Your Guide to Data Quality | Why Now?

By Mick Hittesdorf, Senior Cloud Architect, KX

Data quality is an essential prerequisite for actionable, trusted, AI-Ready data. But how do you measure your data quality? The consensus among data quality professionals in the industry is that data quality metrics fall into the following data quality dimensions:

• Accuracy: Data reflects the true, error-free state of a real-world event or object.
• Completeness: All required fields and entries are present without gaps.
• Consistency: Values remain uniform and follow standard formats across multiple systems.
• Timeliness: Data is up-to-date and accessible when needed for real-time decisions.
• Uniqueness: There isn’t any redundant or duplicated data present.
• Validity: Information conforms strictly to defined rules, ranges, and formats.

As financial institutions make the serious, strategic commitment to leveraging AI systems that can automate, amplify, and improve workflows, it is becoming clearer and clearer that AI outputs are only as good as the data inputs.

Without market data you can trust, your team’s talent is wasted on timestamp alignment, feed cleaning, and symbol mapping. This is no longer acceptable. Those expensive, scarce talents could instead be focused on high-level analytics and alpha generation.

The Non-Negotiable Foundation – Data Quality Assurance

The unforgiving nature of capital markets data means that "garbage in, garbage out" is an absolute truth. Firms that invest in quality data and the underlying data platform infrastructure first will find greater success in the era of AI.

Modern data platforms simultaneously address legacy pain points and support high-velocity AI use cases:

1. Cloud-Native Architecture: The move toward cloud-first solutions allows firms to use the full elastic capabilities of storage and compute. OneTick Cloud offers a service model where developers spin up analytics workflows in days, without infrastructure buildouts.
2. Unification of Data: A robust market data platform must seamlessly handle both real-time and historical data through a single engine, enabling consistent joins and analytics.
3. Open Data Formats: The shift towards open data formats isn’t gradual – it’s a stampede. High-performance storage is necessary for read-intensive, analytics-heavy tasks, while transactional and metadata management capabilities add crucial UPDATE/UPSERT functionalities and avoid vendor lock-in.

Data quality must be treated as a first-class citizen. At OneTick, we have established a comprehensive and systematic data quality process that ensures explicitly, quantifiable data quality invariants are satisfied - and stakeholders are proactively alerted when they are not.

The Alternative – What Happens When Data Quality Is Not Prioritized

At every hedge fund, bank, and asset manager, CEOs, CTOs, CIOs and desk heads are all pushing agentic AI investment aggressively. The budgets are real and so is the urgency to translate the promise and potential of AI into bottom-line revenue, operational efficiencies, new trading signals, and better risk management.

When data quality assurance is skipped, something alarming happens. Agents hallucinate. Symbols don’t reconcile across venues. Timestamps drift. A corporate action creates a phantom 75% crash. The backtest leaks future information. The agent confidently produces an answer the desk cannot trust, and your high-visibility AI initiative stalls.

The LLM is not the problem, it’s the data on which the agent relies for context. When internal data is not temporal, not point-in-time, and not AI-ready, quants and AI teams spend 70-80% of their time cleaning feeds, mapping symbols, and aligning timestamps before an agent can accurately reason on anything. That is the Data Tax. It is the single biggest obstacle to agentic AI in capital markets today.

Free Up Resources – Use OneTick Cloud

OneTick Cloud from KX delivers AI-ready, hydrated, temporal market data as a managed service. Pre-normalized across 250+ venues, 30+ years of history, point-in-time with no look-ahead bias, machine-readable from day one, fed natively into Python, SQL, and KDB-X.

New funds, new pods, and new desks can get started with OneTick Cloud’s on-demand market data access, right now. Gone are the days when you might spend three months or more and $150K in CapEx to start up your own tick capture and storage environment.

Start your free trial to OneTick Cloud here, and start querying in minutes.

Want to learn more? If you’re building systems that need market data infrastructure to power your AI initiatives, request a demo today.

Best wishes,

Mick Hittesdorf