Profiling and lineage review
Column profiles for every source in scope, plus join checks between systems. Profiled from real extracts, not from documentation.
Strategy
Data Readiness Assessment
Check whether your data can support a decision before you fund the work.
GIST stepGround
Download the PDF What you get
A clear recommendation on whether to proceed, fix the data first, or stop.
Get the Data Readiness Assessment overview
The problem it solves, what is included, how it works, the technical components, and how we adapt it with you.
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The problem
Data work gets funded before anyone checks the data.
A use case gets approved on a slide. Weeks into the build, the team finds duplicate customer IDs, a source nobody can access, and fields that are mostly empty. By then the budget is spent and the decision is no better.
What this accelerator does
The Data Readiness Assessment checks the actual data behind one decision first, so you fund the work knowing what is ready and what is not.
What's included
One decision. The real data behind it. A clear answer.
Four parts, each adapted to your data, platforms, and controls.
Readiness checks
Access, ownership, completeness, validity, uniqueness, timeliness, join coverage, and personal data, each against what the decision needs.
Ranked gaps
Every gap ranked by effort to fix and by effect on the decision, then reviewed with the source owners.
Decision Brief and first-increment plan
A draft brief for the Decision Owner to confirm and sign, and a plan for the first build increment.
You keep the assessment configuration, the readiness report, the signed Decision Brief, and the tooling to re-run the checks as fixes land.
How it works
From a decision to a verdict in six steps.
Frame the decision
With the Decision Owner: the decision, who acts on it, how improvement is measured, and what it is worth.
List the sources
For each system: owner, access route, business key, fields needed, required freshness, and joins to other sources.
Get extracts
Profile the real data. A recent full extract or a large sample is enough, or query the database directly.
Agree thresholds
Set pass levels with the Decision Owner before running, so results are not argued after the fact.
Run and review
Walk the ranked gaps with source owners and adjust impact where they know better.
Decide
The Decision Owner signs the brief, or the work stops or waits for fixes, with evidence either way.
Technical detail
Under the hood.
Vendor-neutral Python and configuration, Azure first, with tests included from the start.
- Profiler
- Column types, fill rates, ranges, and top values for each source in scope
- Readiness checks
- Eight check types, scored against thresholds agreed in the assessment file
- Join coverage
- How many keys in one source are found in the sources it must join to
- Privacy handling
- Columns that look like personal data keep counts only, never sample values
- Outputs
- Readiness report, draft Decision Brief, and a JSON record to track readiness over time
- Pipeline gate
- Exits as proceed, fix first, or stop, so the verdict can gate a pipeline
Proof in the package
Worked example: which customers get a retention offer each month.
The decision draws on a CRM, an ERP, and a helpdesk. The sample data carries the problems real projects run into.
Planted in the data
- Duplicate customer IDs in the CRM
- Orders for customers the CRM does not know
- Invalid customer segment values
- A helpdesk extract that is 40 days old
- A source the team cannot access yet
Result
The run finds each problem, ranks the gaps, and recommends fixing the data first. It writes the readiness report, the draft Decision Brief, and a record of every check.
Where it fits
Platforms, related accelerators, and limits.
Works with
- CSV extracts from any source system
- SQLite, or any database through a standard Python connection
- Runs where your data is allowed to be
Pairs with
- Runs first, in the Ground step of GIST
- Duplicate and join gaps feed the Master Data Starter
- Definition gaps feed the Metrics Layer Starter
Assumptions and limits
- Type and personal-data detection use patterns, so source owners review the profiles
- Join checks test keys, not meaning
- Very large tables are profiled from a representative sample
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