You can have the best analytics infrastructure in the world and still not know if you can trust your data. Data lineageData lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.Voir la définition complète → is what closes that gap.
Regulatory compliance: Financial regulators (, Basel III, MiFID II) require banks to demonstrate that their risk data can be traced from source to report. If you can't show where a number came from, every transformation, every join, every aggregation, you fail the audit. compliance without is literally impossible.
Business trust: When the CFO's revenue number doesn't match the CMO's revenue number, someone has to explain why. Without data lineagedata lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.Voir la définition complète →, that investigation takes weeks of manual forensics. With lineage, it takes minutes. Data lineageData lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.Voir la définition complète → is the foundation of data trust.
Impact analysis: When you need to change a source system schemaschemaA schema is the formal blueprint that defines how data is structured, named, typed, and related within a database, file, or message.Voir la définition complète →, you need to know what downstream reports and models will break. Without lineage, you make the change and discover the breakage in production. With lineage, you see the impact before you touch anything.
Debugging: When a dashboard shows a number that looks wrong, lineage lets you trace exactly which transformation introduced the error. Without it, you're hunting through dozens of pipelines hoping to find the bug.
Technical lineage traces the data flowdata flowAn automated sequence of steps that moves data from source to destination: ingestion, transformation, validation, and loading, so it arrives clean and ready to use.Voir la définition complète → at the system level: Table A → SQLSQLSales Qualified Lead: a prospect the sales team has validated as ready for direct outreach and a proposal, having passed clear qualification criteria.Voir la définition complète → transformation → Table B → ETLETLETL (Extract, Transform, Load) is a data integration process that pulls data from sources, reshapes it into a consistent format, and writes it into a target system.Voir la définition complète → job → Data warehouseData warehouseA central repository that consolidates data from many source systems into a structured, query-optimized store designed for analytics, reporting, and business intelligence.Voir la définition complète → → Report. It's generated automatically by modern tools and is primarily useful for engineers and architects.
Business lineage translates technical lineage into business terms: "The revenue figure in the CFO's dashboard comes from transaction data in the ERP, adjusted for returns processing, and excludes intercompany transactions as defined in Policy FIN-047." This is what executives and auditors actually need.
The CDO's challenge: technical lineage is auto-generated (tools like Collibra, Alation, or OpenLineage capture it from your pipelines). Business lineage requires human curation, someone who understands both the business process and the technical implementation must write it. This is typically the Data StewardData StewardA business-side owner responsible for the quality, consistency and appropriate use of data in their domain.Voir la définition complète →.
Vérification des acquis
1. According to the lesson, which regulatory framework explicitly requires banks to demonstrate that risk data can be traced from source to report, making data lineage compliance 'literally impossible' without it?
2. Per the lesson, who is typically responsible for curating business lineage?
3. OpenLineage, mentioned in the lesson, is best described as:
4. Select ALL the business benefits of data lineage explicitly cited in the lesson:
Sélectionnez toutes les réponses correctes.
5. Select ALL correct statements about technical vs. business lineage according to the lesson:
Sélectionnez toutes les réponses correctes.
MetadataMetadataDonnées sur les données, informations décrivant le contexte, la structure, la provenance et les caractéristiques d'un asset de données (auteur, date, format, source, définition). is often described as "data about data." That's technically correct but unhelpfully abstract. In practice, metadatametadataDonnées sur les données, informations décrivant le contexte, la structure, la provenance et les caractéristiques d'un asset de données (auteur, date, format, source, définition). is the context that makes data usable:
Technical metadata: SchemaSchemaA schema is the formal blueprint that defines how data is structured, named, typed, and related within a database, file, or message.Voir la définition complète → definitions, data types, table relationships, APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → contracts, update frequencies. Auto-captured by your data platforms.
Business metadata: Business definitions ("customer" means a person who has made at least one purchase, excluding trial users and employees), business owners, data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → rules, sensitivity classification.
Operational metadata: Data freshness, last update timestamps, pipelinepipelineAll active sales opportunities across the stages of the sales process, together with their combined potential value and probability of closing.Voir la définition complète → run history, data volume trends. Critical for monitoring data health.
Social metadata: Who is using this dataset? How many times has this dashboard been viewed? Which data assets are most queried? Who has approved this data as trustworthy? Increasingly important for driving adoption of well-governed data.
A data catalogdata catalogA centralized inventory of an organization's data assets, enriched with metadata, that helps people find, understand, and trust the data they need.Voir la définition complète → is the interface that makes lineage and metadatametadataDonnées sur les données, informations décrivant le contexte, la structure, la provenance et les caractéristiques d'un asset de données (auteur, date, format, source, définition). useful. Think of it as Google for your internal data: you search for "customer revenue," the catalog surfaces the relevant tables, dashboards, and reports, shows you who owns them, how fresh they are, what they mean, and where they came from.
Without a catalog, data teams waste enormous time answering "where is the data I need and can I trust it?" With one, those questions take seconds.
Tool comparison:
The tool is secondary. The adoption challenge is primary. A data catalogdata catalogA centralized inventory of an organization's data assets, enriched with metadata, that helps people find, understand, and trust the data they need.Voir la définition complète → that nobody uses is a governance theater prop. Build adoption through integration (surface the catalog in Slack, in BIBITechnologies and processes that turn raw data into actionable insights via reporting, dashboards and analysis, so teams can decide based on facts rather than intuition.Voir la définition complète → tools, in the data warehousedata warehouseA central repository that consolidates data from many source systems into a structured, query-optimized store designed for analytics, reporting, and business intelligence. UI), curation (ensure the highest-used assets are well-documented first), and recognition (reward teams that contribute quality ).