We're the pre-flight check. You're still the pilot.
ADM doesn't fix your data or fly the mission for you — it tells you exactly what to check before you take off. Where sensitive data lives. Where a column is likely to be misread. Where missing data will quietly get filled in with a guess instead of flagged as unknown.
Your dashboards work. That's not the question anymore.
A BI dashboard works because someone already did the hard part — an analyst who knows what a column like src_cd means, which fields to ignore, which numbers to trust. That knowledge lives in a person, not in the data itself.
The moment you let AI query that same data directly — not through a dashboard someone built and tested, but live, on demand — that person isn't standing between the question and the answer anymore. The AI has to interpret the raw column itself, every time, with none of the context your team has built up over years.
The ambiguity was always there. It just never got a chance to matter before.
What You Get
Every assessment also gives you:
A plain-English definition of what your data actually means (Dataset Briefing) — so the next person, or the next AI tool, isn't guessing either. Real starter questions your data can actually answer (Discovery Prompts) — so you're not staring at a spreadsheet wondering where to begin. A full column-by-column data dictionary, ready to export — documentation you didn't have to write yourself.
The table below shows what this catches, domain by domain. This is what you walk away with, every time.
| Domain | Pain Point | What ADM Flags |
|---|---|---|
| Data Governance | Data stewards lack a fast, repeatable way to assess AI-readiness and PII exposure across many datasets before they're approved for AI use | Ambiguous columns, PII/sensitive data location, and quality gaps across any dataset — a standardized pre-flight check to apply consistently, regardless of which team owns the data |
| Pre-Export / Data Sharing Safety Check | A file heading out the door — to a partner, vendor, or AI tool — may carry PII no one meant to include | A last-look scan for sensitive data hiding in a file before it leaves your hands, catching what a manual review might miss |
| Healthcare | Missing vitals or lab values silently treated as normal by AI; PHI reaching a model unmasked | PHI location, ambiguous clinical columns, and null-heavy fields (e.g. missing lab results) before AI fills the gap with a guess |
| Lending & Credit | Gaps in delinquency or income data leading to a false risk picture; regulatory exposure from misread fields | Ambiguous risk/eligibility columns, high-null fields (delinquency, income), and where PII lives before AI-assisted decisioning |
| Insurance | Incomplete claims data producing a misleadingly clean-looking risk assessment | Where sensitive policyholder data lives, ambiguous claims/coverage fields, and null rates that could distort claims analysis |
| Accounting & Finance Ops | Missing transaction detail treated as zero or "no activity" instead of "unknown" | Ambiguous account/category fields and null-heavy columns before they're fed into AI-assisted reporting |
| Sales & CRM | Blank fields (deal stage, close date) skewing pipeline analysis; contact PII exposed | Where contact PII lives, inconsistent categorical fields, and high-null pipeline fields before analysis |
| HR & People Data | Incomplete employee records producing skewed workforce analytics | Where employee PII lives, ambiguous HR fields, and gaps in record completeness |
| Retail & E-commerce | Missing SKU/inventory data leading to wrong demand or trend conclusions | Where customer PII lives, inconsistent product labeling, and null-heavy inventory/transaction fields |
| Supply Chain & Logistics | Missing shipment/tracking data producing false confidence in delivery performance | Ambiguous vendor codes, high-null tracking fields, and inconsistent status values |
| Marketing & Digital Analytics | Gaps in campaign/attribution data producing misleading ROI conclusions | Ambiguous campaign/source codes, inconsistent categorical values, and null rates that undermine attribution |
| Legal & Compliance | Incomplete case metadata leading AI to miss or misclassify key details | Where sensitive entities live, ambiguous metadata fields, and gaps in case/contract records |
| Government & Public Sector | Incomplete constituent records skewing service or eligibility analysis | Where PII lives, ambiguous fields, and null-heavy records before any AI connection |
| Real Estate | Missing listing or transaction detail producing unreliable valuation or trend analysis | Where PII lives, ambiguous listing fields, and gaps in transaction completeness |
Data Governance
Pain Point
Data stewards lack a fast, repeatable way to assess AI-readiness and PII exposure across many datasets before they're approved for AI use
What ADM Flags
Ambiguous columns, PII/sensitive data location, and quality gaps across any dataset — a standardized pre-flight check to apply consistently, regardless of which team owns the data
Pre-Export / Data Sharing Safety Check
Pain Point
A file heading out the door — to a partner, vendor, or AI tool — may carry PII no one meant to include
What ADM Flags
A last-look scan for sensitive data hiding in a file before it leaves your hands, catching what a manual review might miss
Healthcare
Pain Point
Missing vitals or lab values silently treated as normal by AI; PHI reaching a model unmasked
What ADM Flags
PHI location, ambiguous clinical columns, and null-heavy fields (e.g. missing lab results) before AI fills the gap with a guess
Lending & Credit
Pain Point
Gaps in delinquency or income data leading to a false risk picture; regulatory exposure from misread fields
What ADM Flags
Ambiguous risk/eligibility columns, high-null fields (delinquency, income), and where PII lives before AI-assisted decisioning
Insurance
Pain Point
Incomplete claims data producing a misleadingly clean-looking risk assessment
What ADM Flags
Where sensitive policyholder data lives, ambiguous claims/coverage fields, and null rates that could distort claims analysis
Accounting & Finance Ops
Pain Point
Missing transaction detail treated as zero or "no activity" instead of "unknown"
What ADM Flags
Ambiguous account/category fields and null-heavy columns before they're fed into AI-assisted reporting
Sales & CRM
Pain Point
Blank fields (deal stage, close date) skewing pipeline analysis; contact PII exposed
What ADM Flags
Where contact PII lives, inconsistent categorical fields, and high-null pipeline fields before analysis
HR & People Data
Pain Point
Incomplete employee records producing skewed workforce analytics
What ADM Flags
Where employee PII lives, ambiguous HR fields, and gaps in record completeness
Retail & E-commerce
Pain Point
Missing SKU/inventory data leading to wrong demand or trend conclusions
What ADM Flags
Where customer PII lives, inconsistent product labeling, and null-heavy inventory/transaction fields
Supply Chain & Logistics
Pain Point
Missing shipment/tracking data producing false confidence in delivery performance
What ADM Flags
Ambiguous vendor codes, high-null tracking fields, and inconsistent status values
Marketing & Digital Analytics
Pain Point
Gaps in campaign/attribution data producing misleading ROI conclusions
What ADM Flags
Ambiguous campaign/source codes, inconsistent categorical values, and null rates that undermine attribution
Legal & Compliance
Pain Point
Incomplete case metadata leading AI to miss or misclassify key details
What ADM Flags
Where sensitive entities live, ambiguous metadata fields, and gaps in case/contract records
Government & Public Sector
Pain Point
Incomplete constituent records skewing service or eligibility analysis
What ADM Flags
Where PII lives, ambiguous fields, and null-heavy records before any AI connection
Real Estate
Pain Point
Missing listing or transaction detail producing unreliable valuation or trend analysis
What ADM Flags
Where PII lives, ambiguous listing fields, and gaps in transaction completeness
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