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Data Quality

14 items tagged with "data-quality"

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Standards2

Standard

ISO/IEC 29119-1:2022 (Software Testing)

ISO/IEC standards are crucial for ensuring quality and compliance in software migration projects. By adhering to these guidelines, teams can mitigate risks, enhance data integrity, and foster trust among stakeholders. This comprehensive overview provides actionable insights on key requirements, compliance strategies, and tools to facilitate successful migrations.

Standard

SysML 1.6

Understanding the OMG standard is crucial for successful software migrations, as it ensures interoperability, data integrity, and regulatory compliance. By following its guidelines, teams can navigate the complexities of migration projects with confidence, leveraging tools and best practices that uphold these standards while addressing common challenges effectively.

Best Practices4

Best Practice

Data Governance Framework

A structured set of roles, policies, and processes that make an organization accountable for the quality, security, and proper use of its data assets.

Best Practice

Data Quality Management

The practice of measuring, monitoring, and improving data across dimensions like accuracy, completeness, consistency, timeliness, and validity so it stays fit for use.

Best Practice

Data Contracts

Explicit, version-controlled agreements between data producers and consumers that define schema, semantics, quality, and SLAs to prevent breaking changes.

Best Practice

Medallion Architecture

A layered data design that refines data through Bronze (raw), Silver (cleaned and conformed), and Gold (business-ready) tables to improve quality and reuse.

Anti-Patterns1

Anti-Pattern

Schemaless Sprawl

Treating a schemaless store as license to skip data design, so documents drift into inconsistent shapes that no consumer can reliably read.

Tutorials1

Tutorial

How to validate data quality with Great Expectations

Define expectations for a dataset, run validations, and surface failures so bad data is caught before it spreads downstream.

Reference Architectures1

Reference Architecture

Data Observability and Quality Platform

A data observability platform on Snowflake that monitors freshness, volume, and schema with automated tests and lineage on AWS.

Playbooks2

Playbook

Master Data Management Program Playbook

Establish a master data management program to create golden records, resolve duplicates, and govern shared reference data across systems.

Playbook

Data Quality Program Playbook

Stand up a data quality program with profiling, automated tests, SLAs, anomaly detection, and incident workflows across pipelines.

Checklists3

Checklist

Data Quality Validation Checklist

Checks for validating accuracy, completeness, consistency, and timeliness of data after a migration or pipeline change.

Checklist

Data Reconciliation Checklist

Checks for reconciling data between source and target systems after a migration to prove completeness and accuracy.

Checklist

Data Pipeline Observability Checklist

Checks for instrumenting data pipelines with freshness, volume, schema, lineage, and quality monitoring to detect issues early.