How Firms are Currently Prioritizing Data Quality and Control
Manual processes remain prevalent across capital markets operations, with 68% of firms reporting they always, mostly or sometimes rely on spreadsheets and other offline methods to process data before it is formally stored. Only 9% indicated always using manual methods, but notably, no firms reported fully eliminating them, highlighting a persistent reliance on non-automated practices.
The biggest pain points in data governance centre on managing complexity and ensuring accuracy. Volume and velocity of data (56%) and maintaining consistency across the data lifecycle (55%) are top challenges, followed closely by regulatory compliance (48%) and inconsistent policy enforcement (47%). Firms also struggle with documenting transformations (45%), securing organizational buy-in for centralized frameworks (44%), and lack of ownership over data quality (40%).
To strengthen regulatory adherence, organizations have adopted a diverse set of proactive controls. These include:
- Implementing audit trails
- Aligning compliance metrics with internal controls
- Increasing internal review cycles
- Embedding compliance alerts and checklists
Many firms are investing in regtech tools, geo-tagged controls for jurisdictional compliance, and automated variance detection to reduce manual monitoring. A growing number are consolidating compliance processes into unified dashboards. This signals a shift towards scalable, transparent, and tech-enabled governance frameworks that respond rapidly to evolving global regulatory standards.
What is clear is that firms are putting more value on the data assets they hold, but data is not clearly owned and initiatives to improve the quality of these valuable data assets can often fail to find clear sponsors. Data is not yet seen as a product with a roadmap and quantifiable benefits attached to that roadmap.
Question 1: In your organization, how frequently do your operations users collect the data manually and use spreadsheets, or other manual processes, to perform operational functions before storing that data?
We mostly collect and store data this way
We sometimes collect and store data this way
We rarely collect and store data this way
We always collect and store data this way
Question 2: What are the biggest challenges you face when it comes to data lineage, reconciliation and governance?
(Respondents were asked to select all that apply)
The volume and velocity of data making lineage tracking and reconciliation difficult.
Maintaining data accuracy and consistency throughout the data lifecycle.
Ensuring compliance with evolving regulatory requirements related to data governance.
Difficulty in establishing and enforcing consistent data governance policies across the organization.
Difficulty in understanding and documenting data transformations and calculations.
Resistance from different teams to adhere to centralized data governance frameworks.
Lack of clear ownership and accountability for data quality and governance.
Lack of automated tools and processes for tracking data lineage.
The time and resources required for manual data reconciliation and lineage tracking processes.
"Firms can no longer afford to treat manual processes as "good enough". The cost is measured not just in time, but in missed opportunities, increased risk and reduced agility. By shifting to automated, integrated data practices, firms can turn data from a liability to a competitive advantage."
Jenn McMackin, Global Director, Data and Managed Services, Gresham
"The regulatory landscape today is fundamentally driven by data consumption. If you look at recent regulatory changes — things like email rules, refeed requirements, and ME feed enhancements — they all demand more detailed and structured data. By 2027, we anticipate a significant increase in the volume of data we’ll be expected to report, which means organizations must build models capable of automating data collection and mapping it effectively across the full lifecycle.
To meet these demands, it’s essential to have partners that can help translate regulatory requirements into practical data models and ensure we stay compliant. Yes, there’s pressure, but I see this more as an inevitable evolution. As the industry continues to innovate and introduce new financial products, regulators will need to establish guardrails to manage risk and ensure compliance. The scope of regulatory expectations will continue to grow in step with the expansion of product offerings — and as firms, we’ll need to be ready for that.”
Krzysztof Wierzchowski, SVP Business Transformation, Franklin Templeton
