Implementing Value Scoring for In-Scope GenAI Data
When everything feels “high priority,” data readiness work loses focus. This workshop helps teams define business value criteria, score GenAI-relevant data consistently, and produce a ranked investment plan tied to measurable outcomes.
Leave with a value-scoring model and a prioritized list of the data assets that matter most for GenAI outcomes.
Organizations frequently invest in data readiness broadly—without a shared, measurable way to focus effort on what will move GenAI adoption fastest.
- Too many “important” datasets: Without a common scoring approach, teams can’t agree which data assets deserve priority, funding, and attention.
- Value is discussed, not measured: Scoring criteria aren’t tied to enterprise priorities and KPIs, making decisions subjective and hard to defend.
- Scoring doesn’t stick operationally: Even when value is identified, it isn’t embedded into ingestion and curation workflows—so priorities drift and the same debates repeat.
When data value isn’t made explicit and operational, GenAI readiness becomes unfocused work—and adoption slows.
We help teams implement a repeatable value-scoring approach that turns priorities into action and keeps focus over time.
- Define business value criteria for GenAI data: Establish clear, plain-language criteria that reflect how data enables outcomes, reduces risk, and improves decision-making.
- Align scoring factors to enterprise priorities and KPIs: Connect scoring to what leadership already measures, so prioritization decisions are credible and comparable.
- Build a practical scoring model informed by profiling inputs: Use data profiling signals to strengthen scoring defensibility and reduce opinion-driven ranking.
- Operationalize scoring in ingestion and curation workflows: Embed scoring into how data is onboarded, curated, and improved—so “high value” drives day-to-day decisions.
- Track and optimize usage of high-value data assets: Define simple adoption/usage indicators so teams can see whether priority assets are being used and refine scoring over time.
- Defining business value criteria for GenAI data scoring
- Aligning scoring factors with enterprise priorities and KPIs
- Building value scoring models using data profiling inputs
- Integrating value scoring into data ingestion workflows
- Integrating value scoring into data curation workflows
- Tracking high-value data asset usage over time
- Optimizing value scoring based on adoption and outcomes
- Define a shared set of business value criteria for scoring GenAI-relevant data
- Produce an initial value-scoring model aligned to enterprise priorities and KPIs
- Generate a prioritized list of high-value data assets to focus readiness investment
- Identify where value scoring should be embedded into ingestion and curation workflows
- Establish a lightweight approach to track usage and continuously improve scoring over time
Who Should Attend:
Solution Essentials
Facilitated workshop (interactive discussion + working session)
4 hours
Intermediate
Virtual whiteboard and shared document workspace