Published by Jessica Weisman-Pitts
Posted on May 2, 2023
5 min readLast updated: February 1, 2026
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By Simon Rayfield, Head of Operations, Alveo
For the global financial services industry, the current data deluge is both a blessing and a curse. As financial data continues to explode in volume, complexity, and diversity – and the data intensity of all business processes increases – there are opportunities for firms to gain a competitive edge in effectively processing and mining this data. For example, the fast extraction and provision of quality information and diverse data sets can give organisations a rich layer of insight into their markets, customers, and financial products, enabling them to streamline their operations and quickly innovate to stay ahead of the curve.
However, while financial services has long been known as a ‘data rich’ industry, organisations still struggle when it comes to harnessing this data to drive decision-making and operational efficiency. Data often quietly accumulates as a by-product of routine business activities – unseen, undervalued, and ultimately unmanaged. When not properly organized, this data can get in the way and potentially clutter rather than inform decision making. When a business lacks a unified vision of what it wants to achieve with all the data it holds, the big-picture data insights that business leaders demand are hard to assemble. Organisational silos are another enduring problem in financial services, with multiple data sets often buried deep in disparate systems.
With the global market set to reach $28.5 trillion by 2025, at a compound annual growth rate (CAGR) of 6%, the finance industry cannot afford to pair this growth with a siloed and reactive approach to data. Customers are now demanding more detailed information than before, whether from asset servicers, banks, or asset managers – and there is no room for ambiguity. When factored alongside evolving regulatory demands for more granular reporting, the need for organisations to improve their data collection and curation practices becomes even more acute.
As a result of these pressures, Data as a Service (DaaS) is coming to the fore as a solution for breaking data silos. This is enabling financial services firms to proactively and effectively provision business users and the application landscape with the data required, anchoring the data into seamless decision-making workflows.
Most banks are failing to capitalise on the competitive advantages their data sets provide. In fact, analysis by Capgemini and the European Financial Management Association shows that only a select few can manage – let alone leverage – their most actionable, valuable datasets.
A recent Vena Industry Benchmark survey points towards a contributing factor. 57% of business leaders, finance executives and operations professionals report that multiple disparate and disconnected data sources are a key data challenge. Data is getting trapped across different silos – ranging from outdated applications to inaccessible data warehouses and scattered data management functions. This often hinders innovation and operational efficiency, which has knock-on impacts on external reporting, client interaction, and other missed opportunities for firms to enhance customer experience and gain a competitive edge.
DaaS is a data management strategy that introduces flexibility, transparency, and fast integration, enabling financial services firms to quickly “put data to use” and maximize its ROI. Putting firms on a solid data foundation, DaaS helps to bridge data silos, functioning as an expert data service bureau to the entire organisation. Data-as-a-Service combined a managed services model of technology – hosting and running a data management platform – with operational services in sourcing, integrating and validating data. It can be offered as a menu of data sets covering for example aggregated end-of-day pricing, historical market data, security reference data, corporate actions and ESG data sets.
To overcome current data challenges, firms must focus on integrating existing workflows and providing them with high quality, consistent data. With DaaS, solutions providers use cloud technology to deliver aggregated, cross-referenced and quality-vetted data sets in different formats and delivery methods to accelerate onboarding and provide easy access to data.
DaaS is also highly scalable. It can cover any data set including corporate actions, security master, issuer and pricing data and can range from use-case specific offers to providing data services at an enterprise level. Data collection and curation typically encompasses the tracking of quality metrics, the cross-referencing and verification of different sources, the set-up of business rules for data quality and data derivation and root-cause analysis on gaps and errors. Cleansed and fully prepared data then feeds into a customer’s operations, risk management, performance management, and compliance strategies.
DaaS provides firms with cross-referenced identifiers; data lineage and other metadata including quality characteristics. These can help financial services develop personalised customer experiences, using predictive analytics to understand consumer behaviour and patterns.
Firms get the best out of their data when it’s easy to visualise and monitor. This means empowering users with a real-time view of data, typically including acquisition, distribution and delivery to downstream applications. A DaaS solution can also provide complete transparency as to the provenance of data, alongside stats on data quality and remediation. DaaS offers users the flexibility to request additional or different data sources, new delivery formats, changes to the data in scope, integration with their cloud data warehouses and its delivery frequency.
DaaS solutions go a step further than managed services, embracing the full suite of data operations and quality management. This one-stop shop enables firms to benefit from shortened change cycles, transparency into the collection and verification processes, and quality metrics on diverse data sets and sources. With technology and services working hand in hand, financial services firms will be able to gain greater control over their data, leading to much stronger decision making and operational efficiency.
Data as a Service (DaaS) is a data management strategy that provides on-demand access to data through cloud-based services, allowing organizations to integrate and utilize data efficiently.
Data silos are isolated data repositories that are not easily accessible or integrated with other data systems, leading to inefficiencies in data management and decision-making.
Operational efficiency refers to the ability of an organization to deliver products or services in the most cost-effective manner while maintaining high quality.
Data analytics involves examining data sets to draw conclusions about the information they contain, often using statistical tools and software to uncover patterns and insights.
Customer experience in banking refers to the overall perception and interaction a customer has with a bank, including service quality, product offerings, and ease of access.
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