book

Designing Financial Data Architectures

Name: Designing Financial Data Architectures
Author: Tamer Khraisha
ISBN: 9798341627178

by Tamer Khraisha

December 2026

Intermediate to advanced

400 pages

8h 57m

English

O'Reilly Media, Inc.

Read now

Unlock full access

Brief Table of Contents (Not Yet Final)
Preface
Who This Book Is ForWho This Book Is Not ForMy Approach to Writing This Book
1. An Introduction to Financial Data
Finance BasicsFinancial Assets, Instruments, and SecuritiesStocks, shares, and equityBonds and fixed-incomeDerivativesSecurities exchangesClearing and SettlementThe Financial Data EcosystemFinancial Data Generation MechanismsFinancial Data SourcesFinancial Data Arrival and Exchange MechanismsFinancial data types, formats, and structuresCharacteristics of Financial DataFinancial Data Value ChainData management challenges in financial servicesData Silos and FragmentationFinancial Data Standardisation Cross-Entity LinkageReference Data ManagementLegacy SystemsData Quality and IntegrityData Security and PrivacyData Integration and AggregationScalability and PerformanceRegulatory ComplianceAI AdoptionSummary
2. Financial Data Architectures
Contextualizing ‘Architecture’ in IT: A Broader PerspectiveDefining financial data architectureWhy is a financial data architecture needed?When is a financial data architecture not needed?Very Small Businesses with Simple Data NeedsStartups in the Very Early StagesShort-Term ProjectsHighly Isolated DataReliance on Third-Party PlatformsData Architecture MaturityComponents of a Financial Data ArchitectureFinancial Data ModelsFinancial Data GovernanceIntegration and InterfacesTechnological InfrastructureProcessing and Transformation LogicPipelines and OrchestrationAccess LayerReference and MetadataMonitoring and ObservabilityConstraints and influences on financial data architecturesBusiness RequirementsTechnology DriversEconomicsBusiness PoliciesRegulatory RequirementsCategorising financial data architecturesClassification by Storage TechnologyDeployment-Based ClassificationClassification by Data Modeling ApproachProcessing-Focused ClassificationBusiness-Focused ClassificationClassification by Data Ownership & GovernanceWhy master the art of financial data architecture?Data is the BusinessDomain-Driven Development is Gaining GroundA Required Human Skill in the Age of AIMarket DemandFinancial InnovationSolve Complex ChallengesCareer LeverageSummary
3. Foundations of Financial Data Modeling
What Is Financial Data Modeling?Diagramming and Visualization of Financial DataCrow’s Foot NotationBarker NotationUnified Modeling LanguageFinancial Data Modeling Life CycleConceptual data modelingLogical data modelingPhysical Data ModelingModeling Financial Data LifecyclesSummary
4. Advanced Financial Data Modeling Techniques
Point-in-Time Modeling of Financial DataUnderstanding the Temporal Dynamics of Financial DataThe Consequences of Non-PIT Financial DataApproaches to Point-in-Time Data ModelingLifecycle-Aware Modeling of Financial DataModeling Financial Market LifecyclesFinancial Event Data ModelingSemantic Modeling of Financial DataControlled Financial VocabulariesFinancial TaxonomiesFinancial OntologiesFinancial Message ModelingWhat Is a Financial Message?What is financial message modelingAnatomy of a financial messageISO20022: The universal financial message standardISO20022: the MetamodelISO20022: The scope levelISO20022: The conceptual levelISO20022: The logical levelISO20022: The Physical levelISO20022: VariantsThe ISO 20022 Repository: The Financial VaultISO20022: A Simplified Illustrative ExampleSummary
5. Financial Reference Data
Financial reference data defined: a conceptual breakdownFinancial InstrumentsFinancial PartiesFinancial ContractsFinancial TransactionsWhere Reference Data Fits in the Financial Data LandscapeReference Data vs. Master Data vs MetadataFinancial Reference Data vs Market data vs Transaction DataFinancial Reference Data vs Company ProfilesThe Critical Role of Reference Data in FinanceInput to financial transactionsData Integration and HarmonisationRegulatory, Risk, and Compliance ReportingValuation, Pricing, and AnalyticsTransaction Cost AnalysisCounterparty and Entity VerificationTypes of Financial Reference DataCountry Reference DataSector Reference DataLegal Entity Reference DataMarket Infrastructure & Venue Reference DataCurrency Reference DataStock Reference DataFixed Income Reference DataDerivative reference dataFund Reference DataCorporate actions dataPayment Reference DataSummary
6. Designing Financial Reference Data Architectures
Step 1: Understand Your Business Needs and WorkflowsFinancial Market lifecyclesEntity Scope & CoverageRegulatory & Compliance RequirementsReference Data Acquisition and SubscriptionsStep 2: Understand the challengesInconsistent Data ModelsLack of a Single Source of TruthLegacy Infrastructure and Technical DebtManual Intervention and Spreadsheet DependencyEvolving Standards and Regulatory RequirementsEntity matchingGovernance and Cultural BarriersData QualityStep 3. Model your Reference Data LifecycleGenerationAcquisition/IngestionCleansing, Normalization and EnrichmentStorageDistributionUsage and MaintenanceUpdateArchivalRetirement/DeletionStep 4. Design a Data Governance FrameworkData QualityData IntegrityData Usage and SpendData ArchivalStep 5. Design your Data Collection StrategyStart with a business discussionIdentify the required reference data elementsIdentify technical requirementsDefine a data provider evaluation frameworkOutline your ingestion layerStep 6: Design your Identification SystemDesired properties of a financial identification systemCreate an internal identifierStep 7: Design your Entity Extraction, Resolution, and Linking capabilitiesEntity ExtractionEntity ResolutionCross-Entity LinkageStep 8: Model your Reference DataGeneral principles for modeling financial reference dataUnderstand your business needsAim for structured data firstAvoid the “one huge table” anti-patternDefine common attributes explicitlyLeverage SemanticsDesign for Point-In-TimeDefine uniqueness rulesAvoid over-normalizationDeep dive: Modeling a Security MasterThe Generalization/Specialization approachThe Product Composition ApproachStep 9: ⁠Design your Reference Data FlowsFlow and Task DefinitionLifecycle-oriented reference data flowsStep 10: Design the Consumption LayerStep 11: Define Technology Selection PrinciplesData storage technologiesData Distribution TechnologiesFlow orchestration technologiesData management technologiesSummary
About the Author

Overview

The financial industry runs on a vast and complex data ecosystem—one that presents unique challenges and requirements for financial market players. To make sense of this complexity, financial institutions must design data architectures that do more than just move data—they must support real business needs, account for the unique nature of financial data, and meet the industry's high standards for compliance, performance, and resilience.

Designing Financial Data Architectures provides a comprehensive guide to building data frameworks tailored to the requirements of modern financial systems. Whether supporting trading and payments, powering an investment platform, enabling AI-powered analytics, or meeting ever-evolving compliance demands, this book equips professionals with the principles and design patterns to structure, manage, and optimize financial data systems.

Understand the financial data landscape and its data management challenges
Learn about financial data modelling, semantics, ontologies, and entity relationship diagrams
Implement governance frameworks for data security, quality, and compliance
Design data architectures tailored to the needs of financial markets
Master the principles of operational and analytical data architecture for financial systems
Leverage AI-driven methodologies to enhance data-driven decision-making

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9798341627185Errata Page

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills