For any Chief Data Officer or Head of Trading Technology, the line item for market data is both one of the largest and most complex to manage. The challenge is no longer simply about plumbing feeds into applications. It is a strategic imperative to control spiralling costs, integrate a chaotic mix of traditional and alternative sources, and satisfy ever-stricter regulatory demands for data governance.
As the traditional, siloed approach to data management proves increasingly inadequate, is a unified, platform-centric approach to data curation the necessary evolution for firms that want to survive and thrive? This is precisely the kind of question that will be debated during the panel session “Best of Breed – How to build a curated market data platform” at A-Team Group’s upcoming Buy AND Build Summit in London.The issue is not about simply acquiring more data, but about mastering its entire lifecycle, from acquisition and integration to governance and cost optimisation. And transforming data from a costly operational burden into a streamlined, strategic asset.
Addressing the Core Challenges of Data Acquisition
The initial hurdle for any firm is acquiring the right data at the right time. This process is fraught with four persistent challenges:
- Data Quality: Inaccurate or incomplete data can lead to flawed analysis, poor trading decisions, and significant compliance risks. The Achilles’ heel of many data strategies is the lack of robust, automated validation and cleansing at the point of ingestion.
- Coverage: The demand for new and alternative datasets alongside traditional feeds means firms must constantly expand their reach. Sourcing, vetting, and onboarding new vendors is a complex and resource-intensive task.
- Timeliness: For latency-sensitive trading strategies, every microsecond counts. Ensuring timely data delivery requires sophisticated infrastructure, but even for non-real-time use cases, delayed data can render analysis obsolete.
- Cost: Market data remains one of the largest single expenditures for financial firms. Without centralised oversight, costs can spiral due to redundant subscriptions, poor licence management, and paying for data that is never used.
Best practice demands a fundamental shift towards a centralised ingestion model built on three key principles: Centralised control, to consolidate all vendor management into a single point of entry; proactive quality assurance, applying automated validation checks the moment data arrives; and complete transparency, delivering a consolidated, real-time view of every data stream. By adhering to these principles, a firm can create the necessary foundation to identify coverage gaps and enforce critical service level agreements (SLAs).
Integration complexity: stitching together a fragmented data fabric
Once acquired, market data typically needs be cleaned, standardised and distributed to various internal systems: OMS, EMS, analytics engines, risk platforms and client-facing applications. But often, each source has its own schema, delivery format, and update cadence, creating significant integration headaches.
This fragmentation leads to inconsistent symbology, ambiguous metadata, and duplication of effort across business lines. It also fuels the proliferation of point-to-point connections and siloed databases, a situation that increases fragility and slows innovation.
Modern platform strategies aim to solve this by introducing centralised data fabrics, often built around event-driven architectures and microservices. These frameworks enable firms to consume data once, transform it centrally, and make it available across the enterprise via standardised APIs or pub/sub mechanisms. Crucially, they also allow for decoupling of upstream sources from downstream consumers, reducing interdependencies and making onboarding of new feeds faster and less risky.
Governance as a Foundation, Not an Afterthought
Effective data governance is non-negotiable in financial markets. Platforms should therefore embed governance into their very architecture rather than treating it as a bolt-on solution. Core to this is comprehensive metadata management, providing visibility into data lineage, usage entitlements, and transformation logic. This is essential for both compliance and internal transparency.
Best-in-class platforms now feature data catalogues, entitlement frameworks, and policy-based controls that allow firms to define who can access what, and under what conditions. These controls increasingly operate in real time and across systems, especially as firms move toward federated architectures with multiple lines of business consuming shared data assets.
Additionally, metadata-driven orchestration provides a powerful mechanism to automate operational tasks such as feed switching, format upgrades, and service failovers, which reduces human error and enhances resilience.
Cloud, on-prem, or hybrid? A platform deployment balancing act
The cloud offers clear advantages for data curation: elastic compute for normalisation and enrichment, global distribution via CDN-like architectures, and simpler collaboration with third-party vendors. For buy-side firms in particular, cloud-native data platforms are becoming the default.
However, sell-side firms operating latency-sensitive trading infrastructure or those subject to stringent regulatory controls are often required to maintain on-prem or co-located deployments, at least for front-office workloads. The result is a growing preference for hybrid deployment models, where core data curation happens in the cloud, while ultra-low latency use cases remain local.
Key to success is choosing a platform that supports cloud-agnostic orchestration, with containerisation, edge processing, and fine-grained control over data locality. Without this flexibility, firms risk vendor lock-in or degraded performance in critical workflows.
Cost optimisation: reducing TCO across the data lifecycle
Controlling the total cost of market data has become a strategic imperative. Beyond renegotiating licensing agreements, firms are pursuing platform-led strategies to reduce waste and improve efficiency.
One common approach is to consolidate redundant data feeds and eliminate duplicate entitlements across departments. Another is to implement intelligent usage tracking, ensuring that teams only consume the data they truly need, and flagging underutilised assets for decommissioning.
On the infrastructure side, firms are turning to platform-as-a-service models and data consumption marketplaces to avoid upfront capital expenditure. Modular architectures with pluggable components such as normalisers, converters and adapters also help firms scale incrementally and avoid costly re-platforming.
Ultimately, platforms that enable transparent cost attribution by user, asset class, and business function offer the greatest levers for cost control.
Future-proofing: building intelligence into the platform
Looking ahead, AI and machine learning offer powerful new ways to enhance data curation and insight generation. This is crucial for tackling the age-old problem of data integrity. As panellist Richard Bell, Head of Engineering at CoinShares, aptly puts it, “In my experience AI is not the first time we’ve had to deal with data hallucinations. What you can see is never what you can trade.”
This fundamental gap between displayed and tradable data is precisely where AI/ML models are making an immediate impact. For instance, some firms are using them to identify anomalies in tick data, automatically tagging outliers and flagging stale or suspicious updates. Others are deploying natural language processing to extract structured signals from unstructured data sources – such as news feeds, social media, or earnings transcripts – and fuse them with traditional pricing data.
The next frontier is the use of agentic AI to automate data operations: self-healing pipelines, smart ingestion agents that can adapt to schema changes, and predictive analytics that guide data procurement decisions based on usage patterns and market conditions.
To support this, platforms must expose data in machine-consumable formats, support real-time inferencing pipelines, and include governance frameworks that ensure explainability, auditability, and compliance in AI-driven processes.
Platforms as strategic enablers
As market participants confront rising data volumes, evolving compliance requirements, and intense cost pressure, the case for modernising market data infrastructure is undeniable. This sentiment is powerfully articulated by panellist Martina Satherlund, Global Market Data Leader:
“Building a curated market data platform isn’t just about aggregating sources – it’s about aligning data strategy with business outcomes. To truly optimise cost and coverage, firms must rethink vendor relationships, streamline entitlements, and embed governance from the ground up. The future lies in platforms that are not only interoperable but intelligent – leveraging AI to surface insights and automate quality checks in real time.”
The firms that succeed will be those that heed this advice, treating data curation not as a cost centre, but as a strategic capability. By implementing flexible, scalable, and intelligent platforms that address the full data lifecycle from acquisition to analysis, they can transform data from a bottleneck into a true source of competitive advantage.
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