Beyond Data Quality: Turn Data Risk into Controls You Can Prove
Finding a data issue is easy. Proving it’s controlled is not.
Banks across the US can see their critical data. The next exam will ask whether they can prove it’s under control.
6 min read
For CDOs, Heads of Data Governance & Data Quality, Operational Risk, Compliance & Internal Audit
Executive summary
Banks in the US have spent years on data catalogs, automated data lineage and data quality monitoring. Most can now find their critical data and flag when it breaks. Proving the risk is controlled is a different job: control designs, approvals and evidence still live in spreadsheets, scripts and tickets. This article shows where that gap opens, what a governed data control actually contains, and how Alex Solutions anchors every control to the metadata and lineage it depends on.
In this article
So, now you can identify your data risk. But can you prove it’s controlled?
Ask a governance lead where the bank’s critical data lives and you’ll get a confident answer. Ask how long it would take to prove a critical data control worked last month, and the room goes quiet.
That silence is the gap. Banks have invested heavily in seeing their data. Controlling it is still manual work.
The Data Risk Gap: Visibility Without Assurance
Most banks already run a data catalog, a data lineage tool and some form of data quality profiling. Together, they’re very good at discovering risk. They surface missing customer identities, duplicate payments and reports that don’t reconcile.
What happens next is far less mature. The controls that mitigate those risks are designed in spreadsheets, approved over email, implemented as SQL scripts and evidenced through tickets. Each step works. None of them connect.
It’s the pattern Alex Solutions hears most often from data governance and risk teams. You know about the risk. You can’t easily prove it’s managed.
Where Data Quality and Compliance Programs Break Down
Alex Solutions sees the same failure points again and again. They’re rarely technical. They sit in the handoffs between teams and tools.
1
Fragmented ownership
Control duties span technology, operations, governance and risk. When a control fails, no single owner can confirm its scope.
2
Manual control design
Findings get rewritten into rules, thresholds and workflows one at a time. Coverage lags the data estate, and gaps stay open.
3
Disconnected end-to-end view
Upstream issues rarely connect to downstream reports and decisions. The impact surfaces after a report is filed.
4
Evidence gaps
Approvals, results and remediation never form one record. Every exam becomes a manual reconstruction.
A Passing Check Is Not Proof of Control
A data quality tool can tell you a rule failed. It may not tell you whether that rule addresses the right risk, who approved it, whether its scope still holds or whether anyone fixed the failure.
A rule can pass every night and still miss the risk. Effective control needs the right scope, an accountable owner, approved thresholds and a record of every failure. It’s one reason analysts such as Gartner have pushed the market toward active, connected metadata rather than static inventories.
What a Governed Data Control Contains
A data control is a governed activity designed to prevent, detect or correct a data-related risk. It protects a business outcome, not a data field. That’s why it can’t be reduced to a single rule.
01
Risk
What could go wrong?
02
Control
What prevents, detects or corrects it?
03
Rule
What logic or threshold defines it?
04
Test
When does the rule run against data?
05
Evidence
What proves it was approved, run and managed?
Keep those five objects separate but connected, and an auditor can follow the chain from risk to proof. Collapse them into one spreadsheet row, and the chain breaks the moment someone changes a pipeline. At Alex Solutions, we treat that separation as the starting point for every control.
What American Regulators Expect
No single regulation requires data controls software. Together, though, supervisory expectations in the US point one way. OCC Heightened Standards apply to OCC-supervised banks with $50B or more in assets, and Call Report and FR Y-9C filings must be accurate.
FFIEC handbooks address data integrity and IT controls, while SR 26-2 covers the data that feeds models. Many banks also benchmark governance maturity against BCBS 239, even though it isn’t a general US requirement. The common thread is simple. Supervisors expect banks to show that critical data is governed, and to evidence it.
The Alex Approach: Metadata Orchestration Behind Every Control
Alex’s Enterprise Data Operations Platform is the metadata and lineage foundation for a connected data control operating model. Its semantic inference engine links critical data elements to owners, transformations, dependencies and quality signals in one Enterprise Knowledge Graph.
That context is what controls have been missing. A control without lineage can’t tell you what breaks downstream. A control without an owner can’t tell you who fixes it.
Available today
Discover
Critical assets, dependencies, owners and quality signals
In development
Propose
Explainable control candidates
In development
Approve
Accountable owners sign off
In development
Bind
Connect to SQL, DQ tools or processes
In development
Assure
One evidence trail
Control lifecycle. Solid = available today. Dashed = product direction, in development.
Discover is available today through Alex’s automated data lineage, cataloguing, classification and profiling. Alex Solutions is now building the remaining stages, so proposal, approval, execution and assurance share one picture instead of four tools.
Design principle
AI proposes. People approve. Execution engines enforce. Alex preserves the evidence.
Why Governed Controls Come Before Trusted AI
This is agentic data operations with guardrails. Agents can draft control candidates faster than any team, but without approved scope, accountable owners and retained evidence, those drafts become a fresh source of risk.
The same connected metadata that proves a regulatory report is controlled also proves an AI model’s inputs deserve trust. Data security works the same way. Classification and masking only count when you can show they ran. You can’t govern AI on data you can’t evidence.
Real-World Example: A Reconciliation Break, Traced End to End
Picture a mid-sized regional bank operating across several US states. It runs core banking, an enterprise data warehouse, cloud analytics and regulatory reporting on a mix of legacy and modern platforms.
A reconciliation break appears between a source transaction system and a downstream regulatory reporting dataset. Data Engineering finds the technical cause. Governance and Risk then have to rebuild the data path, control definition, ownership and remediation history from scratch.
Sound familiar? The Alex Solutions case study walks through how a bank like this could move from manual data controls to continuous assurance, and what changes once data, risk and audit share one record.
Case study · Regional banking
From Manual Data Controls to Continuous Assurance
See the phased program, the before-and-after operating model and the single record that ties critical data elements to owners, controls, exceptions and evidence.
Frequently Asked Questions
What is a data control in banking?
A data control is a governed activity that prevents, detects or corrects a data-related risk, such as duplicate payments or incomplete regulatory reporting data. It links a risk to a rule, a test, an accountable owner and evidence that it ran.
Is a data quality rule the same as a data control?
Not necessarily. A data quality rule checks data against logic. A governed control also needs accountability, scope, approval, execution, monitoring and evidence.
How do I prove a data control worked for an audit?
You need the control’s approved design, its execution results, any exceptions and the remediation history, all tied to the critical data it protects. When those live in separate tools, every audit means rebuilding that record manually.
Does automated data lineage help with regulatory compliance?
Yes. Lineage shows where critical data moves, transforms and lands, so you can trace a reporting field back to its source and see which controls sit along the way.
Does Alex Solutions replace our data quality tools?
No. Alex provides the metadata and lineage foundation that connects controls to critical data and owners. Its product direction connects approved controls to the execution engines banks already run, such as SQL or data quality platforms.
Where Are Your Critical Data Controls Still Manual?
One critical data process, 45 minutes, and a clear view of your control coverage.
