Rayterton helps financial institutions and large enterprises turn data into a governed, trusted asset. This consulting suite combines governance frameworks, metadata and lineage, data quality engineering, and privacy controls so that AI, analytics, and regulatory reporting are all built on a reliable foundation.
This consulting offer is designed for institutions that rely on accurate and explainable data to support credit decisions, regulatory submissions, AI models, and executive reporting.
Institutions that are scaling AI or digital risk initiatives, preparing for new data privacy regulation, modernising their data platform, or needing stronger data controls for supervisors and auditors usually benefit the most from this program.
The engagement is modular. You can start with rapid gap assessment and roadmap, then extend into implementation of control frameworks, tooling, and operating processes.
Design or refine enterprise data governance framework, including roles, decision rights, stewardship model, and data domain ownership. Deliverables usually include policy set, standard templates, and governance committee charters.
Establish practical lineage from source systems to reports and AI models, supported by metadata standards and catalog structure. This helps teams understand where data comes from, how it is transformed, and who is accountable.
Define critical data elements, quality dimensions, and rule sets for key domains such as customers, exposures, transactions, and reference data. Implement dashboards for quality scorecards, issue workflow, and remediation tracking.
Map personal data flows, classify sensitive attributes, and design controls for consent, access, retention, and anonymisation. Aligns with local data protection law and sector regulations while remaining workable for business teams.
Identify critical master data entities such as parties, products, accounts, and organisational structures. Develop golden record strategy, matching and survivorship rules, and integration pattern with core banking and risk systems.
Ensure that AI governance and regulatory reports are linked to governed data sets. Provide lineage documentation, quality evidence, and data control checklists that can be reused in model validation and supervisory discussions.
Rayterton normally runs this as a focused program with clear milestones, while keeping space for quick wins that can be implemented during the engagement.
Share your current data landscape, key regulations you must comply with, and the initiatives that depend on trusted data. Rayterton will design a tailored Data Governance and Data Quality Management program that your leadership team can review and refine together.