DataTerminology.comNEW
Documented methods shown to consistently yield superior outcomes and reduce risk in a given domain.
DESCRIPTION
Best practices are proven, repeatable techniques that guide teams toward reliable, high-quality outcomes. They codify lessons learned, standards, and patterns to reduce errors and variability. In data and AI, examples include version control, code review, reproducible pipelines, and robust testing. Best practices evolve with technology and organizational context, balancing rigor with adaptability. They underpin governance, compliance, and operational excellence.
ALTERNATIVE DEFINITIONS
Wikipedia
A best practice is a method or technique that has been generally accepted as superior to any alternatives because it produces results that are superior to those achieved by other means.
Emphasizes general acceptance and superiority compared to alternatives.
TechTarget
Best practices are authoritative standards or sets of guidelines that are known to produce good outcomes if they are followed.
Frames best practices as standards or guidelines producing good outcomes.
Project Management Institute
Best practices are effective techniques and processes that have been shown to consistently achieve project objectives.
Contextualizes within project management objectives and consistency.
Cambridge Dictionary
A set of established methods or procedures that are accepted as being the most effective.
Concise, general-language definition highlighting established and effective methods.
RELATED TERMS
Guidelines Design Patterns Standard Operating Procedure Code Review Quality Assurance Governance Playbook Checklist Framework
COMPETING TERMS
Anti-Pattern
RELATIONS
- Broader: Guidelines
KNOWLEDGE AREAS
Software Engineering Data Management Collaboration Testing & Validation
TARGET AUDIENCE
Data Engineer Software Developer DevOps Engineer Data Architect IT Manager Chief Technology Officer Data Governance Manager
EXTERNAL SOURCES
- Wikipedia - Best practice
- Microsoft Learn - DevOps practices overview and guidelines
- Google Cloud - DevOps best practices checklist
- AWS - Well-Architected best practices framework
TOP QUESTIONS
Why are Best Practices important in data and AI work?
They reduce risk, improve quality, and enable repeatable outcomes. In fast-moving domains, codified practices help teams maintain reliability under change.
Category: Relevancy
How should Best Practices be maintained over time?
Review regularly, collect feedback, and evolve practices with technology and business changes. Keep them documented, measurable, and enforceable.
Category: Management
What is a common misunderstanding about Best Practices?
People assume they are rigid rules; in reality, they are adaptable guidelines informed by context, risk, and objectives.
Category: Misunderstanding
What are common mistakes around Best Practices?
Blindly copying without context, failing to document exceptions, and neglecting continuous improvement lead to stagnation and misapplication.
Category: Mistakes
What risks arise from ignoring Best Practices?
Increased defects, security vulnerabilities, compliance failures, and unpredictable outcomes can erode trust and value.
Category: Risk
How do Best Practices relate to standards and guidelines?
Best practices often synthesize standards and guidelines into actionable steps tailored to specific workflows.
Category: Requirements
What is a competing concept with Best Practices?
Anti-Pattern describes commonly used but ineffective or harmful approaches that best practices aim to prevent.
Category: Alternatives
How can teams adopt Best Practices effectively?
Start with small pilots, measure outcomes, document learnings, and gradually embed practices into workflows and tooling.
Category: Example
Where to start learning Best Practices for data engineering?
Review vendor frameworks like AWS Well-Architected, study DevOps handbooks, and practice code reviews, testing, and documentation.
Category: Requirements