TrueTick · Data Quality & Observability for Capital Markets
Trust your data, tick by tick
Your market data may be available and queryable. That’s not the same as fit for use — whether it’s OneTick or any other connected source, right now you’re probably taking someone’s word for it.
The Market Data Problem
You’re not measuring trust. You’re assuming it.
Capital-markets firms depend on market data for research, trading, risk, and reporting — yet quality is treated as a given until someone downstream finds a problem.
Trust is assumed, not demonstrated.
Vendors and internal platforms claim reliability. Few show the evidence.
Checks are fragmented and manual.
Scripts, dashboards, and escalation paths differ by team and by dataset.
Bad data doesn’t announce itself.
It just flows quietly into research, trading, and risk decisions.
Can you prove your data is fit for use?
Score your data across six quality dimensions.
Each dimension builds on the one before it — robust lower-dimensional quality is a prerequisite for meaningful measurement above it.
Is your data available exactly when users need it?
Timeliness
How well does the data agree with an objective source of truth?
Accuracy
Is all required data present — no missing records or fields?
Completeness
Does the data conform to defined business rules and formats?
Validity
Does your data contain any redundant or duplicate records?
Uniqueness
Does the data reconcile with the primary source or system of record?
Consistency
Continuous quality, proven at every run
TrueTick: Know your data is fit for use.
Timeline · Real-Time Monitoring
Dimension by dimension, updated continuously.
The timeline tracks Timeliness and Completeness tests per database as they execute. A single red bar in an otherwise green row tells you when and where a test failed — before anyone downstream notices.
Summary · Test Result Overview
Every test, every database. One composite score.
Every test run — across T+1 and real-time data, every dimension, every database — rolls up to a single composite DQ score and a pass/fail breakdown. Filter by database, dimension, category, or notebook to isolate exactly what you need to see.
Detail · Database Quality Matrix
Every database, every dimension, at a glance.
A color-coded matrix maps quality scores across all your databases and dimensions. Green is passing, yellow is within two points of threshold, red is below. Drill into any cell to see the individual test name, dataset, date range, and run timestamp.
Failure Modes TrueTick Catches.
Four ways bad data slips through unnoticed — and the layer that catches each one.
Stale Feeds
Catches timestamps drifting past SLA before a trader notices lag.
Silent Gaps
Flags missing records mid-session, not at end-of-day reconciliation.
Duplicate Ticks
Catches repeated prints that quietly inflate volume and skew signals.
Out-of-Bounds Prints
Flags prices outside expected bounds before they reach a backtest.
Who benefits
Built for every team that depends on market data.
TrueTick answers the data quality and observability questions that consumers, producers, and owners ask every day.
Data Consumers
Is the data complete, correct, and available when I need it?
Confidence that every dataset is timely, complete, and trustworthy. Find what exists in OneTick, understand its schema, and use it without relying on data engineering to answer basic quality questions.
Data Producers
Is the pipeline performing correctly before users find out it isn’t?
Real-time latency checks, SLO tracking, and intraday monitoring across every database you maintain. Know about a failure the moment it happens — not when a trader reports it downstream.
Data Owners
Are SLOs being met and can I prove it?
Dashboards, daily reports, and proactive alerts give stakeholders the visibility to govern market data with confidence — and documented evidence that quality expectations are being met over time.