For years, investment firms have been adopting tech tools to support business functionality and to, frankly, keep up with the Joneses. Digital transformation is an ongoing multi-phase process executed with varying levels of success that’s turned out to be more sweeping in practice. By going beyond technology, firms stand to gain a comprehensive strategic reorientation on how they perceive, manage, and extract value from their data.

Financial technology is evolving in scope and sophistication

Not so long ago, financial technology meant giant mainframes or huge spreadsheet models. As asset managers sought more from their technology they moved to inflexible home-engineered or vendor software on-site or in private data centers. These solutions were task oriented but couldn’t adapt quickly or integrate easily with other applications. Businesses then moved those same solutions to the cloud for easier scaling but still faced closed software that required a lot of vendor involvement.

Now, we are on the road toward more self-service, scalable, cloud-native solutions for the buy-side. Even siloed cloud native solutions that address a single business line are being looked at over the course of transformations – executive stakeholders are seeking timely normalized views over the whole firm. But that is still a work in progress.

Firms are leveraging specialized platforms and offline tools to support unique asset classes, focusing on aggregating data, portfolio views, and client reporting to create an end-to-end view of the investment lifecycle. Quant managers use algorithmic trading platforms for real-time market data and order execution. Firms have deployed SaaS point solutions for most functions, from reconciliation and performance attribution to fund accounting. Some are even using new blockchain solutions for regulatory compliance and tokenization. However, the efficacy of today’s cloud-based solutions is reliant on the firm’s data management practices and infrastructure.

A data house divided: quest to achieve data democracy

Anomalies, errors, inefficiencies, and bottlenecks have hindered the impact of investment lifecycle technologies. Firms have struggled with unwieldy datasets flooding in from numerous external and internal sources, forcing them to stitch data together from disparate systems. Incongruent structures lead to opaque data, firm-wide access challenges, and difficulty consolidating information for meaningful reporting and analysis. Everybody is trying to solve the data problem before the competition. The generative AI revolution has added another level of urgency in getting data houses in order.

The industry has discovered the oil and can get it out of the ground; but first, firms must refine it for daily use.

Of course, AI/ML has had its automation and analytics fingerprints across the tech ecosystem for years. Two-thirds of asset managers recently surveyed said they were already using AI/ML to drive change in their business. But the full realization of AI/ML value is still on the horizon. Global asset managers and asset owners said they were planning to further develop capabilities in AI (56%), predictive analytics, (51%) and cloud computing (41%) over the next three years.

Firms are racing toward AI maturity, as those that have moved beyond the conceptual stage into the development stage (or farther) have risen from 33% of US asset management professionals in 2024 to 55% in 2025. To reap rewards from AI and the rest of the digital transformation, we must make sense of the data chaos.

Addressing data chaos: building a central source of truth

People need to feel confident that the information they are producing, reporting, and communicating to stakeholders is accurate. Moreover, analysts, PMs, accountants, risk managers, and everybody else need quick access to precise, reliable data to meet current business needs. If they have to chase colleagues for data, they lose time. A centralized “source of truth” for data is an essential foundation for any modern operational strategy. It ensures that teams across various departments work from the same set of accurate, up-to-date data, eliminating inconsistencies and discrepancies. One reliable data source allows for the integration of various platforms, systems, and processes.

Asset managers are demanding solutions that aggregate data from in‑house sources, third parties and their service providers for valuable intelligence and more actionable insights. Data transformation will enable a firm to achieve the democratization of data, self-service control, the impactful use of data science, and an advantage in leveraging new technologies like GenAI. The foundational technology and data architecture required for widespread AI adoption are still in the relatively early stages of maturity for many firms. This provides another opportunity to gain an edge by realizing ROI from GenAI before the competition.

RELATED READING: A Tech-Tonic Shift: What’s Leading Asset Managers to Need New Tech?

Data mesh & beyond: architectures that drive data democratization

The first step in achieving game-changing data transformation is installing the right data architecture.

The quality of a firm’s data architecture is fundamental to:

Satisfying the front-office's need for better client service and satisfaction
Meeting the middle-office's need for robust data validation processes to minimize errors and meet compliance with financial regulations
Addressing the back-office's need to streamline processes and reduce operational risks

The end goal is integrated financial data that flows seamlessly across all your systems.

There still are a lot of data silos in financial organizations, particularly big firms. But when an executive wants to gain a firm-wide view over things, it becomes a case of issuing commands to leaders of every department to prepare an analysis in a lengthy, custom one-and-done activity. Firms must find easier methods for leadership to iterate quickly.

We favor data mesh architecture for institutions that want to give ownership to departmental teams and need to transform colossal volumes of raw data into an actionable asset that can drive higher-level decision making and ultimately ROI.

As global firms increasingly focus on controlling data access methods, API functionality support has become crucial. Data mesh architecture supports APIs to enable democratized access across the firm. Additionally, data mesh architecture acts as a bridge between centralized legacy systems and the decentralized, data-sharing principles of collaborative businesses. This approach enables the smooth replacement of underlying systems, allowing for an easier, faster plug-and-play approach to upgrades. Moreover, this can buy time for IT or data science leaders rushing toward digital transformation by extending the life of existing technologies.