Accelerated Innovation

Identifying In-Scope Data for Your GenAI Solutions

By /

Identifying In-Scope Data for Your GenAI Solutions

Description

Identifying in-scope data for your GenAI solutions involves determining which datasets are relevant, high-value, and appropriate for your intended use cases. This includes evaluating what data exists, how it aligns to business priorities, and how it can be prepared to support GenAI performance.

Why it's Important

Without a clear and consistent approach to identifying in-scope data, GenAI projects risk using irrelevant, low-quality, or incomplete inputs. This can lead to reduced model accuracy, poor user experience, or ethical risks. Clear data scoping practices help teams prioritize the most impactful datasets, accelerate solution development, and reduce time spent retrofitting or re-engineering systems later. It also lays the foundation for data compliance, quality, and readiness, which are critical for scaling GenAI responsibly across the enterprise.

Why it's Challenging @ Scale

  • Decentralized data ownership: Teams often struggle to identify and access the right data due to fragmented data ownership across departments and systems.
  • Lack of clarity on use case requirements: Without well-defined solution objectives, it’s difficult to determine what data is truly in-scope.
  • Overabundance of irrelevant data: Enterprise systems may contain massive volumes of data, much of which is unnecessary or unhelpful for specific GenAI use cases.
  • Inconsistent data documentation: Poor metadata and unclear data definitions make it hard to evaluate whether a dataset should be included.
  • Low visibility into existing data assets: Many organizations lack centralized inventories or tools to help teams discover and assess what data is already available.

Complexity

High: Maturing this capability requires cross-functional collaboration, improved data discovery and inventory tools, and repeatable frameworks to define and prioritize in-scope data across diverse business contexts.

Ready to accelerate your GenAI journey?

Click below to validate your access

Let's go!

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 GenAI Data Scoping Sprint: Identify and assess in-scope data sources for a high-priority use case.
  • Develop a Minimum Viable Data Map: Visualize key datasets and gaps for a single GenAI prototype.
  • Pilot a Data Intake Template: Standardize how teams document and submit in-scope data for GenAI experimentation.
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: Evaluate how consistently and effectively in-scope data is identified for current GenAI pilots.
  • Define In-Scope Processes and Guardrails: Document clear criteria and reusable methods to determine which datasets qualify as in-scope.
  • Close Any Data or Measurement Gaps: Identify missing metadata, data quality indicators, or usage insights needed to improve scoping precision.
  • 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: Sequence data scoping efforts to align with the rollout of high-impact GenAI use cases.
  • Build Awareness and Finalize Enablers: Equip teams with toolkits, discovery templates, and reference guides for consistent execution.
  • Operationalize Your Comms Plan: Establish routines for sharing updates, success stories, and learnings around data identification and selection.
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.
  • Standardize Data Scoping Guidelines: Publish clear, accessible criteria for identifying in-scope data across GenAI initiatives.
  • Develop Shared Templates and Checklists: Create reusable formats for documenting, submitting, and reviewing data scoping efforts.
  • Embed Scoping into Workflow Governance: Integrate in-scope data identification into solution intake, design, and review processes.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers.
  • Broaden Application of Scoping Practices: Extend use of standardized scoping methods to new business units and domains.
  • Upskill Cross-Functional Teams: Train product owners, analysts, and data leads to apply consistent scoping practices independently.
  • Expand Access to Discovery Tools: Provide teams with better visibility into available data sources via catalogs or search platforms.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum.
  • Showcase Data Scoping Success Stories: Share examples of how proper scoping accelerated GenAI development or improved outcomes.
  • Highlight Process Efficiency Gains: Quantify time saved or rework avoided due to better scoping discipline.
  • Recognize Champions and Contributors: Spotlight individuals who helped operationalize and scale this practice.
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 Scoping Tools into Solution Intake: Equip teams with self-service forms or digital assistants that guide data scoping during project intake.
  • Automate Scoping Feedback Loops: Use AI or rule-based systems to flag when in-scope data is incomplete or misaligned.
  • Integrate with Data Catalogs and Platforms: Allow teams to search, filter, and validate potential in-scope datasets directly from centralized systems.
  • Leverage Automation: Use GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort.
  • Automate In-Scope Data Tagging: Use machine learning or NLP to pre-label or suggest relevant datasets based on use case inputs.
  • Auto-Generate Scoping Summaries: Create draft summaries of in-scope data for each solution using enterprise templates.
  • Monitor Data Fitness with Alerts: Trigger alerts when data age, format, or completeness fall below acceptable thresholds for GenAI readiness.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases.
  • Refine Scoping Criteria Using Performance Data: Adapt your scoping guidelines based on which data profiles deliver the best results.
  • Extend Scoping Across Modalities: Expand identification practices to include audio, video, and other non-textual sources.
  • Benchmark Against Industry Leaders: Compare your scoping practices to peers and adjust based on evolving standards and expectations.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Over-scoping or under-scoping data: Including too much irrelevant data or missing critical sources can both degrade GenAI performance.
  • Failing to align with business context: Scoping efforts that ignore use case goals often produce technically sound but low-value datasets.
  • Treating scoping as a one-time step: Data needs evolve-teams must revisit what’s in-scope as solutions change or expand.
  • Relying solely on manual discovery: Without system-level support, scoping remains inconsistent and dependent on tribal knowledge.
  • Delaying integration with tooling: If scoping practices aren’t embedded into workflows, they become a bottleneck or get bypassed entirely.

Targeted Benefits

While Identifying In-Scope Data for Your GenAI Solutions can be challenging, its benefits are clear and compelling, including:

  • Faster time to value: Targeted data reduces delays and rework during solution development.
  • Improved GenAI output quality: High-relevance inputs increase the accuracy and usefulness of generated results.
  • Reduced risk and waste: Eliminating unnecessary or risky data from the start limits exposure and inefficiency.
  • Greater scalability and consistency: Standardized practices make it easier to replicate success across teams and use cases.
  • Stronger alignment with business priorities: Focused scoping ensures data efforts support the most critical GenAI opportunities.

Looking to Move Faster, and 'Go Bigger'?

Contact us to explore additional acceleration resources or support.
Click here
Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

Ask me anything about AI concepts, best practices, Accelerated Innovation solutions, or how to get started.