Accelerated Innovation

Evaluating the Performance of Your GenAI Solution Data

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Evaluating the Performance of Your GenAI Solution Data

Description

This capability focuses on measuring how well your solution data supports GenAI use cases. It includes tracking data quality, coverage, freshness, and relevance-and linking those factors to GenAI output accuracy, latency, and user satisfaction.

Why it's Important

Strong GenAI performance depends on more than model capabilities-it hinges on the quality of the underlying data. Without evaluation, teams risk building on outdated, incomplete, or misaligned content. By regularly assessing solution data, organizations can identify weak points, prioritize improvements, and optimize for retrieval, summarization, and generation tasks. This not only boosts system performance but also builds trust, speeds time to value, and ensures continued alignment with user needs.

Why it's Challenging @ Scale

  • Lack of clear metrics: Many teams struggle to define what “good” looks like when evaluating GenAI data performance.
  • Disconnection between data and outputs: It can be difficult to trace specific quality issues back to their data source.
  • Volume and variety of data: Large, diverse datasets complicate efforts to monitor quality, freshness, and relevance.
  • Limited feedback integration: Without systematic user or model feedback, issues may go undetected.
  • Inconsistent ownership: Data evaluation often spans teams with different tools, standards, and incentives.

Complexity

High: Maturing this capability requires defining meaningful evaluation criteria, deploying scalable measurement tools, and integrating findings into ongoing data operations and GenAI workflows.

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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.

The most important part of any journey is starting. To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the Making Your Solution Data “GenAI Ready” workshop (2 hrs.) to understand foundational key concepts and explore applied best practices.:
  • Defining ‘GenAI Ready’ Data Requirements
  • Assessing Existing Data Gaps and Risks
  • Understanding the Role of Context and Format
  • Preparing for Ethical and Legal Compliance
  • Aligning Data Strategy to GenAI Use Cases
  • 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.:
  • Run a Data Quality Scan: Use automated tools to assess accuracy, freshness, and completeness of a key dataset.
  • Launch an Output Review Workshop: Manually review GenAI outputs linked to a specific dataset and flag content gaps or inconsistencies.
  • Pilot a Data-Driven Scorecard: Create a lightweight dashboard to track GenAI performance indicators tied to specific data assets.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including::
  • Identifying Your Target Data.
  • Defining Your Data Architecture
  • Clearing & Parsing Your Data – Profiling, Cleaning, & Normalizing Your Data
  • Clearing & Parsing Your Data – Parsing & Tokenizing Your Data
  • Pre-Processing & Enriching Your Data – Metadata Enrichment
  • Semantic Enrichment & Multi-Lingual Support
  • Chunking & Embedding Your Data – Chunking, Embedding & Vectorizing Your Data
  • Optimizing Your Solution Data
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale.:
  • Assess Your Proposed Solution or Process: Establish baseline metrics for GenAI accuracy, latency, and grounding using current data.
  • Define in-scope Processes and Guardrails: Document how solution data will be evaluated, reviewed, and updated across use cases.
  • Close any Data or Measurement Gaps: Enable tools to capture retrieval relevance, hallucination frequency, and user corrections.
  • 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: Start with high-impact workflows where data evaluation can drive immediate improvement.
  • Build Awareness and Finalize Enablers: Provide evaluation templates, checklists, and success stories to guide adoption.
  • Operationalize Your Comms Plan: Share how data performance affects GenAI outputs and how teams can help improve both.
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases.:
  • Create a Solution Data Evaluation Framework: Define success criteria, scoring methods, and issue resolution processes.
  • Standardize Output Evaluation Routines: Set expectations for regular audits of GenAI responses linked to specific datasets.
  • Establish Feedback Loops: Incorporate user signals and analyst reviews to flag recurring issues or underperforming content.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers.:
  • Align Data Evaluation with Business KPIs: Show how better-performing data translates to higher satisfaction, accuracy, or task completion.
  • Enable Non-Technical Contributors: Give content owners tools and guidelines for helping evaluate and improve solution data.
  • Integrate with Delivery Pipelines: Make evaluation part of launch readiness and release checklists for GenAI solutions.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum.:
  • Share Before-and-After Data Impact Stories: Highlight improvements in GenAI performance after dataset refinement.
  • Spotlight Business Value Gains: Showcase how better data led to faster decisions, improved customer responses, or fewer errors.
  • Recognize Contributors to Data Quality: Celebrate cross-functional efforts to improve how data supports GenAI capabilities.
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific capability represents a clear competitive advantage for your business.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine.:
  • Embed Evaluation Metrics into Dashboards: Provide real-time visibility into how data quality impacts GenAI results.
  • Automate Data Performance Scoring: Use AI to continuously rate datasets for relevance, completeness, and recency.
  • Build Evaluation into Content Lifecycles: Make performance measurement part of data creation, review, and retirement workflows.
  • Leverage Automation: Use GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort.:
  • Set Up Alerting for Degradation: Notify owners when specific datasets lead to poor retrieval or hallucinated outputs.
  • Use AI to Suggest Data Improvements: Recommend specific enrichments, updates, or removals based on performance trends.
  • Auto-Tag Underperforming Content: Flag problem areas within datasets for prioritized review and remediation.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases.:
  • Expand Evaluation Across Modalities: Measure performance of data powering audio, image, and video-based GenAI use cases.
  • Benchmark Data Quality Against Peers: Compare internally and externally to identify opportunities for differentiation.
  • Close the Loop Between Data and UX: Use user behavior and satisfaction data to drive content optimization decisions.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Focusing only on technical metrics: Latency and volume matter, but don’t reflect whether the data drives useful outcomes.
  • Ignoring user feedback: Without input from those consuming the outputs, performance gaps may go unrecognized.
  • Overlooking content freshness: Outdated data-even if high quality-can result in off-target or misleading outputs.
  • Evaluating in isolation: Reviewing data without considering downstream GenAI behavior leads to incomplete insights.
  • Assuming “good enough” is scalable: Minor issues in small pilots can create major failures when scaled.

Targeted Benefits

While evaluating the performance of your GenAI solution data can be challenging, its benefits are clear and compelling, including:

  • Higher quality GenAI outputs: Relevant, well-maintained data leads to more accurate and trustworthy responses.
  • Faster root-cause diagnosis: Clear visibility into data performance reduces time spent troubleshooting GenAI issues.
  • More strategic data investments: Evaluation results guide where to improve, enrich, or scale solution data.
  • Increased user trust and adoption: When outputs are consistent and reliable, teams are more likely to engage with GenAI tools.
  • Clearer connection between content and value: Data performance tracking shows how content quality drives business impact.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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