True or false: Someday, digital transformation of the financial services and capital markets industries will be complete?

Consider that the second industrial revolution spanned 40 years starting in 1870 and continues to evolve today. Most firms are wading through the early to moderate stages of digital maturity, actively exploring and experimenting with new technologies. But they complain of challenges with legacy systems, data silos, and the need for more robust data management practices.

The main objective of data and systems modernization is to reap benefits from the innovations of the digital age via automation, to get more precise and faster in driving risk-adjusted returns. In the current environment of volatility and uncertainty, the ability to respond quickly to change is an invaluable survival trait for firms.

The agility imperative: Matching hedge funds’ speed

Asset managers are adapting to become nimble when responding to changing market demands and implementing new ideas. However, launching new offerings or entering new asset classes requires spinning up significant amounts of data infrastructure, which can be a major obstacle to progress.

It’s great if a manager has masses of data at their disposal. But if disconnected technologies and processes mean its people have no easy way to access, authorize, and analyze it they will struggle to efficiently complete analytical responsibilities and fulfill stakeholder requests for information accurately and efficiently.

Firms operating in an inadequate state of modernization will have to change. Otherwise, they will continue to throw people (and their salaries) at problems like adding new asset classes, entering new markets or welcoming new investors. Good ideas can die on the vine because of the operational load of acting on them.

New tech as a risk mitigation strategy

Risk management has become a new breed of animal in the past 15 years. The gradual digitization of the investment management ecosystem (along with the 2008 financial crisis) has forced regulatory agencies to bring their own rules into the 21^st century to protect investors and the financial system. The global pandemic moved the digitization timeline ahead by years with the progression toward remote work and cloud infrastructure.

The 2020s have been a risk and compliance nightmare, with teams managing an array of new regulatory frameworks and novel risks in areas such as digital transformation, technological advancements, and cybersecurity. Specific product and regulatory developments are also bringing “important risk elements to the investment management industry in the form of direct indexing, mutual fund to ETF conversions, convergence of traditional and alternative asset classes...”

See this AlphaWeek article on how to propel diversified public-private strategies.

Trends like the popularity of third-party technology vendors and alternative data have introduced new risk vectors. Softening financial regulatory scrutiny and tariff threats elevate risk management vigilance to top priority. And the rapid evolution of generative artificial intelligence (GenAI) has thrown more accelerant on the data quality flames. Poor-quality data brings far-reaching consequences, including subpar GenAI performance, erosion of customer trust, regulatory noncompliance, and financial and reputational damage. See our earlier article for more on GenAI and mitigating risks with robust data management.

Gritty data grinding the gears of alpha

Neglecting data management can lead to a build-up of complexity and inefficiencies, grit in the gears of growing AUM. Everybody knows that legacy data infrastructure requires significant manual effort to consolidate information for meaningful reporting and analysis. Asset management is too complex for older data architecture. Firms of increasing size find older architectures generate data silos that are hard to reconcile or summarize across the firm.

Different parts of large firms can refer to the same things in different ways. For example, a trader may regard a swap equity as the same kind of trade as a straight-up equity trade. It seems straightforward: a simple synthetic equity versus a regular equity - with the same exposure and same economics. However, for the back office it has a really big implication from a tax and accounting perspective. For compliance, a synthetic equity means much higher regulatory scrutiny, and they need to keep track of that.

A firm that doesn’t have a single normalized model means its data either has to be translated between domains or it has to be massaged. And every time it moves around across departments, it's another check, another settlement mismatch, or it shows up incorrectly on the reports. Someone catches the error in the exception report and must manually fix it. Or they miss it altogether. Problems radiate in many directions. This is the grit in the gears of your alpha machine. Moreover, as the business grows, a few daily manual fixes become unmanageable, swelling to a hundred or thousand fixes each day.

Driving risk-adjusted returns and better CX

Aside from modern, unified data architecture’s capabilities to relieve operational bottlenecks, speed workflows, and manage risk with agility in volatile times, it is also the foundation for driving better risk-adjusted returns and client satisfaction. Clients expect modern turn-around times for requests. When a portfolio manager cannot answer questions about a new investor report within an hour, investors get angry. If an investor calls wanting to think about their portfolio differently and run some what-if scenarios, a PM will lose hearts and minds quickly if they have to wait for a meeting next week to review the allocations.

Legacy technology prevents PMs from executing creative ideas for investors and prevents analysts from bringing creative ideas to PMs. It’s like sending a platoon out to battle with muskets. In this age of magical, real-time information, investors expect speed and accuracy. Firms win internal and external hearts and minds when they can smoothly launch new business strategies, leverage intuitive and automative reporting capabilities, run ‘what-if', and confidently re-distribute risk across strategies.

With modernized data and operational infrastructure, a firm sends PMs into battle with cutting-edge tools that empower them to have fruitful collaborations with clients that engender trust and loyalty – and returns.

Check out our blog on Transforming Technology in Asset Management: Tools, Trends, and Data Architectures for more insight.

Enjoy your vintage turntable, not your vintage investment data infrastructure

Delaying investment in proper data infrastructure can lead to polycrisis situations where everything goes wrong at once. Tiny issues accumulate and lead to a very bad day when things that slowly were decaying fall over very quickly in unpredictable ways. Firms should recognize when their people are at maximum capacity doing tasks that could be automated. If you burn out your talent, they leave. If you empower your talent, they have time and capacity to be resilient to change. The data science and IT departments often scramble to make changes when new funds or business models are introduced. Growth plans get stymied when you can't hire people to work on your vintage system. Worse, when a market crisis descends, the firm will be ill-prepared to move quickly to manage risk and/or seize opportunities. And in 2025, disruption is a real possibility.

High performing asset managers will strategically invest in their data infrastructure and adopt modern architectures to achieve agility, effectively utilize talent, meet rising expectations, and navigate volatile market conditions.