Accelerated Innovation

Assessing the Core Business Value of Your Data

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Assessing the Core Business Value of Your Data

Description

Assessing the Core Business Value of Your Data ensures your enterprise understands which datasets most directly support growth, efficiency, and risk mitigation. This capability focuses on systematically identifying, scoring, and prioritizing the data assets that drive outcomes aligned with business strategy.

Why it's Important

Most enterprises are overwhelmed by the volume and variety of data available – but not all data is equally valuable. GenAI initiatives that treat all data as equal often waste time, inflate costs, and introduce unnecessary risks. By identifying which data sources drive customer engagement, product performance, operational efficiency, or financial decision-making, organizations can make smarter investments in data quality, readiness, and governance. This clarity accelerates GenAI use case success, reduces data sprawl, and builds stronger alignment between technical and business teams. Ultimately, knowing which data matters most is foundational to scaling GenAI impact.

Why it's Challenging @ Scale

  • Data Value is Context-Dependent: The same dataset may be critical in one domain but irrelevant in another, making universal scoring difficult.
  • Lack of Standardized Scoring Models: Most organizations lack a repeatable framework to assess and compare data value across use cases.
  • Business Stakeholder Engagement Gaps: Scoring data effectively requires deep alignment with business priorities, which is often missing or inconsistent.
  • Siloed Ownership of Critical Data: Valuable data is often fragmented across teams, limiting the ability to assess and compare its overall impact.
  • Inconsistent Use of Data in GenAI Projects: Without clear prioritization, teams may train models on irrelevant or low-value data, diminishing returns.

Complexity

High: Maturing this capability requires cross-functional alignment, business engagement, and strong change management to integrate value-based scoring into daily data operations.

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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 Enterprise GenAI Data Readiness workshop (2 hrs.) to understand foundational key concepts and explore applied best practices.
  • Explore enterprise GenAI ready data key concepts.
  • Establishing effective data value and quality measures.
  • Exploring discoverability and understandability best practices.
  • Exploring accessibility, observability, and connectibility best practices.
  • Defining your GenAI data readiness roadmap.
  • 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.
  • Score High-Impact Datasets: Launch a pilot to score data assets by business value. Focus on one business unit or product line.
  • Define Value-Driven Criteria: Co-create value-based scoring criteria with business partners. Ensure scoring reflects real-world impact.
  • Publish Prioritized Data Lists: Visualize and publish a ranked list of high-value datasets. Create visibility to guide GenAI prioritization.
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:
  • Implementing Value Scoring for In-Scope GenAI Data.
  • Ensuring Your GenAI Data is Trustworthy.
  • Enterprise GenAI Search Best Practices.
  • Enterprise GenAI Data Explorability Best Practices.
  • Enterprise Data Entitlements Management Best Practices.
  • GenAI Data Definition Best Practices.
  • GenAI Metadata Management Best Practices.
  • GenAI Data Ontology Best Practices.
  • GenAI Data Consumer Enablement Best Practices.
  • GenAI Data Accessibility Best Practices.
  • GenAI Data Lineage Best Practices.
  • GenAI Data Auditability Best Practices.
  • GenAI Data Explainability and Transparency Best Practices.
  • GenAI Data Monitoring & Alerting Best Practices.
  • 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 your current scoring model and confirm alignment with business impact.
  • Define in-scope Processes and Guardrails: Clarify which domains and data types are included and what standards will apply.
  • Close any Data or Measurement Gaps: Identify missing quality or usage metrics needed to support value scoring.
  • 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: Roll out value scoring in waves, starting with high-priority domains.
  • Build Awareness and Finalize Enablers: Provide education, training, and toolkits to support adoption across teams.
  • Operationalize Your Comms Plan: Share how scoring drives GenAI prioritization and recognize early adopters.
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 Value Scoring Criteria: Align on a shared framework that reflects enterprise-wide priorities and use cases.
  • Create Reusable Scoring Templates: Develop and publish templates and tools to support repeatable data value assessments.
  • Integrate Scoring into Governance Workflows: Embed value scoring checkpoints into standard intake and prioritization processes.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers.
  • Expand Scoring Coverage Across Domains: Extend business value scoring to all major data domains and systems.
  • Automate Data Prioritization Workflows: Use automated triggers and pipelines to identify and surface high-value datasets.
  • Enable Self-Service Scoring by Teams: Empower domain owners to assess and update data value using guided tools.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum.
  • Spotlight Business-Aligned Data Wins: Highlight how prioritizing data by value has improved GenAI use case performance.
  • Publish Data Value Success Stories: Share team examples that demonstrate the impact of better data prioritization.
  • Reward Data Leadership Behaviors: Recognize teams that consistently maintain high-value, high-quality datasets.
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.
  • Operationalize Data Value Scoring: Embed scoring outputs into enterprise data catalogs, governance systems, and intake workflows.
  • Simplify User Experience for Scoring Tools: Ensure scoring tools are intuitive and integrated into existing platforms.
  • Align Score Visibility with Decision-Making: Make high-value data scores visible in dashboards, OKRs, and prioritization meetings.
  • Leverage Automation: Use GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort.
  • Automate Scoring Based on Usage Signals: Continuously update scores based on consumption metrics, quality indicators, and outcomes.
  • Trigger Governance Actions from Scoring Thresholds: Route datasets for remediation or investment based on scoring rules.
  • Continuously Rank and Surface Strategic Data Assets: Auto-curate a live inventory of the most valuable datasets for GenAI development.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases.
  • Refine Scoring Models Using GenAI Feedback Loops: Use GenAI solution outcomes to strengthen data value models over time.
  • Expand Scoring to New Data Types and Modalities: Include unstructured, streaming, or third-party data in enterprise scoring frameworks.
  • Benchmark Against Industry Leaders: Compare scoring models and value frameworks externally to sharpen competitive advantage.

Key "Watchouts"

  • Treating All Data as Equally Valuable: Failing to prioritize data can lead to wasted resources and missed opportunities.
  • Scoring Without Business Input: Models that don’t reflect actual business priorities are unlikely to drive meaningful outcomes.
  • Overcomplicating the Scoring Process: Excessive complexity can limit adoption and slow down decision-making.
  • Neglecting to Operationalize Scoring Results: Even strong value models are useless if not integrated into workflows.
  • Failing to Evolve Scoring Over Time: Business value changes – scoring models must adapt to remain relevant.

Targeted Benefits

  • Smarter GenAI Investment Decisions: Focuses time and funding on the most valuable, impact-driving data.
  • Greater Alignment Between Business and Data Teams: A shared view of value bridges priorities and improves collaboration.
  • Faster Time-to-Impact for GenAI Use Cases: Prioritized data improves model relevance, speed, and outcomes.
  • Reduced Data Sprawl and Storage Costs: Low-value data is deprioritized or retired, lowering overhead.
  • Stronger GenAI Governance and Accountability: Value scores bring transparency and clarity to how data is used and managed.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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