Why Banking Data Management Is the New Strategic Asset for Scale, Innovation, and Compliance

October 10, 2025
Last Updated: October 10, 2025
Read Time: 7 minutes
Authors: Ted O’Connor
Operations & Growth
Sell Side

In part 1 of this 2-part series, Why Sell-Side Data Modernization Must Be an Institution-Wide Mandate, we talked about why sell-siders are now looking at quality data as a strategic asset and are thinking more holistically about upgrading the modern data stack. In this article, we will dive into how institutional-wide data modernization pulls the most powerful growth levers that multiply speed, innovation, and scale, which Accenture correctly referred to as the “ultimate competitive advantage” and how “by 2030, scale will define success.”i

Investment banks are living large as the M&A giant has awakened and are regaining some lending territory from the private credit market, reportedly now arranging more than $20 billion of M&A debt, thanks to their creation of new collateralized loan obligation funds.ii These forces, in combination with a more certain regulatory environment, have resulted in US capital market banks recording the highest net revenue Q2 performance growth in 5 years, up 15% from Q2 2024.iii The problem is the money left on the table from a post-great recession decade of high cost-income ratios and returns on equity that have consistently failed to eclipse firms’ cost of equity capital.iv

Institutions can make an outsized business impact by winning the race to the best modern data stack, reducing costs, accelerating speed to market, enabling scalability — and allowing their talented humans to spend more time making money.

Scale’s wingman is data modernization

Accenture wrote that, “The beating heart of the investment banking business has seen profound change over the past decade,” in its 2021 report on capital markets, referring to the period between 2009-2019. I would argue that 2020-2025 has produced much more profound change, and in a shorter time frame — at a time when banks were striving to catch up with that first wave of change, the migration to cloud environments and SaaS solutions. Most definitely, it has proven a massive undertaking to integrate these SaaS point solutions with traditional banking systems, built on a lot of disparate systems that were either amalgamated through acquisitions, individual business units, or built internally. Yet, the most frustrating problem that institutions have faced in the last decade of digital transformation is the inability to scale efficiently.

As institutions onboard new clients, product lines, or tradeable assets like crypto, structured products, or private markets vehicles, they often lack unified systems that can handle booking, risk, compliance, and reporting seamlessly. Or their systems’ processing power simply cannot handle big increases in volume. In a 2024 survey report, 94% of sell-side respondents said that data fragmentation made integration of new applications challenging, and 90% reported that poor data quality caused issues in clearing and settlement.v

Holistic banking data and operational modernization reduces the need for expensive, time-consuming custom builds, duplicative manual processes, and tech-related bottlenecks. Instead of new products triggering manual workarounds in valuation, booking, and risk monitoring, a data platform built expressly for capital markets participants, like the Aquata platform, automates the ingestion, normalization, and centralization of new datasets, as discussed in part 1.

This level of data management opens a lot of doors, including self-service tooling for business users. Automation and no-code tooling enable sell-side institutions to avoid the necessity to go on a pricey talent acquisition spree when scaling.

AI-powered self-service opens the door to innovation

Self-service tools with intuitive design help sell-side internal teams to build, iterate, and solve business challenges faster and more effectively. Recent advancements in AI have created fertile conditions for “citizen coders” or “non-technical users” in multiple functions who can now execute tasks that would otherwise have demanded emails to IT and other departments and considerable time expenditure. People across banking functions can create their own data quality and governance rules and customize their own models. They can ask our Aquata AI Copilot to make tailored dashboards for decision making in compliance, risk modeling, analytics, and more.

Users in risk, accounting, treasury, sales, or trading can ask AI-powered assistants to locate relevant data or uncover trends. My colleague referred to this as data curation that turns data noise into clarity. Self-service allows teams to innovate and collaborate more effectively and gives them a new sense of ownership. Often, information is not shared effectively between the front, middle, and back offices, creating grit in the gears of the bank. Self-service tools prevent banks from heavy reliance on external vendors and their own tech teams. Additionally, these intuitive tools are a shot in the arm for innovating distinct offerings in new financial products, structures, and vehicles.

Arcesium Logo Mark
Banking Transformation

“A large share of bank technology investment today is directed toward “run-the-bank” (RTB) initiatives (think core tech activities such as running existing applications in the cloud) rather than “change-the-bank” (CTB) efforts that can build real competitive muscle. No doubt much of that RTB funding is necessary and well spent. But if even a fraction of those funds can be redirected toward more innovative efforts, banks can unlock synergies and position their business to thrive.”

- BCG’s Tech in Banking 2025: Transformation Starts with Smarter Tech Investmentvi

Compliance, risk, and the cost of poor data quality

Inadequate banking data management modernization hampers reporting and auditability, which naturally limits the ability to build stronger trust and transparency with FINRA and the SEC, as well as clearinghouses, clients, and counterparties. Regulatory agencies expect sell-side firms to maintain effective monitoring of liquidity and funding risks to guard against blind spots. The key to preventing blind spots is automated compliance reporting and risk disclosures.

Standardization and centralization of an institution’s data, with an emphasis on quality assurance, is the tentpole on which the data quality, automation, and consolidated reporting workflows tent is erected. Teams can schedule automated bulk generation of reports in a repeatable way that delivers clear information so they can be more responsive and reduce work while ensuring consistency and compliance across investor tear sheets, board packs, and regulatory filings.

Another method for banks to contain rising compliance costs is to install superior data lineage and traceability for quicker stress testing and auditability. The Aquata data governance and lineage solution is a data time-travel machine. The platform’s bitemporal modeling ensures permissions and entitlements are correct in multiple timelines so regardless of any changes made to information, the audit trail retains integrity with reliable historical information.vii Moreover, using data lake storage enables complete observability of all centralized data. No blind spots. Automated oversight tools take over the continuous monitoring of data pipelines and processes, so data quality becomes persistent, and anomalies are rapidly resolved. 

To take a deeper look at how to achieve high-fidelity, real-time liquidity reporting for strong risk monitoring and capital planning, see my previous article.

Comprehensive banking data modernization

This is the phase of digital transformation that calls for banks to look at data modernization through an organizational-wide lens. These businesses' pillar assets may be the US dollar, but their data is just as valuable a currency in the digitally transformed financial system. When dealing with inaccurate, fragmented reference data, sell-side institutions certainly grapple with mismatched trades, reporting snafus, and sinister concealed risks. However, subpar data management also translates to stunted growth and the inability to scale the business. The opportunity cost of balking on institutional data transformation is substantial. The opportunity to gain ground on the competition is undeniable.

Key takeaways

Q1. Why are investment banks prioritizing data modernization now?

 A1. Rising revenues, M&A resurgence, and new regulatory clarity demand holistic banking data management to scale efficiently and profitably.

Q2. How does modernization impact growth levers?

 A2. AI-driven automation reduces costs, accelerates speed to market, and enables scalability, making banking data modernization the ultimate growth enabler.

Q3. What role does self-service play in modernization?

 A3. Self-service data tools empower business users to manage governance, reporting, and analytics, driving faster collaboration and innovation.

Q4. How does better data management help with compliance?

 A4. Centralized, standardized data ensures accurate regulatory reporting, stronger audit trails, and reduced compliance costs.

Q5. What’s the cost of ignoring modernization?

 A5. Institutions risk inefficiency, higher costs, and lost opportunities, as poor data quality and fragmentation hinder scale and profitability.

Ted O’Connor

Authored By

Ted O’Connor

Ted is a Senior Vice President focused on Business Development at Arcesium. In this role, Ted works with leading financial institutions in the capital markets to optimize data, technology, and operational needs.

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