Why Subsec —
Autonomous Agent Data Quality
A practical narrative of the problem, why legacy tools struggle, and how Subsec’s autonomous agent becomes the impact player for data quality.
What is Autonomous Agent Data Quality?
Autonomous Data Quality Agents (ADQA) represent a new category of data management solutions that apply AI-driven, self-learning agents to monitor, detect, and remediate data quality issues across modern ecosystems.
Unlike traditional rule-based platforms, ADQAs run in a serverless, event-driven model, scaling automatically with workloads and adapting in real time to schema drift, anomalies, and evolving needs.
ADQAs are becoming a foundational layer for enterprises adopting cloud-native, multi-cloud, streaming, and AI/ML, transforming data quality from a cost center into a strategic business advantage.
Bad data is the silent killer of AI & analytics
Data scientists spend ~80% of their time cleaning and reconciling data. Gartner estimates an average of US$12.9M annual loss per enterprise due to poor data quality. And 1 in 3 leaders don’t trust the data they use to make decisions.
Without trusted data, every model, insight, and decision is at risk. And yet, the legacy tools we’ve relied on—built for batch processing, static rules, and on-prem systems— simply can’t keep up with today’s real-time, cloud-native world.
Why traditional tools fall short
Legacy data quality platforms were built for yesterday’s challenges. They’re rigid and rule-based (every schema change needs manual updates), infra-heavy (long setups and maintenance), and batch-first (a poor fit for real-time and unstructured data). You pay for licenses and idle infrastructure whether you use them or not.
In short, they weren’t designed for the scale, speed, or complexity of today’s data world.
Rigid rule-based design
Manual checks to create, maintain, and update—teams slow down.
Infrastructure overhead
Setup, maintenance, scaling, licenses, and idle infra.
Limited adaptability
Schema drift, streaming, and unstructured data exceed their design.
Batch-first orientation
Real-time AI needs continuous monitoring, not periodic validation.
High cost-to-value
Perpetual licensing + idle infra = weak ROI.
Enter the impact player
This is where our autonomous agent comes in — the impact player for data quality. It doesn’t wait for rules or manual oversight; it monitors and fixes in real time, learns from every dataset, and adapts to schema drift, anomalies, and new sources automatically.
Because it’s serverless and pay-per-use, it scales seamlessly without the overhead of traditional platforms. It’s not just a tool — it’s an always-on teammate that raises the game.
Key benefits for the team
Our solution behaves like a teammate, not just another tool: engineers avoid firefighting and rule maintenance, data scientists spend more time modelling than cleaning, analysts get consistent, trustworthy data, and the enterprise scales faster with lower risk.
Deliver faster, more accurate AI models by eliminating data prep bottlenecks.
Reduce risk and compliance exposure via continuous monitoring and automated remediation.
Free up data engineers and scientists to focus on innovation, not maintenance.
Achieve agility at scale with a solution that evolves with cloud ecosystems and AI advances.
Source: Harvard Business Review — “If your data is bad, machine learning is useless.”
Months of prep for each month of modelling
Time spent on prep/cleaning
Say prep/cleaning is least enjoyable
From cost center to business advantage
For years, data quality meant expensive licenses, idle infrastructure, and manual firefighting. Our serverless, pay-per-use model flips that equation—continuous monitoring and real-time remediation reduce risk, speed time-to-insight, and turn data quality into measurable ROI—with no wasted spend.
By freeing teams to focus on innovation instead of maintenance, the enterprise moves faster, cuts risk, and scales effortlessly across modern cloud ecosystems.
Cloud-native platform
Serverless—no servers or databases to manage; zero infra overhead.
Low maintenance
Implement in minutes; automatic upgrades without DBAs or DevOps.
No-code fixes (NLTs)
Natural Language Transformations generate fixes automatically.
Composable architecture
Rapid, secure change aligned with business and AI needs.
The future vision
We’re building a foundation for the next decade of data. Subsec’s autonomous agent evolves with advances in AI and cloud, embedding trust directly into the data fabric so every model, dashboard, and decision runs on reliable information by default—across real-time pipelines, unstructured data, and multi-cloud.
- Always-on teammate — monitors, detects, resolves in real time.
- Self-learning contributor — improves with every dataset and fix.
- Versatile operator — handles structured & unstructured data.
- Amplifier for the team — reduces repetitive checks.
- Fast starter — quick to deploy; native cloud integrations.
- Cost-conscious — serverless, pay-per-use.
- Adaptive innovator — resilient to schema drift, anomalies, and evolves with AI/ML.
- Trusted partner — boosts governance & compliance.
In short, Subsec isn’t just another tool — it’s the MVP of data quality, embedding trust by default and consistently raising the standard of every model, dashboard, and decision.
How Subsec compares
Today’s data quality market is dominated by legacy, rule-based, infrastructure-heavy platforms. Cloud ETL tools move data, but their checks are shallow and manual. Subsec’s autonomous agent sits in a category of its own—serverless, AI-driven, and adaptive—combining intelligence and scalability in a single platform.
| Capability | Legacy Data Quality (Informatica / Talend) | Cloud ETL (Fivetran, Matillion) | Subsec Autonomous Agent |
|---|---|---|---|
| Infrastructure | Heavy, on-prem/cloud VMs | Light; orchestration required | Serverless, zero overhead |
| Approach | Rule-based; manual updates | Schema sync; limited checks | AI-driven, self-learning, adaptive |
| Real-time | Limited / batch-first | Basic; streaming optional | Always-on, streaming + batch |
| Cost model | High license + infra | Subscription | Pay-per-use, scale with demand |
| Value | Compliance baseline | Convenience for data movement | Competitive advantage via trusted data |
The result: lower overhead, faster time-to-trust, and reliable data by default—across batch and streaming.
Every team needs an impact player. For your data team, that’s Subsec’s autonomous agent—always-on, adaptive, and ready to turn data quality into a competitive advantage.