Data Readiness Services
Launch AI with confidence. Fix your data first.
Most AI initiatives fail quietly—not because models can't perform, but because the underlying data isn't trustworthy, accessible, or compliant. Sortis AI's Data Readiness Services give you a clear, objective picture of where you stand and a prioritized plan to get production‑ready fast.
Why data readiness matters (in plain terms)
AI only performs as well as the data it's fed. If your training and operational data are incomplete, inconsistent, siloed, or poorly governed, you'll see:
Delays
Projects stall during integration, labeling, or cleanup
Cost overruns
Unexpected rework and cloud waste
Compliance risk
Unclear lineage, consent, or access controls
Poor outcomes
Models that drift, bias that creeps in, dashboards that contradict
Data readiness is the discipline of making sure your data is fit for AI—reliable, well‑governed, documented, and fast to access—before you invest in models and apps.
What you get from a Sortis Data Readiness Check
We deliver actionable outcomes—not just a slide deck.
Readiness Score (0–100) & heatmap
A single number and category‑level scores you can share with leadership.
Risk register
Specific risks with likelihood/impact ratings and mitigation steps.
Prioritized roadmap (90‑day + 6‑month)
Quick wins to unlock near‑term value and a sequenced plan for durable foundations.
Architecture & governance recommendations
Right‑sized patterns for ingestion, storage, metadata, lineage, access, and privacy.
Backlog of fixes
JIRA‑ready user stories and acceptance criteria to move immediately.
Executive brief
Plain‑English summary of business impact, investments, and expected payoffs.
The Sortis Data Readiness Scorecard
We assess eight dimensions and tailor depth to your stack.
| Dimension | What we examine |
|---|---|
| Data Quality | Completeness, accuracy, timeliness, deduplication, PII handling in raw vs. curated zones |
| Availability & Access | SLOs, access patterns, latency, catalog coverage, self‑service vs. ticket‑driven |
| Lineage & Metadata | End‑to‑end lineage, column‑level impact, ownership, semantics, data contracts |
| Pipelines & Reliability | Orchestration, tests (unit/data), observability, incident response, backfills |
| Governance & Compliance | Policies, consent, retention, DPIAs, RBAC/ABAC, auditability |
| Security | Network posture, secrets, key management, tokenization, least privilege |
| Model/AI Readiness | Feature stores, labeling, drift/monitoring, feedback loops, MLOps maturity |
| Cost & Efficiency | Storage/compute patterns, tiering, query efficiency, lifecycle policies |
Scoring bands: 0–39 (At Risk), 40–69 (Emerging), 70–84 (Solid), 85–100 (Enterprise‑grade)
How our engagement works
Discover
Workshops with data, platform, and security leads; scoped read‑only access where needed; artifact review.
Diagnose
Automated checks + targeted interviews; sample profiling; lineage tracing; governance walkthroughs.
Deliver
Score, risks, and roadmap presented live; working backlogs and architecture notes your teams can execute immediately.
Who should be involved: Head of Data/AI, Platform Engineering, Security/GRC, and the business owners for your first AI use cases.
Benefits you can take to the bank
When to run a Data Readiness Check
You're about to fund an AI/ML initiative (chatbots, copilots, personalization, forecasting)
You're migrating to a new warehouse/lakehouse or consolidating after M&A
You have multiple 'sources of truth' or frequent data incidents
You need to demonstrate compliance posture for AI use
What makes Sortis AI different
Pragmatic, platform‑agnostic advice
We design to your tools and constraints
Action‑ready deliverables
Roadmaps and backlogs, not just findings
Security‑first
Minimal required access, least‑privilege reviews, and privacy‑by‑design guidance
Business alignment
Every recommendation ties to a use case and KPI
Example outcomes (anonymized)
Digital commerce
Unified product and events data, implemented data tests and contracts; enablement for a real‑time recommendation POC.
B2B SaaS
Introduced feature store patterns and drift monitoring; cut model retraining failures; clarified ownership for top 20 tables.
Financial services
Improved consent tracking and lineage; tightened PII access; unblocked generative‑AI assistant pilot.
Frequently Asked Questions
What is 'data readiness,' exactly?
It's the measurable state of being able to use your data safely and effectively for AI: quality, access, governance, and reliability aligned to your first use cases.
Do you need production data?
We aim for the least‑privileged approach. Often metadata, samples, and read‑only views are enough.
Will you sign an NDA?
Yes—standard mutual NDAs are supported.
What tools do you require?
None. We adapt to your stack (cloud warehouse, lakehouse, on‑prem). We can also recommend sensible defaults if you're still choosing.
Does this include remediation?
The core service delivers score, risks, and roadmap with implementation‑ready tickets. Many clients ask Sortis AI to help execute the plan—happy to support.
How much time will my team need to invest?
Expect a handful of short workshops and timely access to artifacts; we do the heavy lifting.
Get your Data Readiness Score
We'll review your environment and send a clear score, risks, and a prioritized plan.