47 assessments completed · 38+ enterprises served · average 2-week delivery
Get a complete AI data readiness assessment before you build
See exactly where your data is ready and where it isn't. We deliver a structured, engineering-ready action plan in 2 weeks, before you commit budget to a build.
Submit briefcall within 48 hoursassessment plan in 3 daysSprint 1 within 1 week of sign-off
Your team's time commitment across the full 2-week assessment is typically 4 to 6 hours total: one kickoff call, one data walkthrough, and a final debrief. We handle all analysis and documentation.
Assessment sprint
2
week sprint-ready action plan
Your report includes
- Data source registry
- Quality scorecard per dataset
- Pipeline architecture diagram
- Governance readiness matrix
- Prioritized action list for your use case
The data problem nobody talks about
The cost of skipping a data readiness check
Teams that start building AI without assessing their data spend 30 to 50 percent of project budget on rework. Here is what that pattern looks like compared to starting with a clear data assessment.
Without an assessment
- AI project starts with unmapped data sources and unknown quality
- Critical data gaps surface 3 sprints in, forcing scope changes
- Data engineers rebuild pipelines mid-build, burning 6 to 10 weeks
- AI models trained on inconsistent, incomplete data underperform expectations
- Project overruns budget by $40,000 or more in avoidable rework costs
After a data readiness assessment
- Full inventory of data sources, schemas, and access paths before Sprint 1
- Every data gap documented and prioritized before any development starts
- Pipelines validated and documented so engineers build on a solid foundation
- Clean, correctly labeled data available on day one of your AI build
- Assessment costs a fraction of one wasted sprint, saving weeks of delivery time
Interactive assessment
Score your AI data readiness across 5 dimensions
Work through the items below. Your readiness score updates in real time. This is the same framework our consultants use on day one of every engagement.
Data readiness score0%
Needs foundationPartial readinessReady to build
Dimension 1
Data Availability
0 / 4 items
1
AI-relevant data is stored in accessible systems, not only spreadsheets2
Data can be queried via application programming interface or direct database connection3
Key entities (customers, products, transactions) are in structured form4
At least 12 months of historical data exists for priority processesDimension 2
Data Quality
0 / 4 items
1
Critical fields have fewer than 10% null or missing values2
Duplicate records are identified and have a deduplication process3
Data schemas are consistent across source systems4
Field definitions are documented and agreed on across teamsDimension 3
Infrastructure
0 / 4 items
1
A data warehouse or data lake is in place and actively used2
Data flows from source systems into a central store automatically3
Version control or change tracking exists for data pipelines4
A sandbox or staging environment is available for AI experimentsDimension 4
Governance
0 / 4 items
1
Each data source has a named owner and point of contact2
Data access controls and role-based policies are documented3
Data usage complies with GDPR, CCPA, or applicable regulations4
A data lineage or audit trail is maintained for key datasetsDimension 5
AI Enablement
0 / 4 items
1
Labeled datasets exist or can be produced within 4 weeks2
The data team has experience with feature engineering or machine learning pipelines3
A model serving or inference layer exists (or a vendor-neutral option is being evaluated)4
Data and engineering teams can dedicate time to an AI sprintYour readiness snapshot
0 of 20 checkpoints complete
Five dimensions in this checklist
- Data availability
- Data quality
- Infrastructure
- Governance
- AI enablement
Select any item in the cards to update your score and unlock a tailored recommendation with a direct next step.
This checklist is a directional self-assessment. A full engagement goes deeper into each dimension with hands-on data profiling, pipeline testing, and stakeholder interviews.
What the assessment covers
Four structured work streams. One sprint-ready report.
Work stream 01
Data source inventory and access mapping
We enumerate every data source relevant to your AI use case. Every table, application programming interface, file store, and event stream gets catalogued with schema, freshness, access path, and volume.
You receive a structured asset register your engineering team can use from day one of development, not a slide with bullet points.
Source NameTypeAccessStatus
Customer transactions DBPostgreSQLDirect SQLReady
Customer relationship management event logREST APIAPI keyReady
Warehouse inventory feedSFTP CSVManualGap found
Enterprise resource planning purchase historyODBCVPN onlyBlocked
Web clickstream (GA4)BigQuery exportSA keyReady
3 ready · 1 gap · 1 blockedAction plan attached
Completeness91%
Consistency78%
Uniqueness96%
Timeliness62%
Timeliness gap: last 4 months of data has 38% late-arrival records. Fix recommended before model training.
Work stream 02
Data quality profiling and scoring
We profile every priority dataset against four dimensions: completeness, consistency, uniqueness, and timeliness. Every issue is given a severity rating and a remediation recommendation.
You know which datasets are safe to use today and which need targeted cleaning before your AI build can rely on them.
Work stream 03
Pipeline architecture review and gap analysis
We map how data moves from source to storage to consumption. We identify missing pipeline stages, transformation bottlenecks, and latency issues that would block your AI use case.
You get a visual pipeline map and a prioritized list of infrastructure changes, sorted by impact on your target AI deliverable.
Customer relationship management to warehouse ingestion
Automated nightly, schema validated, latency under 2 hours
Transaction log streaming
Kafka topic active, consuming reliably
Feature transformation layer
Gap: no dbt or Spark job converts raw events to features
Feature store
Missing: no reusable feature registry for AI model training
Enterprise resource planning to warehouse pipeline
Missing: enterprise resource planning data only available via manual export (critical gap)
2 critical gaps · 1 optimization needed · action plan generated
| Control area | Status | Action |
|---|---|---|
| Data ownership registry | Active | None |
| Access control policy | Partial | Expand to AI layers |
| GDPR / CCPA compliance | Active | None |
| Data lineage tracking | Missing | Implement dbt docs |
| AI bias / fairness audit | Not started | Add to Sprint 2 |
Work stream 04
Governance and compliance readiness mapping
We audit your data governance posture against AI deployment requirements: ownership, access controls, lineage, GDPR or CCPA compliance, and bias risk surface.
You get a governance matrix showing what is in place, what blocks AI deployment, and what can be resolved before or alongside the build.
Assessment fee: $4,500 flat · 2-week sprint · Full written report delivered
One undiscovered pipeline gap can cost 4 to 6 weeks of rework. At typical development rates that is $40,000 or more. This assessment costs a fraction of one wasted sprint.
Architecture depth
Your data pipeline, visualized in the report
Every assessment delivers a visual pipeline diagram showing where your data flows, where it stalls, and where it connects to your AI use case. This is the diagram your engineers build from.
Pipeline active
Gap identified
Blocked
Missing stage
Proof of delivery
What data readiness unlocks in practice
Client
Enterprise apparel and retail organization
Apparel & RetailA global apparel and retail enterprise operating across multiple enterprise resource planning systems with fragmented customer analytics and revenue operations.
The problem
Customer payment behavior data was fragmented across three enterprise resource planning systems with no unified view. Revenue recovery efforts were reactive and poorly targeted because no one knew which accounts were high risk until it was too late.
Reporting took 40 hours per cycle because data was manually reconciled across sources. Teams lacked confidence in the numbers they were using to make decisions.
The result
0
%improvement in
targeting effectiveness
After a structured data readiness assessment and centralized analytics build, the team reduced reporting time by 40% and achieved a 35% improvement in recovery targeting accuracy.
40% faster reportingUnified data modelAI-ready pipelines
Why Redefine
Why enterprise teams choose us over generalist consultancies
Redefine
Hands-on data profiling
We run SQL, profile schemas, and pull samples. You get numbers, not opinions.
Use-case specific scope
We assess data against your specific AI use case, not a generic maturity framework.
Sprint-ready deliverable
The report includes an action-sorted issue list your team can execute from immediately.
2-week delivery, flat fee
$4,500 flat, always completed in 2 weeks. No billable-hour surprises.
Assessment leads directly to build
Teams that complete the assessment can roll straight into an AI data engineering sprint without re-scoping.
Typical large consultancy
Maturity model presentations
Abstract frameworks and heat maps. Rarely any hands-on data work in the assessment itself.
Generic data strategy scope
Assessments evaluate the whole data estate. The output isn't tied to your specific AI goal.
Recommendations decks only
Slide-based output. Difficult to translate into a development backlog without further work.
6 to 12 week timelines
Enterprise delivery pace. Often still billable-hour pricing with scope creep risk.
Assessment is a separate engagement
The assessment is sold separately from the build. You re-scope and re-staff before anything gets built.
Common questions
Questions about the assessment
Answered directly. No pitch language.
An AI data readiness assessment is a structured 2-week sprint that evaluates whether your current data infrastructure can support a specific AI use case. We examine your data sources, quality, pipelines, governance posture, and AI enablement signals. You receive a written report that tells you exactly what is ready, what needs fixing, and in what order to act.
A general AI readiness assessment covers your organization's overall preparedness for AI: technology, people, processes, and strategy. This assessment focuses specifically on your data: sources, quality, pipelines, and governance. The output is an engineering-ready action list rather than a strategic roadmap.
We cover any data source relevant to your target AI use case: relational databases, NoSQL stores, data warehouses (Snowflake, BigQuery, Databricks), streaming platforms (Kafka, Pub/Sub), customer relationship management and enterprise resource planning exports, file stores, and external application programming interface feeds. If you aren't sure which sources are relevant, we help you identify them in the kickoff call. See our AI data engineering services for full infrastructure support.
You receive a written assessment report including: a full data source registry, a quality scorecard per dataset, a visual pipeline architecture diagram, a governance readiness matrix, and a prioritized action list sorted by impact on your AI use case. Every finding includes a specific, actionable recommendation your engineering team can begin executing immediately.
Yes. Teams that complete the assessment can roll directly into an AI data engineering sprint to resolve the gaps identified. Because the assessment produces a structured action list, there is no re-scoping phase. You continue immediately with the same team on a clear build plan. This is how we collapse the time between assessment and production AI delivery.
Not at all. Many teams run this assessment after Sprint 1 or 2 when they suspect data issues are limiting model performance. A mid-build data assessment identifies exactly which data problems are degrading your outputs and gives you a clear remediation path. See our AI services frequently asked questions for more delivery questions.
Fit check
Is this assessment right for you?
Good fit
- You have a specific AI use case in mind and need to know if your data can support it
- Your organization has data in multiple systems that has never been fully mapped or profiled
- Leadership is asking for proof that data is ready before approving an AI build budget
- A previous AI project stalled because data wasn't ready and you want to avoid repeating that
- You want to connect this assessment directly to an AI data engineering or AI consulting sprint
Not a fit
- You have no specific AI use case yet and need strategy and discovery work first. Start with an AI discovery workshop.
- You need governance, risk, and compliance evaluation across your entire AI program. See our AI governance and risk assessment.
- You have no data at all and need greenfield data collection and infrastructure built first
- You need a broad enterprise data strategy engagement covering all business units and systems
Not sure? Tell us your situation and we'll be straight with you.
Book your assessment
Know what your data needs before you build
Submit your brief and get a scoped assessment plan within 3 business days. No commitment. No pitch. Just a clear picture of your data and a sprint-ready action list.
Assessment fee: $4,500 flat · 2-week sprint · Full report delivered
Submit your brief
Call within 48 hours · Assessment plan in 3 days · Sprint 1 within 1 week of sign-off
Brief received
We'll review your data situation and send a scoped assessment plan within 3 business days. Expect a call from our team within 48 hours.
48 hours
Response time
3 days
Assessment plan
47+
Assessments done
100%
Data ownership yours