DataTerminology.comNEW
Also known as: Source Assessment, Data Source Due Diligence, Data Profiling for Sources
Systematically examine source systems to understand structure, quality, risks, and suitability for integration and analytics.
DESCRIPTION
Evaluating and assessing data sources ensures downstream reliability by analyzing schema, completeness, timeliness, consistency, and privacy risks. Typical steps include profiling samples, checking constraints, reconciling counts, and verifying lineage. Teams assess access methods, SLAs, and change cadence to plan ingestion patterns. Findings inform data contracts, cleansing rules, and governance controls. Early assessment reduces rework, mitigates data risks, and improves trust in shared datasets.
ALTERNATIVE DEFINITIONS
Microsoft Learn
Data profiling is the process of examining the data available in an existing data source and collecting statistics and information about that data.
Highlights statistics-driven examination to inform quality and transformation.
Wikipedia
Data profiling is the process of examining the data available in an existing data source and collecting statistics and information about that data.
Emphasizes exploratory analysis of data structure and content.
IBM
Data profiling analyzes source data to understand its structure, content, and relationships and to identify issues that affect data quality and integration.
Links profiling outcomes directly to data quality and integration planning.
RELATED TERMS
Data Profiling Data Quality Metadata Management Data Discovery Platform Data Exploration Lineage Schema Enforcement Data Contracts Data Sampling Source System
COMPETING TERMS
KNOWLEDGE AREAS
Data Management Data Quality Metadata Management Data Integration & Interoperability Data Engineering
TARGET AUDIENCE
Data Engineer Data Steward Data Analyst Data Architect Data Management Consultant
EXTERNAL SOURCES
- Wikipedia - Data profiling
- Microsoft Learn - Azure Data Factory data profiling guide
- IBM - Overview of data profiling concepts
- Great Expectations - Validation patterns after profiling
TOP QUESTIONS
Why is Evaluate and Assess Data Sources critical before integration?
It reveals structure, quality, and risks early, shaping ingestion strategy and cleansing rules. This reduces rework, prevents bad data propagation, and improves trust in downstream analytics.
Category: Relevancy
What techniques are used to Evaluate and Assess Data Sources?
Teams apply data profiling, schema validation, sampling, constraint checks, lineage verification, and anomaly detection. They also test access methods and performance under realistic workloads.
Category: Requirements
Who should lead Evaluate and Assess Data Sources activities?
Data engineers and stewards collaborate, with architects ensuring standards and analysts validating business meaning. Governance managers oversee compliance and risk considerations.
Category: Management
How do data contracts support Evaluate and Assess Data Sources?
Contracts formalize schema, semantics, SLAs, and change policies. They codify expectations discovered during assessment and reduce ambiguity between producers and consumers.
Category: Best practices
What are common mistakes in Evaluate and Assess Data Sources?
Skipping sampling across time windows, ignoring null semantics, and relying on undocumented fields are frequent errors. Underestimating change cadence leads to brittle pipelines.
Category: Mistakes
How does privacy impact Evaluate and Assess Data Sources?
Assessment must identify personal data, apply minimization, and ensure lawful bases. PII discovery tools and masking strategies should be part of the checklist.
Category: Risk
How often should you re-run Evaluate and Assess Data Sources?
At onboarding and whenever schemas, volumes, or business rules change. Periodic reassessments catch drift and maintain data reliability.
Category: Operations
What is a striking analogy for Evaluate and Assess Data Sources?
It’s like inspecting ingredients before cooking—quality checks prevent spoiled dishes and ensure consistent results for every recipe.
Category: Example
What tools assist Evaluate and Assess Data Sources?
Catalogs, profilers, and validation frameworks such as Microsoft Purview, Great Expectations, and open-source profilers streamline discovery and checks.
Category: Tooling