Building your first regulatory data warehouse: a 90-day implementation blueprint

1. Start by defining what the regulator needs to see

Do not begin with:

Which data warehouse should we buy?

Begin with:

What should the Commission, Authority or supervisory team be able to understand that it cannot see reliably today?

This is the most important workshop in the program.

Ask executives, supervision teams, licensing, examinations, enforcement, finance and IT to describe the decisions they regularly make and the information required to make them.

The output should be a short list of priority supervisory questions.

Examples:

• How many active regulated entities do we supervise by sector and license type?
• Which entities have applications, filings or regulatory obligations currently overdue?
• Which entities have open examination findings?
• Which entities have been subject to enforcement activity?
• Which individuals hold significant roles across multiple regulated entities?
• What is the complete regulatory history of one entity?
• Which sectors are showing increasing examination, filing or compliance issues?
• What regulatory activities are increasing or decreasing over time?
• Which entities have multiple risk signals across different departments?
• What is the current workload and aging of regulatory cases?
• Which licenses are approaching renewal or expiry?

These questions become the supervisory view.

That view is the business specification for the data program.

It keeps the warehouse connected to actual regulatory outcomes rather than becoming an infrastructure project looking for a use case.

For the wider argument behind this sequence, see the modern supervisory system of record.

2. Build the source-system map before building pipelines

Once the priority questions are clear, identify where the required information currently lives.

A regulator’s initial information landscape may look like this:

Source	Typical information	Questions to answer before integration
E-filing / regulatory portal	Entities, licenses, applications, filings, submissions	Is the data structured? Is there API or database access?
Licensing system	Licenses, status, dates, conditions	Is there one entity record or one record per license?
Examinations	Examination plans, visits, findings, remediation	How are entities identified? Are findings structured?
Enforcement / legal	Cases, allegations, actions, outcomes	What can be shared across supervisory teams?
Document management	Supporting files, correspondence, evidence	Can metadata be extracted separately from document content?
Finance	Fees, payments, receivables	What finance data is relevant to supervision?
Risk analytics	Scores, assessments, indicators	Are definitions stable and documented?
Spreadsheets	Local registers, trackers, reconciliations	Which sheets are operationally authoritative?

For every source, document:

• business owner;
• technical owner or supplier;
• system purpose;
• authoritative records;
• key identifiers;
• approximate data volume;
• update frequency;
• historical depth;
• access method;
• available API, database or export;
• licensing or vendor dependencies;
• known data-quality issues;
• security classification;
• integration constraints.

This exercise usually changes the implementation plan.

A source that looked simple may require a vendor-paid connector.

Another may have a clean API.

A third may contain the same concept in several incompatible forms.

These are not details to discover after the warehouse project begins.

They are the facts that determine scope, cost and sequence.

3. Create a common regulatory information model

Most regulators do not primarily have a storage problem.

They have an identity and meaning problem.

The same regulated organization may appear once per license, once per legislation, under several names, under different system IDs and separately in examinations and enforcement.

If those records are loaded into a warehouse without resolving the meaning, the regulator gets a centralized version of the same fragmentation.

The first warehouse therefore needs a common regulatory model.

At minimum, define these core concepts:

Regulated entity

The durable organization being supervised.

Individual

A person connected to one or more regulated entities, such as a director, beneficial owner, compliance officer or approved person.

License or registration

The regulatory authorization associated with the entity or individual.

Filing or submission

Information delivered to the regulator, such as returns, applications, notifications and regulatory forms.

Regulatory activity

An event performed by or involving the regulator, including review, examination, correspondence, approval, remediation or supervisory meeting.

Examination and finding

A supervisory review and the issues arising from it.

Enforcement case or regulatory action

A formal matter involving investigation, action, sanction or resolution.

Document

An associated evidence item, correspondence or submission document.

4. Decide what the master entity is before anything else

For a first regulatory data warehouse, entity resolution is usually the most important modeling decision.

There should be one durable identity for the regulated entity even if the organization:

• holds several licenses;
• changes its legal name;
• changes ownership;
• appears in several operational systems;
• is supervised under more than one regulatory regime.

A practical model looks like this:

REGULATED ENTITY
    |
    +-- LICENSE / REGISTRATION
    |
    +-- INDIVIDUAL RELATIONSHIPS
    |
    +-- FILINGS / SUBMISSIONS
    |
    +-- REGULATORY ACTIVITIES
    |
    +-- EXAMINATIONS
    |       |
    |       +-- FINDINGS
    |
    +-- ENFORCEMENT CASES
    |
    +-- DOCUMENTS

This becomes the backbone of the supervisory view.

Do not let the structure of the first source system define the enterprise model automatically.

5. Choose the first source strategically

Do not connect every source before delivering the first useful result.

Choose the source that gives you the best combination of:

• structured information;
• known business rules;
• reliable identifiers;
• management value;
• available access;
• low dependency risk.

For many regulators, the strongest starting point is the regulatory portal, e-filing platform or licensing system because it often already contains structured information about entities, licenses, applications, individuals, filings and submissions.

That is enough to establish the first warehouse model and produce useful Commission-level reporting.

The first source is not the final warehouse.

It is the first reliable reporting path.

6. Keep the first architecture simpler than you think

A regulator building its first warehouse does not automatically need a large lakehouse, complex streaming architecture, multi-cloud platform, advanced machine learning or real-time processing.

For many regulators, the first architecture can be straightforward:

SOURCE SYSTEMS
      |
      v
INGESTION / INTEGRATION
      |
      v
STAGING / RAW DATA
      |
      v
TRANSFORMATION + DATA QUALITY
      |
      v
REGULATORY DATA WAREHOUSE
      |
      v
SEMANTIC / REPORTING MODEL
      |
      v
DASHBOARDS + ANALYSIS

The important design properties are:

• repeatable ingestion;
• traceability;
• controlled transformations;
• stable regulatory definitions;
• role-based access;
• historical preservation where required;
• reliable reporting;
• ability to add future sources.

The technology can vary.

A Microsoft-oriented regulator may use Azure SQL, Microsoft Fabric, Azure Data Factory and Power BI.

Another organization may use PostgreSQL, Snowflake, Databricks, AWS, Google Cloud or another approved stack.

The platform matters.

The information model matters more.

See Data, BI & Decision Intelligence.

7. Build the first warehouse around a small number of useful tables

The first warehouse does not need hundreds of tables.

It needs enough structure to support the initial supervisory questions.

A practical starting model might include:

Dimensions

DimEntity
DimIndividual
DimLicense
DimSector
DimLegislation
DimDate
DimRegulatoryActivityType
DimFilingType
DimStatus

Facts

FactLicense
FactFiling
FactApplication
FactRegulatoryActivity
FactExamination
FactFinding
FactEnforcementAction

The exact schema will depend on the regulator.

The key design principle is to separate durable business concepts from transactional activity.

For example:

DimEntity

• EnterpriseEntityKey
• SourceEntityID
• LegalName
• TradingName
• RegistrationNumber
• EntityType
• Sector
• Jurisdiction
• CurrentStatus
• EffectiveFrom
• EffectiveTo

FactFiling

• FilingKey
• EntityKey
• LicenseKey
• FilingTypeKey
• SubmissionDate
• DueDate
• FilingStatus
• ReviewStatus
• DaysLate

This structure allows the regulator to answer questions across time without rebuilding logic separately in every dashboard.

8. Define data-quality rules before exposing the data

A warehouse can make bad data more visible without making it more reliable.

Before publishing a metric, define the rule behind it.

Examples:

Entity quality checks

• missing legal name;
• duplicate registration number;
• multiple active enterprise identities for the same entity;
• invalid entity status.

License quality checks

• missing issue date;
• expiry date before issue date;
• active license attached to inactive entity;
• duplicate active license where prohibited.

Filing quality checks

• filing with no entity;
• overdue status but no due date;
• duplicate submission ID.

Cross-system checks

When additional systems are connected:

• examination entity cannot be matched;
• enforcement matter linked to an unknown entity;
• finance account cannot be mapped to a regulated entity;
• systems disagree on current entity status.

Do not hide these exceptions.

Create a data-quality view so the regulator can improve the underlying information estate over time.

9. Deliver the first reporting product quickly

Do not make the first 90 days about architecture diagrams.

The organization should see a working regulatory outcome.

A useful first executive reporting pack might contain:

Regulated population

• total active regulated entities;
• entities by sector;
• licenses by type;
• new licenses over time;
• expired, suspended or revoked licenses.

Regulatory submissions

• filings due;
• filings received;
• overdue filings;
• filing timeliness by sector;
• review backlog.

Applications

• applications in progress;
• average age;
• stage distribution;
• pending information requests.

Regulatory activity

• supervisory activities by type;
• activity trends;
• workload by department.

Data quality

• unmatched entities;
• duplicate identifiers;
• incomplete master data;
• source-system exceptions.

Every metric should have an owner, definition, source, refresh frequency and business purpose.

10. A practical 90-day delivery plan

Weeks 1–2: define the supervisory view

Activities

• executive and departmental workshops;
• identify 10–20 priority supervisory questions;
• identify first reporting outcomes;
• agree the first source;
• nominate business and technical owners.

Deliverables

• supervisory-question register;
• priority reporting backlog;
• first-release scope;
• governance structure.

Exit condition: everyone agrees what the first release needs to show.

Weeks 2–3: establish the data position

Activities

• source inventory;
• inspect schema and exports;
• identify supplier dependencies;
• review identifiers;
• assess data quality;
• confirm access and security.

Deliverables

• source-system map;
• data-access matrix;
• initial data-quality assessment;
• integration-risk register.

Exit condition: the team knows what information exists and how it can be accessed.

Weeks 3–4: define the regulatory information model

Activities

• define enterprise entity;
• define individual relationships;
• define license and filing concepts;
• agree common statuses;
• map source fields into common definitions.

Deliverables

• conceptual regulatory data model;
• source-to-target mapping;
• data dictionary;
• entity-resolution rules.

Exit condition: the first source can be translated into regulator-wide concepts.

Weeks 4–7: build the first reporting path

Activities

• provision warehouse environment;
• establish secure connectivity;
• ingest first source;
• build staging structures;
• transform into regulatory model;
• implement data-quality rules;
• automate refresh.

Deliverables

• first repeatable data pipeline;
• regulatory warehouse schema;
• transformation logic;
• refresh process;
• exception handling.

Exit condition: warehouse data can be recreated reliably from the source.

Weeks 7–9: build the supervisory reporting layer

Activities

• create measures;
• define semantic model;
• build executive reporting;
• build operational drill-downs;
• validate definitions with users.

Deliverables

• first supervisory dashboard;
• management reporting model;
• metric dictionary;
• UAT results.

Exit condition: supervisors can answer agreed questions from the new environment.

Weeks 9–11: harden governance and operations

Activities

• role-based access;
• monitoring;
• backup/recovery;
• data-quality monitoring;
• runbooks;
• support ownership;
• change control.

Deliverables

• access model;
• support model;
• data-quality dashboard;
• technical documentation;
• release process.

Exit condition: the solution can be operated, not merely demonstrated.

Weeks 11–12: define the expansion roadmap

Activities

• rank remaining source systems;
• define integration dependencies;
• estimate complexity;
• identify vendor actions;
• sequence examinations, enforcement, finance and documents.

Deliverables

• 6–18 month roadmap;
• source-integration plan;
• budget range;
• procurement scope where required.

Exit condition: the regulator knows what to do next and why.

11. The minimum team needed

A first regulatory data warehouse does not require a huge program team.

A practical core team can include:

• executive sponsor;
• regulatory product/service owner;
• business analyst or regulatory SME;
• data architect / senior data engineer;
• BI developer / analyst;
• IT/security representative.

Specialist resources can be added for complex integration, cloud infrastructure, security or vendor coordination.

Someone inside the regulator should own the information capability even if an external team builds parts of it.

12. What not to put into phase one

Unless there is a strong business requirement, postpone:

• real-time streaming;
• AI and machine learning;
• natural-language supervisory assistants;
• enterprise master-data-management platforms;
• unstructured document analytics;
• predictive risk scoring;
• full historical reconstruction of every legacy system;
• dozens of dashboards;
• replacement of source systems.

Get the foundation right first.

Then expand.

13. Security and governance requirements regulators should not postpone

Regulatory data is sensitive.

The first release should already establish the controls later releases will inherit.

Access control

Use role-based access so users see information appropriate to their function.

Data classification

Classify public, internal, confidential supervisory, personal and legally restricted information.

Lineage

For important measures, be able to determine the source system, source field, transformation, calculation and final metric.

Change control

Changes to definitions, mappings, transformations, dashboards and access rules should be documented and tested.

Audit

Track administrative changes and access according to the regulator’s requirements.

Resilience

Define backup, recovery, monitoring, pipeline failure handling and support escalation.

These controls belong in the platform from the beginning.

14. When to buy a platform and when to use what you already have

Many regulators already own enough technology to prove the first reporting path.

A first implementation may be achievable largely within an existing Microsoft, Oracle, PostgreSQL, AWS or other approved estate.

Do not create a procurement exercise simply to buy a fashionable platform.

A new platform is justified when requirements actually demand capabilities the current environment cannot provide efficiently.

Examples include:

• very large data volumes;
• complex semi-structured or unstructured data;
• advanced distributed analytics;
• high-frequency ingestion;
• data science at scale;
• materially different resilience or sovereignty requirements.

For the first warehouse, simplicity has value.

15. How to prepare an RFP without paying bidders to discover the problem

Before issuing an RFP, complete enough of the blueprint to define the problem.

Tell bidders:

What the regulator needs to see

List priority supervisory and management outcomes.

What systems are involved

Provide the known source landscape.

What data access is available

Document APIs, databases, exports and vendor dependencies.

What the common regulatory model looks like

At least at conceptual level.

What has already been proven

If the first reporting path exists, explain it.

What remains uncertain

Identify unknowns explicitly so bidders price them consistently.

What the regulator expects to own

Define expectations for data models, code, pipelines, documentation, credentials, environments, dashboards, deployment processes and knowledge transfer.

This produces better bids because providers compete on how to build the solution rather than on different assumptions about the problem.

16. How Amayztech has applied this approach in practice

The sequence in this blueprint is not theoretical.

It reflects how Amayztech approaches information modernization when the data landscape is fragmented and an organization needs useful reporting before a larger platform is fully defined.

Regulatory foundation: Securities Commission of The Bahamas

Amayztech’s work with the Securities Commission of The Bahamas began with organization-wide business analysis rather than a data-platform procurement.

We worked through processes across the Commission’s departments, including supervision, examinations and enforcement, to understand how regulatory information moved through the organization.

That work informed the design and implementation of the Commission’s CoRI regulatory interface.

The platform established structured regulatory information across areas including:

• regulated entities;
• individuals;
• licenses;
• regulatory forms;
• filings and submissions;
• workflow activity;
• departmental reporting.

A particularly important step was moving away from siloed records created separately under different legislative frameworks.

Amayztech designed a consolidated entity and individual data model so the regulator could work from durable master records rather than repeatedly recreating the same organization in separate regulatory contexts.

That matters for a future warehouse because the hardest part of cross-system reporting is often not moving data.

It is knowing that two records represent the same entity and that common regulatory concepts have consistent meaning.

CoRI therefore provides something a regulator needs before broader data consolidation: a structured, understood source with clear entity, license, filing and activity concepts.

It creates a strong first reporting path without pretending that the source system itself must become the final enterprise warehouse.

The sequence is:

UNDERSTAND THE REGULATORY VIEW
        |
        v
STRUCTURE CORE REGULATORY DATA
        |
        v
ESTABLISH COMMON ENTITY DEFINITIONS
        |
        v
PROVE REPORTING FROM A CONTROLLED SOURCE
        |
        v
EXPAND INTO THE WIDER INFORMATION ESTATE

Warehouse delivery: from fragmented operational data to management visibility

Amayztech has also applied the warehouse side of this pattern in operational data programs outside financial regulation.

In a U.S. healthcare environment, our team has led data-warehouse and analytics work integrating operational EHR data through Azure infrastructure and Azure SQL into Power BI.

The purpose was not simply to centralize data.

It was to combine workflow automation, data engineering and management reporting so operating teams could move from fragmented manual analysis to consolidated visibility across claims, denials, rejections and financial performance.

The implementation pattern is directly transferable:

OPERATIONAL SOURCES
        |
        v
CONTROLLED INGESTION
        |
        v
COMMON DATA MODEL
        |
        v
WAREHOUSE
        |
        v
MANAGEMENT / OPERATIONAL REPORTING

The regulatory model is different.

The implementation discipline is the same.

17. The regulator’s warehouse-readiness checklist

Business

• We have identified the first 10–20 supervisory questions.
• We know who owns the reporting outcome.
• We have agreed which departments participate in phase one.
• We know what “useful in 90 days” means.

Data

• We have identified the first source system.
• We know how to access it.
• We understand its identifiers.
• We know its obvious data-quality issues.
• We know which records are authoritative.

Model

• We have defined the enterprise regulated entity.
• We have defined licenses/registrations.
• We have defined individuals and relationships.
• We have defined filings/submissions.
• We have agreed core statuses and terminology.

Technology

• We know the approved hosting environment.
• We know what database/warehouse options are already available.
• We know how reporting will be delivered.
• We know the identity/access model.
• We have an integration method for the first source.

Governance

• Business owners approve metric definitions.
• Access requirements are documented.
• Sensitive data is classified.
• Data-quality exceptions will be monitored.
• Changes will be controlled.

Operations

• Someone will own the service after go-live.
• Pipeline failures will be visible.
• Backup and recovery are defined.
• Documentation will remain with the regulator.
• The regulator can add new sources without starting again.

If several of these answers are missing, that is not a reason to delay indefinitely.

It tells you what the first discovery phase needs to resolve.

18. What phase two should look like

Once the first reporting path is stable, expand according to business value.

A sensible sequence may be:

PHASE 1
Regulatory portal / licensing
        |
        v
PHASE 2
Examinations
        |
        v
PHASE 3
Enforcement / legal
        |
        v
PHASE 4
Document metadata + finance
        |
        v
PHASE 5
Risk analytics + cross-system supervisory views

Do not integrate a system simply because it exists.

Add it because it unlocks a supervisory question that matters.

19. When the warehouse becomes a supervisory platform

A data warehouse is a foundation.

Over time, it can support:

• consolidated entity profiles;
• supervisory risk indicators;
• cross-department case visibility;
• examination planning;
• regulatory obligations monitoring;
• trend analysis;
• entity relationship analysis;
• natural-language information retrieval;
• anomaly detection;
• AI-assisted supervisory review.

But those capabilities become trustworthy only when the regulator can answer basic questions first:

• Which entity is this?
• Where did this data come from?
• What does this status mean?
• Which system owns the record?
• When was it last updated?
• Who is allowed to see it?
• How was this measure calculated?

That is why the first data warehouse matters.

It establishes the information discipline that advanced SupTech capabilities depend on.

20. The practical decision

If your regulator does not yet have a data warehouse, do not wait until every data problem is solved.

And do not begin by buying the largest platform you can afford.

Begin with one question:

What should we be able to see about our regulated population that we cannot see reliably today?

Then:

1. define the supervisory view;
2. map the source systems;
3. define the common regulatory model;
4. choose one strong source;
5. build the first repeatable pipeline;
6. create the first warehouse model;
7. deliver useful reporting;
8. establish governance and support;
9. expand source by source.

The first warehouse should make the organization more informed, not more technologically complicated.

That is the standard to aim for.

Frequently asked questions

Does a small financial regulator need a data warehouse?

Not every regulator needs a large enterprise data platform, but a warehouse or governed analytical store becomes valuable when information is spread across several operational systems and management cannot reliably obtain a consolidated regulatory view. The architecture can start small and expand as additional sources and reporting needs are added.

Should we use a data warehouse or a data lakehouse?

For a first regulatory reporting implementation, a conventional relational warehouse may be sufficient if most priority information is structured. A lakehouse can become useful when the regulator needs large volumes, semi-structured or unstructured data, advanced analytics or broader data-science workloads.

Which system should we integrate first?

Usually the strongest first source contains structured regulatory master data such as entities, licenses, applications, filings or submissions. The exact choice depends on data quality, accessibility, business value and control of the information.

How long should a first regulatory data warehouse take?

A useful first release can often be delivered in approximately 90 days when one structured source is available and scope is controlled. A full multi-system supervisory-data program will take longer.

Do we need to clean all our data before starting?

No. Waiting for perfect data can delay the program indefinitely. Establish explicit data-quality rules, identify exceptions and expose them through the reporting environment.

Should we replace our existing regulatory systems?

Usually not as part of the first warehouse program. Existing portals, case-management applications, document systems and finance platforms can remain operational systems while the warehouse integrates the information needed for reporting and analysis.

How do we prevent vendor lock-in?

Ensure the regulator owns or controls the data model, pipeline logic, documentation, credentials, environments and source-to-target mappings where technically and commercially practical. Avoid undocumented knowledge that exists only with the implementation provider.

Can AI be added later?

Yes. AI becomes more useful when the regulator already has reliable entity identities, common definitions, governed access and traceable information.

Related Amayztech resources

• The modern supervisory system of record
• ReguFlow Pro
• Data, BI & Decision Intelligence
• Regulators & Public Authorities

Next step

Building your first regulatory data warehouse?

Amayztech works with regulators and other complex organizations across regulatory platforms, workflow modernization, data engineering, analytics, integration and ongoing delivery.

We can help you define the supervisory view, map the source estate, establish the regulatory data model, build the first reporting path and turn that foundation into a scalable data program.

Discuss your regulatory data program