Accelerated Innovation

Assessing Your Data Sources

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Assessing Your Data Sources

Description

This capability focuses on evaluating the availability, quality, and relevance of data sources used for Large Language Model (LLM) evaluation. It includes identifying in-scope datasets, validating their fit for purpose, and ensuring they meet enterprise standards for coverage, diversity, and compliance.

Why it's Important

High-quality, well-matched data is the foundation of effective LLM evaluation. Without a clear understanding of what data is available and how suitable it is, teams risk inaccurate results, biased model selection, or compliance issues. Assessing data sources ensures that evaluations reflect real-world scenarios, meet business and regulatory requirements, and can be repeated reliably across use cases. It also helps teams spot gaps early, before they impact downstream performance or trust.

Why it's Challenging @ Scale

  • Data availability varies by use case: Teams often struggle to find relevant data for specialized or emerging domains.
  • Inconsistent data quality standards: Different teams apply different criteria for assessing data accuracy, coverage, and structure.
  • Hard-to-detect gaps and biases: Incomplete or skewed datasets may not be visible until model evaluation results are already affected.
  • Limited documentation and lineage: Many enterprise datasets lack clear metadata, ownership, or update history.
  • Regulatory and compliance constraints: Legal or policy considerations may restrict access to the most relevant evaluation data.

Complexity

High: Maturing this capability requires enterprise-wide visibility into available data, clear quality benchmarks, and well-defined processes to evaluate, document, and maintain evaluation datasets.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

  • Explore Key Concepts & Best Practices: Complete the Evaluating and Selecting the Best Model(s) for Your GenAI Solution workshop (2 hrs.) to understand foundational key concepts and explore applied best practices
  • Outlining the Model Evaluation Lifecycle
  • Understanding Model Types and Capabilities
  • Aligning Evaluation to Solution Objectives
  • Comparing Commercial vs. Open Source Options
  • Establishing a Reusable Evaluation Framework
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy
  • Align on your Current State and define your Target State
  • Create an actionable enablement plan
  • Define target timeline and measures of success
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame
  • Inventory In-Scope Data Assets: Identify existing datasets that could support initial LLM evaluations.
  • Assess Coverage & Fit for Purpose: Evaluate whether sample datasets reflect the intended user needs and business context.
  • Document Gaps and Constraints: Create a lightweight tracker to capture missing, restricted, or low-quality data elements.
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • Defining Your Model Objectives & Requirements
  • Model Evaluation Data Assessment and Prep
  • Selecting In-Scope Models
  • LLM Evaluation
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Review how available datasets are selected, validated, and approved for use in LLM evaluation.
  • Define in-scope Processes and Guardrails: Establish consistent steps for documenting and reviewing the fitness of data sources.
  • Close any Data or Measurement Gaps: Ensure missing, outdated, or low-quality data is flagged and resolved before use.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Prioritize evaluation use cases with well-documented and high-quality data first.
  • Build Awareness and Finalize Enablers: Share guidance and checklists for teams to assess data suitability and readiness.
  • Operationalize Your Comms Plan: Communicate the importance of data quality in evaluation outcomes and share lessons learned across teams.
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Establish Data Suitability Criteria: Define standardized benchmarks for what constitutes “fit-for-purpose” data in LLM evaluations.
  • Create Reusable Data Assessment Templates: Help teams document and validate data quality and relevance efficiently.
  • Integrate Data Review into Evaluation Planning: Make data source validation a required step before launching any LLM test.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Expand Data Readiness Across Teams: Encourage teams to prepare and document evaluation datasets proactively.
  • Provide Access to High-Quality Sample Data: Share curated datasets that meet common evaluation needs and compliance standards.
  • Embed Data Checks in Review Forums: Add data review checkpoints to solution governance and evaluation debriefs.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Spotlight Strong Data Practices: Showcase evaluations that were strengthened by high-quality, well-assessed data sources.
  • Share Before-and-After Examples: Compare outcomes from evaluations with and without strong data foundations.
  • Recognize Data Stewards and Contributors: Highlight team members who improved visibility or quality of enterprise datasets.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Integrate Data Checks into Tooling: Add validation prompts or checklists into LLM evaluation platforms and workflows.
  • Embed Metadata in Registries: Require every evaluation dataset to include source, structure, and access details in the model registry.
  • Align Data Practices Across Teams: Use common processes and artifacts to reduce variation in how data is assessed across projects.
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Automate Data Profiling: Use AI or scripts to assess data quality, completeness, and diversity for candidate datasets.
  • Flag Data Risk or Gaps Automatically: Set up alerts for missing documentation, restricted access, or known data limitations.
  • Generate Summary Reports on Data Readiness: Help teams evaluate whether a dataset is ready to support evaluation goals.
  • Evolve & Further Accelerate: continuously refining GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Refresh Data Benchmarks Based on Use: Refine standards over time as data usage patterns and expectations evolve.
  • Extend to Additional Modalities: Apply data assessment standards to non-text sources such as image, audio, or multimodal inputs.
  • Benchmark Team Readiness: Track which teams have mature data practices in place and provide targeted support to others.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Assuming data is evaluation-ready: Just because a dataset exists does not mean it meets evaluation needs or quality standards.
  • Skipping data validation steps: Failing to review data structure, gaps, or limitations often leads to flawed results.
  • Using mismatched data: Datasets that do not reflect the target use case or user population reduce the value of the evaluation.
  • Underestimating compliance risks: Sensitive or restricted data may require legal review, consent, or access controls.
  • Lacking version control: When datasets change mid-evaluation, results may be inconsistent or invalidated.

Targeted Benefits

While Assessing Your Data Sources can be challenging, its benefits are clear and compelling, including:

  • Higher-quality model evaluation: Well-matched, high-quality data leads to more meaningful and reliable results.
  • Faster evaluation readiness: Reusable datasets and templates reduce prep time and enable quicker experimentation.
  • Lower risk of rework: Early detection of data gaps prevents wasted effort and ensures smoother evaluation cycles.
  • Improved trust and transparency: Documented data choices build stakeholder confidence in evaluation outcomes.
  • Stronger enterprise alignment: Standardized assessment practices support consistent performance across teams and use cases.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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