Your data governance framework, built to last
Trusted data starts here — before the migration, before the AI project, before the audit. We design and implement governance structures that make your data reliable, compliant, and business-ready.
Industry relevance
Banking
Critical
(Regulation)
Telco
Critical
(GDPR, NIS2)
Retail
High
IT/Tech
Medium
The IDS approach to data governance
- Framework-first. We design governance structures before any technology is selected — so the tools serve the framework, not the other way around.
- Regulation-aware. Every framework we build is mapped to the regulatory requirements that apply to your sector — banking, telco, insurance.
- Industry-tested. 14+ years delivering data governance in environments where data quality isn’t optional — it’s regulated.
- Business-embedded. We work with data owners, not just IT — because governance only works when the business takes ownership of it.
What a governance engagement covers
- As-is assessment of your data landscape
- Data ownership and stewardship model
- Data quality KPIs and monitoring thresholds
- Metadata standards and business glossary
- Regulatory gap analysis and remediation plan
- 12-month governance roadmap
Your data governance framework, built to last
Master Data Management (MDM)
Standardised master data domains, ownership models and access frameworks across the enterprise.
Data Quality Management (DQM)
Quality indicators, monitoring thresholds, and remediation workflows for high-impact business data.
Metadata Management
Defining, capturing, and maintaining information about your data — so it stays useful over time.
Data Modelling
Business, logical and physical models; naming conventions, historisation typologies, surrogation methodology.
Enterprise IT Process Design
AS-IS / TO-BE process mapping, SLAs, roles, data provisioning and change management documentation.
Regulatory Reporting Support
Data structures and governance frameworks for IFRS, Basel, Solvency II and national regulatory requirements.
Why data governance fails — and what it costs
Data quality issues surface mid-migration
Teams discover critical inconsistencies only after the project has started — causing delays, rework, and budget overruns.
Regulatory audits find gaps your team didn't know existed
DORA, GDPR and Basel requirements demand documented governance. Without frameworks in place, audits become crises.
AI projects stall because no one trusts the data
Models trained on ungoverned data produce unreliable outputs.
Governance is the foundation every AI initiative needs first.
Technologies we use
Regulatory frameworks we work with
Our governance frameworks are designed to satisfy the requirements of the regulations that matter most to banking, insurance, and telco.
DORA GDPR Basel IV Solvency II NIS2 IFRS 9 AML / KYC
Where this domain matters most
Banking & Finance
Primary trigger: DORA & Basel IV compliance
- Pre-migration data governance assessments
- Credit risk and AML data quality frameworks
- Regulatory reporting structure and audit readiness
- Data governance for AI model validation
Telco
Primary trigger: GDPR & NIS2 compliance
- Subscriber data quality and master data management
- NIS2 data security and governance readiness
- Network data lineage and metadata standards
- Governance foundation for churn and NBO models
Retail
- Customer data standardisation for personalisation
- Supplier & product master data management
Insurance
- Solvency II reporting data quality frameworks
- Policy and claims master data governance
Case studies
Start with a governance assessment
We'll map your current state, identify the regulatory gaps, and tell you exactly what needs to happen before your next data initiative — whether that's a migration, an AI project, or an audit.
- 30-min discovery call — no commitment
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We scope the assessment and confirm the approach
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Assessment delivered with findings and roadmap
Not ready for a call? Download our Data Governance Readiness Checklist — 24 questions to assess where you stand.