean out the backend because, you know, shit data in, shit data out. Makes perfect sense, right? But I can’t think of many that have been successful… because the business doesn’t buy into it.”

And herein lies perhaps the bigger challenge: even if your CTO and CDO are on the same page, the business side may not be, so people familiar with the tech and data requirements must define and communicate those in terms the business understands and will buy into.

This is a recurring issue throughout the Voice of the CTO series, sometimes with obvious frustration. In the second article in the series, Tony explores the push-pull between innovation and stability, describing a scenario faced by many in the space as an “inexorable push coming from the top to be ‘data-driven’ and ‘cloud-native.’ These are catchy phrases for an earnings call, but they ignore the reality of modernizing and automating legacy systems, and the technical debt they accrue.”

He cites a $2 million project to consolidate multiple trading systems at a bank into a new platform, which delivered little despite spending a lot of (extra) money plugging into legacy systems, addressing concerns over data licensing and reporting processes, configuration, and decommissioning old platforms, each of which caused their own ripple effects.

These older systems are not only becoming buffers to making upgrades like the one described; they are also creating barriers to modernization and adopting new tools and technologies to make banks more efficient. Regulatory reporting burdens demand that firms be less siloed, and more interconnected; that data should be discoverable and available. To meet these needs, and to take advantage of AI, firms need to embrace the cloud, which in theory provides a more secure and stable infrastructure, with less redundant technical debt.

One CIO at a G-sib firm expresses hope that the adoption of new technologies and greater automation will force more conversations—and crucially, more questions that lead to a shared understanding and appreciation of what is required—between tech, data, and business execs, which are usually determined by economic cycles: technologists are charged with cutting spend, but aren’t given similar responsibilities to help shape technology decisions that can drive revenues when firms are in growth mode. Data professionals often complain of the same frustrations: because the business side doesn’t appreciate the value of data, it views it as a cost rather than something that drives profits—and can indeed be a profit center in itself, if a firm has the assets and strategic thinkers to monetize them.

“For any modernization project, technologists need to be able to articulate the drivers, cost, and ROI for these endeavors: Cost control versus business drivers? How will this help the bank to react to market shifts better, faster? How does it help the bank make money? How does it help protect money?” summarizes Tony.

Taking data seriously

Much like CDOs and data managers, who are given a budget and tasked with ensuring the organization continues to receive the data it needs, as well as exploring new data, which comes at an additional cost, CTOs and senior technology execs rarely get to set the strategy they’re tasked with delivering, or the budget they have to deliver it within. 

Instead, they get told that the bank or fund is starting a new business area and are told to “make it so.” Find the right tech platform, integrate the data, make sure everyone has what they need, instead of involving them in the earlier decisions about which business areas the firm has existing tech to get off the ground quickest and support from day one, and creating a roadmap for exploiting existing resources to move into new areas, or to give breathing room for them to select external vendor products that can get them up and running quicker than pursuing an in-house build.

Some say vendors do a better job of thinking about products than in-house teams, and that makes perfect sense: an in-house team may come up with the perfect feature set for its firm’s needs. But a vendor serving tens or hundreds of clients will most likely—as a result of building products for a diverse array of clients—already not only have those features built in, but will also have features that a firm doesn’t even realize it needs yet, and which would be more expensive and time-consuming for the firm to add itself when that time comes.

All too often, the buy-versus-build debate comes down to egos and economic cycles, rather than what’s the most sustainable approach to technology for the long haul. In an industry bound by fiduciary responsibilities to ensure best execution that can come down to fractions of pennies, the idea that firms would over-spend on projects that are poorly conceived and overseen, and delivered late, if ever, boggles the mind of an external observer.

And herein lies the culture clash between CEOs who want to call their banks technology firms and the technology staff charged with overseeing each component—whether bought, built, or borrowed. Firms must take their data and technology seriously—not just say that they take it seriously—before they will gain genuine commitment to those areas from those outside the tech and data functions themselves.

Tony spends one article in the series specifically discussing data and those charged with managing and protecting data, and ensuring it is ready for the designated uses—in particular, CDOs. One CTO describes how as their bank became more serious about data, and ring-fenced a sizeable budget to create a golden source of securities, products, and customers, all of that needed an owner—and that ownership couldn’t be left to disorganized collaboration between different functions. Nor should it be divided between business-line CDOs who create fiefdoms within their own silo but don’t answer to the firm-wide strategy.

Instead, the key is to make data a priority at the highest level and recognize that data management must fit hand-in-glove with a firm’s overall enterprise architecture. Without data management in place, “technical debt increases and datasets spread like an expensive weed throughout the organization, failing to provide impactful insights while adding to costs.”

In recent years, much has been made of the interoperability movement. But the true measure of your firm’s interoperability has nothing to do with APIs or desktops, but rather how well your tech and data teams work with each other, with the business side, different business lines, and the organization as a whole. Once each function’s voice—not just the Voice of the CTO—is heard and understood, and all sides are speaking as one, then projects will become less expensive and painful, and more productive and successful.

Or, as Tony sums it up, senior technology execs are learning that “their lives become easier if there’s someone by their side telling the story of data. And, ultimately, the hope is that the story becomes more palatable to the bank’s top executives.”

I hope this “Cliff Notes” summary of these articles proves useful and encourages tighter collaboration between tech and data in pursuing their firms’ goals. If you agree, disagree, or want to share your own thoughts or experiences, please contact me at max.bowie@infopro-digital.com.