Financial regulation such as BCBS 239, FSCS and FATCA require banks to establish data quality management, including data profiling, data provenance or lineage, monitoring, reporting and escalation procedures. Specifically, conforming to BCBS 239 principles 3, 4, & 5 requires the need for a continuous data quality management solution.

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BCBS 239 Principles Datactics Solution
Principle 3: Accuracy and Integrity

A bank should be able to generate accurate and reliable risk data to meet normal and stress/crisis reporting accuracy requirements. Data should be aggregated on a largely automated basis so as to minimise the probability of errors

Datactics RegMetrics & Continuous Data Quality Management
Principle 4: Completeness

A bank should be able to capture and aggregate all material risk data across the banking group. Data should be available by business line, legal entity, asset type, industry, region and other groupings, as relevant for the risk in question, that permit identifying and reporting risk exposures, concentrations and emerging risks.

Datactics RegMetrics & Continuous Data Quality Management
Principle 5: Timeliness

A bank should be able to generate aggregate and up-to-date risk data in a timely manner while also meeting the principles relating to accuracy and integrity, completeness and adaptability. The precise timing will depend upon the nature and potential volatility of the risk being measured as well as its criticality to the overall risk profile of the bank. The precise timing will also depend on the bank-specific frequency requirements for risk management reporting, under both normal and stress/crisis situations, set based on the characteristics and overall risk profile of the bank.

Datactics RegMetrics & Continuous Data Quality Management


Our Solution

Datactics RegMetrics & Continuous Data Quality Management provides the tools you need to address regulatory  data quality demands for BCBS 239 and beyond:

Data Aggregation Manage ETL consolidation and standardization of data from multiple sources

Generate metadata for transparent data lineage

Data Quality Measurement Benchmark quality of existing data with built in flexible data profiling
Data Quality Improvement Actively monitor quality of new data

Apply powerful cleansing, de-duplication of data

Manage manual correction of data with built in delegation workflows

Reporting Produce live data quality metrics dashboards in popular BI visualization tools

Supports DCAM data quality dimensions and any other assessment criteria

Facilitates reporting to business & regulators e.g. One Sum X

Report across all taxonomies & asset classes

Flexibility Manage and re-use rules for data quality criteria without coding

Data Analyst / SME-friendly interface

Transparency Enhance data  with metadata for transparent provenance

Rapidly identify & review records failing data quality criteria

’4-eyes’ validation controls


The Benefits

Ease of Deployment

We have a track record of rapid deployment – integrate your systems with Datactics data quality software in a matter of weeks. Our solution is portable – implement data quality management in one area of the enterprise and then easily extend to others.

Future Proof

Investment in flexible data quality technology that can accommodate future changes. Data analysts can very easily add new rules or build new projects for any kind of data- without maintaining code

Holistic Data Quality

It makes sense to look at all regulation and asset classes together. In addition to BCBS 239 our solution can satisfy the data quality requirements of other regulations, directives and asset classes.


Data Quality Metrics Graph, Diagram Regulatory Metrics, quality of service

Holistic Data Quality

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