What is a data assessment deliverable?
A data assessment deliverable is the decision-ready document that turns data findings — scoring, gaps, business impact, remediation priority — into action.
A data assessment deliverable is the decision-ready document you hand someone at the end of a data evaluation. Not a data dump. Not a status update. A document that says what’s wrong, how bad it is, what it costs you, and what to fix first.
Most teams miss the real point. The assessment isn’t the analysis — it’s the decision the analysis lets someone make. You can profile data for three weeks and produce nothing of value if the output is a spreadsheet no one can act on. Frameworks like DAMA-DMBOK and the AWS Migration Readiness Assessment exist to stop that from happening; they define what a rigorous output actually contains. Here’s what belongs in one, whether you’re cleaning up data quality or sizing a platform migration.
What is a data assessment deliverable and what does it contain?
A data assessment deliverable is a structured report that turns a formal evaluation into something a stakeholder can act on. Profiling scripts and raw query output are not deliverables. They are inputs. The deliverable is the conclusion you draw from them.
The shape changes with the assessment. The spine doesn’t. It states what you evaluated, how you scored it, what’s broken, what that costs the business, and what to fix first. Good deliverables make four things explicit: scoring basis, severity, business consequence, and remediation priority. Drop one and the report stops being actionable — downstream teams can’t turn it into work.
A data strategy assessment goes wider: a maturity baseline, a prioritized roadmap, a governance framework, and a decision pack for the executives who sign off. The format scales with the scope. The principle holds at every size.
Never hand a stakeholder a raw data profile and call it a deliverable. A profile shows what the data looks like. A deliverable tells them what to do about it.
What are the types of assessment deliverables?
Two types cover most of the work: a data quality report and a migration readiness report. Same skeleton, different decision. One tells you whether your data is trustworthy. The other tells you whether it can move.
| Component | Data quality report | Migration readiness report |
|---|---|---|
| Data inventory | Field-level catalog with quality scores | Object inventory with complexity ratings |
| Quality scoring | Six dimensions: completeness, uniqueness, consistency, validity, timeliness, accuracy | Automation rate projections by object |
| Gap analysis | Anomalies mapped to business impact | Compatibility gaps between source and target |
| Risk register | Data issues ranked by severity | Migration blockers ranked by effort and risk |
| Remediation plan | Corrective actions with priority and owner | Manual adjustment estimates and timelines |
| Executive summary | Business consequence of data issues | Cost and timeline estimate for migration |
A data quality report scores your data across six dimensions and ties each finding to a business impact and a fix. That pairing is what makes it usable instead of merely interesting.
A migration readiness report quantifies effort. It includes object inventories, complexity ratings, and projected automation rates, plus a risk register and a cost estimate. Each part answers a question someone will ask before approving a budget.
How do data assessment deliverables support data quality evaluation?
A data quality assessment is an audit, not a cleanup. Cleanup fixes the records in front of you. An audit finds the process that keeps producing bad ones — and the deliverable is where that turns into a plan.
We score data quality across six dimensions. They’re the same six our read-only Salesforce data assessment measures, because they decide whether reporting and AI can trust your data:
- Completeness — are the fields you depend on actually filled in?
- Uniqueness — is each real-world thing represented once, with no duplicates?
- Consistency — is the same value written the same way everywhere (“QC” vs “Québec” vs “Quebec”)?
- Validity — does the data follow the formats and rules it’s supposed to?
- Timeliness — is it current, and updated when it needs to be?
- Accuracy — does it match reality?
Each dimension gets a score. Each score points to a consequence. A low timeliness score on contact records means your campaigns reach the wrong people. Writing that consequence down is what gets the fix funded.
A good deliverable also ranks findings by severity. Critical issues block downstream work. Major issues degrade it. Minor issues wait for a scheduled cleanup. That order is the difference between a team that knows where to start and one that argues about it.
"12% of email fields are blank" is trivia. "12% of email fields are blank, which disables onboarding for one in eight new accounts" is a decision. The most common failure in quality reports is listing anomalies without saying what they cost.
What does a migration readiness deliverable look like?
A migration readiness deliverable answers one question: can this data move to the new platform, and at what cost? Everything in it serves that question — source objects, target compatibility, transformation complexity, and the gap between what a tool can automate and what a human has to touch.
A serious one includes:
- Object inventory — every table, field, relationship, and piece of custom logic in the source
- Complexity tiers — simple, moderate, or complex, rated per object
- Automation rate — how much of each object a tool can migrate without hand-coding
- Gap analysis — a field-by-field comparison of source and target, with the mismatches called out
- Risk register — ranked blockers, each with a likelihood, an impact, and a mitigation
- Cost and timeline estimate — built from the automation gap and the manual work left over
Without that quantification, a migration plan is a guess with a Gantt chart. And if the numbers say leaving is the wrong move, a good deliverable says so. When we run the same assessment before a migration, we’d rather tell you to stay than sell you a move you’ll regret.
A risk register that lists "data quality issues" as one bullet is not a risk register. Every risk needs a name, a probability, an impact, and a mitigation. Vague lists give executives nothing to approve or cut.
What are the best practices for creating effective assessment deliverables?
Start with scope, not data. Before anyone profiles a single field, agree on what you’re evaluating, what questions the deliverable has to answer, and what decision it feeds. Most weak deliverables fail here, not in the analysis.
| Stakeholder | Primary question | Deliverable section that answers it |
|---|---|---|
| Executive sponsor | What is the business risk of acting or not acting? | Executive summary with business impact |
| Project manager | What work is required and in what order? | Remediation plan with priority and effort |
| Data engineer | Which fields fail which rules? | Field-level quality scores and gap analysis |
| Finance lead | What will this cost and how long will it take? | Cost and timeline estimate |
Three habits separate deliverables that drive action from ones that sit in a drive:
- Fix the scoring before you score. Define your dimensions and thresholds up front. Change them mid-assessment and you lose the one thing stakeholders need: a result they can trust.
- Lead with one finding. Your executive summary should name the single most important problem and its consequence, in plain language, before any detail.
- Separate artifacts from deliverables. A SQL output is an artifact. A scored, interpreted, prioritized report built from it is the deliverable. Don’t confuse the two.
Governance platforms like Collibra can automate the cataloging and some of the scoring. Useful, but beside the point. The discipline that makes a deliverable worth reading — tying every finding to a decision — is a human call. No tool makes it for you.
Walk the draft past stakeholders before you call it done. A 30-minute review catches a misread expectation early. Finding out the finance lead needed a cost breakdown by domain after you've shipped costs a week, not half an hour.
Key takeaways
A deliverable earns its keep when it connects a score to a consequence to a priority. Findings without that chain are expensive trivia.
| Point | Details |
|---|---|
| Definition of the deliverable | A data assessment deliverable is a decision-ready document, not a raw data profile or status update. |
| Core content requirements | Every deliverable must state scoring basis, severity, business consequence, and remediation priority. |
| Two primary types | Data quality reports and migration readiness reports serve different decisions but share a structured format. |
| Quality evaluation criteria | Completeness, uniqueness, consistency, validity, timeliness, and accuracy — the six dimensions that drive scoring. |
| Best practice for creation | Define scope and evaluation criteria before analysis begins to keep findings consistent and credible. |
FAQ
What is a data assessment deliverable?
A data assessment deliverable is a structured, decision-ready document produced at the end of a formal data evaluation. It presents scored findings, gap analysis, business impact, and remediation priorities in a format stakeholders can act on.
What should a data quality report include?
A data quality report scores data across six dimensions — completeness, uniqueness, consistency, validity, timeliness, and accuracy — and ties each finding to a business impact and a remediation plan. Each finding states its scoring basis, severity, and corrective action.
How is a migration readiness deliverable different from a quality report?
A migration readiness deliverable focuses on quantifying effort and risk for a platform move. It includes object inventories, complexity ratings, automation rate projections, gap analysis, a risk register, and a cost and timeline estimate.
What are the most common mistakes in data assessment deliverables?
The two most common mistakes are confusing intermediate artifacts like raw profiling outputs with final deliverables, and listing findings without connecting them to business consequences. Both errors prevent downstream teams from converting the report into work.
How do I start creating a data assessment deliverable?
Start by aligning with stakeholders on scope and the decisions the deliverable must support. Define your evaluation criteria before analysis begins, then build the report structure around the questions each stakeholder group needs answered.