Data is the operating system of AI‑powered SaaS. It determines what the product can safely decide and do, how fast it responds, how trustworthy it feels, and whether unit economics work. Winning platforms treat data as a governed product: permissioned by identity, normalized into a shared semantic layer, grounded with provenance and freshness, observed for quality and drift, and wired to typed actions with audit trails. The outcome is not just smarter insights but reliable, reversible actions—measured by cost per successful action.
What data must enable
- Context and truth
- Provide the facts AI reasons over: records, policies/SOPs, telemetry, contracts, events, limits, and entitlements—with timestamps, lineage, and uncertainty.
- Personalization and eligibility
- Encode roles, plans, segments, and feature flags to tailor experiences and gate actions by policy.
- Decision and action
- Supply schemas and contracts so outputs can be validated and executed via downstream APIs safely.
- Learning and improvement
- Capture outcomes, reversals, override reasons, and fairness metrics as feedback signals to continuously refine models and policies.
Core building blocks
1) Customer 360 and identity graph
- Resolve users↔accounts↔assets with roles (admin/maker/viewer) and consent.
- Unify product analytics, tickets/CSAT, billing/entitlements, CRM, docs/policies, and relevant third‑party systems.
- Treat identity and ACLs as first‑class: every retrieval and action must respect row‑level security.
2) Grounding with provenance and freshness
- Permissioned retrieval (RAG) that stores source, section anchor, timestamp, owner, and jurisdiction.
- Block uncited outputs; allow “insufficient evidence.” Prefer small, precise snippets over bulk dumps.
- Maintain freshness SLAs per source; emit “what changed” deltas to keep content and decisions current.
3) Semantic layer and schemas
- A shared ontology for entities (accounts, orders, cases), metrics (ARR, OTIF, NRR), and actions (refund, create PO).
- JSON Schemas for every tool‑call and API payload; validate before execution to reduce integration brittleness.
- Versioned metrics definitions to prevent number drift across agents and dashboards.
4) Data quality and reliability
- Contracts at ingestion: types, units, ranges, null rules, and dedup keys.
- Monitors for completeness, timeliness, conformity, and anomaly; auto‑open remediation tasks with owners.
- Unit normalization and dimension alignment (currency, timezone, locale).
5) Real‑time and batch pipelines
- Streaming for low‑latency hints and guardrails (risk flags, quotas, limits, incidents).
- Micro‑batches for drafts, briefs, and small scenario packs; larger batch lanes for re‑indexes and simulations.
- Backpressure, retries with backoff, idempotency, and dead‑letter queues to keep actions safe.
6) Storage and retrieval patterns
- Data lake/warehouse for historical truth and joins; feature store for reusable signals; vector + hybrid search for unstructured grounding.
- Metadata catalog for ownership, sensitivity, retention, and refresh cadence.
- Caches for embeddings, snippets, features, and decisions to cut latency and cost.
7) Governance, privacy, and sovereignty
- SSO/RBAC/ABAC, tenant isolation, row‑level security; consent registry and suppression rules.
- Data residency/VPC or on‑prem inference options; “no training on customer data” by default for sensitive buyers.
- DLP, prompt‑injection, and egress guards for external inputs/outputs; immutable decision logs for audits.
8) Evaluations and drift checks
- Golden evals for grounding/citation coverage, JSON validity, domain correctness, safety/refusals, and fairness parity.
- Data drift and performance drift monitors; regression gates in CI for prompts, retrieval, and schemas.
- Shadow and champion–challenger routes with holdouts to measure incrementality.
9) Observability and unit economics
- Per‑surface SLOs: p95/p99, cache hit, router mix, JSON/action validity, groundedness, reversal/rollback rate.
- Track optimizer cost: token/compute per 1k decisions and cost per successful action by workflow.
- Weekly “what changed” narratives tying data deltas to product outcomes.
How data flows in an AI SaaS “system of action”
- Ingest and normalize: capture events, records, and documents; clean and tag with lineage and sensitivity.
- Ground and retrieve: resolve identity and ACLs; fetch minimal, authoritative snippets with provenance.
- Decide and validate: route small‑first models; plan actions; emit schema‑valid JSON; run policy checks.
- Execute with controls: approvals/maker‑checker, idempotency, change windows, and rollback.
- Learn and improve: log input→evidence→action→outcome; capture overrides, reversals, fairness metrics; update features and evals.
Practical data design principles
- Least data, most signal: ingest what drives decisions; prune noisy fields; prefer structured interfaces for actions.
- Evidence over eloquence: every user‑facing claim should point to sources, timestamps, and uncertainty.
- Safety by default: refusal on low evidence; simulate before apply; never bypass policy gates.
- Version everything: schemas, prompts, models, metrics; keep diffable, reproducible histories.
- Accessibility and inclusivity: store and serve localized copies, reading‑level variants, and accessibility metadata.
Anti‑patterns to avoid
- Chat over a bucket: unpermissioned RAG with stale PDFs and no provenance.
- Free‑text actions: letting models produce untyped payloads that break downstream systems.
- Single “big model” path: no caches, no routing, exploding cost/latency.
- No decision logs: can’t prove outcomes, debug incidents, or pass audits.
- Metric drift: conflicting definitions across teams erode trust.
60‑day data roadmap (template)
- Weeks 1–2: Map sources and ACLs; define semantic layer and decision SLOs; stand up retrieval with provenance and refusal; enable decision logs.
- Weeks 3–4: Ship grounding QA and caches; implement JSON Schemas for two tool‑calls; add data quality monitors and owners.
- Weeks 5–6: Add streaming signals for eligibility/limits; wire approvals, idempotency, and rollback; instrument SLOs and cost per successful action.
- Weeks 7–8: Golden evals and drift monitors; shadow→champion routes; weekly “what changed” linking data deltas to outcomes.
Checklists (copy‑ready)
Data and grounding
- Source catalog with owners, sensitivity, refresh cadence
- Identity/ACL graph; consent and suppression registry
- Permissioned RAG with provenance, freshness, refusal defaults
- Embedding/snippet/feature caches
Schemas and actions
- Semantic layer (entities, metrics, actions) with versions
- JSON Schemas for tools; validators and simulators
- Policy‑as‑code gates; approvals, idempotency, rollback
Quality, evals, and observability
- Ingestion contracts and monitors (timeliness, completeness, conformity)
- Golden evals (grounding, JSON, safety, domain SLOs, fairness)
- SLO dashboards: p95/p99, cache hit, router mix, groundedness, JSON validity, reversal rate
- Cost meters and cost per successful action
Governance and privacy
- SSO/RBAC/ABAC; tenant isolation; row‑level security
- Residency/VPC/on‑prem options; “no training on customer data”
- DLP, prompt‑injection/egress guards; audit exports
Bottom line: Data is the foundation that turns AI from clever text into reliable, governed actions. Invest first in identity‑aware grounding with provenance, a shared semantic layer and schemas for actions, rigorous data quality and evals, and observability tied to unit economics. Do that, and models become interchangeable components in a trustworthy, auditable product that delivers measurable outcomes.