Data Quality Management & Assurance: Tool To Avoid Data Quality Issues

The checks you'd expect. The intelligence you wouldn't.

Freshness

Volume

Schema drift

Distribution

Custom SQL checks

Reconciliation

Lineage-aware impact

Quality SLAs

What's different

Watch the data. Diagnose the cause. ^{Resolve it.}

Other tools stop at the alert. Unravel correlates, explains, and fixes — autonomously when you let it.

Arvix in Action

One alert. One workload view. ^{One platform deciding what to do.}

A quality alert today kicks off a 40-minute scavenger hunt. With Unravel, the alert opens an incident view that already has the answer.

——— Why this matters now

Your AI is only as good as the ^{data feeding it.}

Stale training data, drifting features, RAG sources gone light — these don't break loudly. They quietly degrade the model until the answer is wrong. Unravel watches the data behind every AI workload.

Silent failure mode

Stale training data

Feature pipeline misses a refresh. Training runs on yesterday's reality. Model ships, accuracy drops, no one paged.

Silent failure mode

RAG retrieval gaps

Embedding source volume drops overnight. Chatbot answers from incomplete context. Customers notice before you do.

Silent failure mode

Distribution drift

Input distribution shifts a few percent a week. Inference quality decays smoothly. By the time a metric moves, you've lost a quarter.

Arvix

Watches every table, check, and pipeline feeding your AI — and flags the moment data trust breaks. Not three weeks later when the metric finally moves.

Bring your own DQ

Keep what works. ^{Make it smarter.}

Standardized on Great Expectations? Heavy in Informatica? dbt tests, Soda, custom SQL? All of it flows in.

Informatica

Great Expectations

dbt tests

Soda

Custom SQL

One incident. Every signal. ^{One platform deciding what to do about it.}

Where pure-play DQ stops

They watch the data. ^{They don't see the system around it.}

The cause of a quality incident is almost never in the table. It's upstream — in the code, the compute, the config that ran the night before.

DQ-only tools

No performance correlation

They see the row drop. They can't see the failed Spark stage or OOM that caused it. ^{Unravel does.}

DQ-only tools

No cost correlation

They tell you the data's wrong. Not what the bad pipeline cost — or what the backfill will. ^{Unravel quantifies both.}

DQ-only tools

Alert. Don't fix.

They open Slack threads and Jira tickets.^{Arvix proposes and applies the fix}— code, config, or compute.

Specialized tools

Single-platform coverage

Snowflake-only. Warehouse-only. ^{Unravel covers Databricks, Snowflake, and BigQuery} in one platform.

——— In practice

Three incidents. ^{Same playbook.}

The same correlated diagnosis works whether the broken thing is a finance dashboard or an AI training pipeline.

——— Customer story

A global airline took control of its data platform costs

"We've gotten a ^{10x return} from Unravel, with full payback in less than six months. Everything else we evaluated just had dashboards and insights. Unravel actually delivers measurable cost and performance improvements."

SVP, Data Platforms, Global Airline

——— Customer story

A small finops team used automation to unlock over ^{$2m in year‑one savings}

"With Unravel, our small FinOps team delivered more than ^{$2 million of realized savings in Year 1}. Unravel’s automation enabled us to work with our data engineering teams to actually implement needed improvements without the friction."

Senior Director, Data FinOps, Global Consumer Health Company

Person holding a pink stethoscope shaped like a heart with manicured nails and rings.

——— Customer story

Logistics giant optimizes cloud data costs up front at speed & scale

“Unravel cut our cloud data ^{costs by 70% in six months}—and kept them down with automated 360° cost visibility, prescriptive guidance, and guardrails for its 3,000 data engineers across the globe.”

Head of Data Platform Optimization, Top 5 Global Logistics Company

Read the Story

Aerial view of a container ship sailing through deep blue water with visible wake trails behind.

Recommended resources

Quality alerts tell you something broke. Then an engineer hops across five tools to find out why. Unravel lives where the answer is — your code, your compute, your pipelines — and Arvix resolves the incident, not just reports it.

Stale training data

RAG retrieval gaps

Distribution drift

No performance correlation

No cost correlation

Alert. Don't fix.

Single-platform coverage

Three incidents. Same playbook.

"We've gotten a 10x return from Unravel, with full payback in less than six months. Everything else we evaluated just had dashboards and insights. Unravel actually delivers measurable cost and performance improvements."

"With Unravel, our small FinOps team delivered more than $2 million of realized savings in Year 1. Unravel’s automation enabled us to work with our data engineering teams to actually implement needed improvements without the friction."

“Unravel cut our cloud data costs by 70% in six months—and kept them down with automated 360° cost visibility, prescriptive guidance, and guardrails for its 3,000 data engineers across the globe.”

Recommended resources

Unravel Automated Data Quality Datasheet

Data Observability: The Missing Link for Data Teams

Data Observability Datasheet

Why Legacy Observability Tools Don’t Work for Modern Data Stacks

Three incidents. ^{Same playbook.}

"We've gotten a ^{10x return} from Unravel, with full payback in less than six months. Everything else we evaluated just had dashboards and insights. Unravel actually delivers measurable cost and performance improvements."

"With Unravel, our small FinOps team delivered more than ^{$2 million of realized savings in Year 1}. Unravel’s automation enabled us to work with our data engineering teams to actually implement needed improvements without the friction."

“Unravel cut our cloud data ^{costs by 70% in six months}—and kept them down with automated 360° cost visibility, prescriptive guidance, and guardrails for its 3,000 data engineers across the globe.”