Keep Data Quality High Across Many Systems

Only 2 of 31 global banks fully met Basel's risk data rules in 2023. A data quality framework fixes errors where one system feeds the next.

Alexander Kiel

Business Analyst · Banking & Wealth Management

10.07.2025 · 6 MIN READ · PROCESS OPTIMISATION

ON THIS PAGE

1. Scope the Data Quality Framework

2. Agree One Definition

3. Name an Owner and a Golden Source

4. Measure Against Clear Rules

5. Reconcile Between Systems

6. Fix It at the Source

Sample case: Citigroup

In short

FAQ

Poor data is expensive. Gartner estimates that it costs organisations at least $12.9 million a year on average. Thomas Redman and his colleagues asked 75 executives to check 100 recent records from their own departments. They found that on average 47% of newly created records had at least one critical error. Only 3% of the scores were acceptable. A data quality framework turns that cost into tasks with owners and numbers.

Banks are no exception. In 2023, the Basel Committee reviewed 31 global systemically important banks. It found only two banks fully compliant with all of its risk data principles. It linked many of the weaknesses to scattered IT systems and old platforms.

The problem rarely sits in one system. It sits between them. Does your bank know which system holds the right address, risk profile or domicile of a client?

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1 · Scope the Data Quality Framework

Which twenty data fields would cause the most damage in your bank if they were wrong?

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List the reports and decisions that matter most

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Trace each one back to the data fields it uses

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Mark these fields as critical data elements

Keep in mind: A list of hundreds of critical data elements is no longer a priority list. Start small and add to it later.

FULL STEP

A bank holds millions of data fields across core banking, CRM, portfolio management, payments and reporting systems. Not all of them need the same care. Start with the few that drive regulatory reports, risk figures and client outcomes. Examples are client identifiers, domicile, risk profile, positions and prices.

Supervisors expect this focus. The ECB guide on risk data asks that the critical data elements underlying the key risk indicators should also be explicitly identified. The Basel Committee also urges banks to ensure sound data quality as the basis for digitalisation projects, not as an afterthought.

2 · Agree One Definition

Would three departments in your bank give the same definition of an active client?

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Write one business definition for each critical data element

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Record allowed values, formats and the system fields that map to it

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Publish the glossary where every team can find it

Keep in mind: A definition agreed only by IT is not enough. The business teams who use the data must sign it off.

FULL STEP

Systems often use the same word for different things. "Client" can mean a person in CRM, a contract in core banking and a household in portfolio management. "Domicile" can mean tax residence in one system and postal address in another. Until the definitions match, the data cannot match.

The Basel Committee's principles state that a bank should have a "dictionary" of the concepts used, such that data is defined consistently across an organisation. Ten years later, its 2023 review still found that some banks lack a common taxonomy and complete data lineage.

3 · Name an Owner and a Golden Source

When two systems disagree on a client's domicile, who in your bank decides?

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Name one owner for each critical data element

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Pick one golden source system for each element

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Stop manual edits of copied values in other systems

Keep in mind: Ownership sits with the business, not with IT. IT runs the systems, but the business knows what the data should say.

FULL STEP

When a value differs between systems, someone must decide which one is right. For each critical data element, name one business owner and one golden source. That is the system where the value is created and kept up to date. Every other system copies from there. In the projects I have worked on, systems disagreed most on client static data such as domicile or risk profile.

The ECB expects data owners responsible for key risk indicators and critical data elements across the whole process. Thomas Redman describes the roles as data creators and data customers. Customers should tell creators what they need. My article on reference data in a bank migration shows how one golden source per area works in practice.

4 · Measure Against Clear Rules

Could you show your management the error rate of your ten most important data fields today?

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Write measurable rules for each dimension and data element

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Set a tolerance and an owner for each rule

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Report results every month, with trends rather than snapshots

Keep in mind: A quality score without a limit invites debate, not action. Agree what "good enough" means before the first report.

FULL STEP

What is not measured is not managed. Turn each definition into rules a system can check. The UK Government Data Quality Hub uses six dimensions: accuracy, completeness, uniqueness, consistency, timeliness and validity. Consistency matters most when the same data lives in several systems.

Yet Gartner reports that 59% of organisations do not measure data quality. The ECB asks banks for data quality indicators with tolerance levels and robust correction processes. A simple scorecard for each data element makes the results visible to management.

5 · Reconcile Between Systems

How many manual spreadsheets sit between your source systems and your regulatory reports?

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Reconcile critical data between the golden source and each copy

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Document the path of the data from capture to report

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Track open breaks with an owner and a due date

Keep in mind: Spreadsheets used to fix reports are systems too. Include them when you trace and reconcile the data.

FULL STEP

At every interface between systems, data can get lost, delayed or changed. Compare the golden source with every system that copies from it: record counts, key values and totals. Look into each break and close it, not only the large ones.

The Basel Committee's principles state that risk data should be reconciled with bank's sources, including accounting data where appropriate. The ECB adds that banks should keep complete and up-to-date data lineages. Each figure can then be traced back to where it came from.

6 · Fix It at the Source

Which errors does your team correct every month without ever asking where they come from?

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Find the root cause of each data break that keeps coming back

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Add checks where data is entered

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Stop typing the same data into two systems

Keep in mind: A fix at the source often needs a change in another department's process. Agree it with that owner, not only in your own team.

FULL STEP

Cleaning data further down the line treats the symptom. The same error comes back with the next delivery. Redman argues that most effort goes into cleaning up errors rather than finding and fixing the cause. He calls for creating data correctly the first time.

The ECB also expects a root cause analysis. It wants clear processes to fix and escalate data quality issues. Many root causes sit where the data is first entered: free text fields, missing checks or data typed in twice. My article on mapping the process before you change it shows how to trace an error back to the step where it starts.

What's So Dangerous About Bad Data? | Georgina Sturge (Data Consultant @ Migration Observatory)

SAMPLE CASE

Citigroup

In October 2020, the US Office of the Comptroller of the Currency fined Citibank $400 million for deficiencies in risk management, data governance and internal controls. The bank agreed to a consent order and a plan to fix the issues.

Four years later, the Federal Reserve found that Citigroup had made insufficient progress remediating its problems with data quality management. In July 2024, the Fed and the OCC imposed further penalties of about $135.6 million. CEO Jane Fraser pointed to "fragmented tech platforms" and decades of underinvestment in parts of the bank's infrastructure. CFO Mark Mason said the bank had fallen short in data quality management, particularly in regulatory reporting.

Citi invested more than $12 billion in technology in 2023. Since then it has focused on simplifying its systems: about 2,000 legacy applications were decommissioned over three years. Another 130 were retired or replaced in the first quarter of 2025.

Key takeaway: Citi's problem was not a lack of investment. It had many systems and no consistent control of the data between them. Fixing it meant simplifying the systems, not only cleaning the data.

"We fell behind in data, particularly regarding regulatory reporting."

Jane Fraser (CEO @ Citi)

In Short

Good data across many systems does not come from a single tool or a one-off clean-up. It comes from clear definitions, named owners, one golden source per data element and regular checks between systems.

Start with your critical data. Measure it against agreed rules. Fix errors where they start. Banks that do this spend less time correcting reports. Management, clients and supervisors can then trust the figures.

Frequently Asked Questions

What is a golden source of data?

A golden source is the one system where a data element is created and kept up to date, such as the client's domicile in core banking. All other systems copy the value from there instead of keeping their own version. When values differ, the golden source wins. Each golden source needs a business owner who is responsible for the quality of its data.

What is the difference between data quality and data governance?

Data governance sets the rules: who owns which data, how it is defined and which system is the golden source. Data quality measures how well the data meets those rules, for example in accuracy, completeness and consistency. Governance without measurement stays on paper. Measurement without governance finds errors but leaves no one responsible for fixing them at the source.

Do we need a new system to improve data quality?

Usually not as a first step. Most gains come from agreed definitions, clear ownership, automatic checks and fixing errors where data is entered. A data quality tool or master data system can help once these basics are in place. Without them, a new system often becomes one more source of inconsistent data.