Data AI Strategy Design And Prioritization

Data & AI Strategy Design and Prioritization

Purpose

Prevents two common data strategy mistakes: (1) AI/data investments are made in silos without anyone having broken a business goal down into concrete data points, so the result is technically impressive but strategically disconnected; and (2) an organization tries to build tomorrow's models on today's data without noticing that the right data should already be collected now. This skill produces a structured bridge between a business goal and a concrete data/AI investment, and prioritizes investments by what's both strategically valuable AND achievable given data readiness.

Anchored in research

• The Data & AI Design Thinking tradition (industry consulting practice, a synthesis of multiple sources): an AI strategy isn't built in silos — cross-functional facilitation ensures the strategy solves genuine user and stakeholder needs ("Systems over Objects").
• The Driver Tree tool (an established business-analytics method): a business goal is broken down hierarchically into components until concrete, measurable drivers are reached — the same logical principle as the McKinsey-tradition issue tree (see ../../../strategic-thinking/skills/hypothesis-driven-strategy/SKILL.md), applied here to identifying data points and AI solutions.
• The "tomorrow's models require today's data" heuristic (a design principle from data monetization pipelines): if a given AI model is meant to be feasible 12-24 months from now, the unique data it requires needs to start being collected today — the lead time for a data investment is typically longer than the lead time for building the model.

Method

1. Build a Driver Tree from the business goal down to data points. Start from the top-level business goal (e.g. "increase customer retention") and break it down into successive questions: what sub-drivers drive this goal? What data is needed to measure each sub-driver? What AI/analytics solution could influence each sub-driver? Keep breaking it down until you reach concrete, measurable data points — don't stop at an abstract level ("better customer understanding" isn't a data point, "usage rate of feature X over the past 30 days" is).
2. Assess every branch of the tree: does AI/data genuinely add value here, or is it noise? Not every component of a business goal benefits from data or AI — some are better solved with a process change or a human decision. Mark each branch either "data/AI-relevant" or "not data/AI-relevant, solve otherwise" before continuing only with the relevant branches.
3. Separate "what can we do now" from "what do we need to build for the future" (Agile Value Assessment). For every identified data point:
   • Available now: the data already exists at sufficient quality — analysis/modeling can start immediately.
   • To be built: the data doesn't yet exist or its quality isn't sufficient — requires designing a collection point before the model is possible. Apply the "tomorrow's models require today's data" heuristic: if this data is wanted for use in 12-24 months, collection needs to start now, not when the model is wanted.
4. Place every identified data/AI opportunity on a Data Readiness × Strategic Value matrix:
   • Achievable now, high value: start here first — the fastest path to proven value.
   • To be built, high value: launch the data-collection investment now, even though the model won't be ready right away — this is the matrix's most strategic quadrant, because it builds future defensible advantage (see the flywheel test in ../data-role-diagnosis/SKILL.md).
   • Achievable now, low value: don't prioritize, even though ease makes it tempting — low strategic value doesn't change with ease.
   • To be built, low value: reject or shelve — the costliest combination (long lead time, small payoff). This matrix follows the same logic as the 2x2 prioritization matrix in ../../../ai-strategy-and-governance/skills/ai-opportunity-portfolio/SKILL.md, but the axes are data-specific (Data Readiness, not Technical Feasibility in general) — use this BEFORE the general AI opportunity scoring, when the question is specifically about data readiness.
5. Facilitate building the tree cross-functionally, not alone or with just the data team. The business owner knows which driver actually matters; the data/technical expert knows what's feasible; the end user knows which solution would actually help day-to-day. A tree built together with these three perspectives is far more likely to be right than one built inside a single function.
6. Produce a prioritized roadmap that separates "start now" (high value, data ready) and "start data collection now, model later" (high value, data to be built) into their own parallel tracks — don't merge them into a single timeline, since they have different time horizons and different success criteria.

What this skill does NOT do

• Doesn't make the final investment decision for you — produces a structured prioritization to support human decision-making.
• Doesn't replace the broader 5-dimension scoring in ../../../ai-strategy-and-governance/skills/ai-opportunity-portfolio/SKILL.md — this skill produces an input into it (particularly the Data Readiness dimension), not a full scoring of overall business impact by itself.
• Doesn't build the technical data architecture or collection system — identifies WHAT data is needed, not HOW it's technically collected.
• Doesn't confirm figures, market data, or data-quality assessments from memory — uses the inputs you provide, or marks an assumption clearly ([assumption — verify]).

Refinement notes

Areas to keep deepening with real practice:

• your own Driver Tree examples from different industries
• a concrete facilitation template for a Driver Tree workshop (into ../../references/)
• rules of thumb for how deep the tree typically needs to be broken down before reaching a useful data-point level

Once this section is filled in and validated in practice, update the maturity field in skills_index.json to draft, validated, or canonical (see ../../../meta/maturity_levels.md). Don't add new fields to the frontmatter — name and description are the only ones allowed (see ../../../meta/frontmatter_schema.md).

Continue from here

• Before this: ../data-role-diagnosis/SKILL.md — first establish whether data functions as an enabler or the asset role is being pursued, before prioritizing investments.
• Next in this pack (if the goal is monetization): ../data-monetization-model-selection/SKILL.md
• Related skill in another pack: ../../../ai-strategy-and-governance/skills/ai-opportunity-portfolio/SKILL.md — receives this skill's prioritized data/AI opportunities into a broader 5-dimension scoring.
• Related skill in another pack: ../../../strategic-thinking/skills/hypothesis-driven-strategy/SKILL.md — the same issue tree logic applied more generally to strategic questions, not just data points.
• A ready-made skill chain for this situation: see ../../../playbooks/
• This pack's shared guardrails: ../../CLAUDE.md

References

• ../../references/ — the pack's shared background material
• ../../CLAUDE.md — the pack's shared guardrails