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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Before profiling data for discovery, confirm that you are allowed to use it, limit exposure of sensitive fields, make sure the sources will remain available, and check that the files can be read. Then turn those checks into a written plan that reflects your priorities and how the data was created.
This second part covers steps 6–10 of a practical data-profiling checklist: regulatory requirements, privacy, availability, usable formats, and planning. The source article was published by DataScienceCentral on September 27, 2022; its guidance is a preparation framework, not current legal advice for a particular jurisdiction.
Step 6: Check regulatory requirements before profiling
Establish what data may be used, for what purpose, and under which jurisdictions before granting analysts access or starting scans. The relevant requirements can depend on the data, its origin, the intended use, and the permissions your organization holds. A general checklist cannot determine whether a specific project is lawful.
- Identify the jurisdictions and rules that may apply to the data and the planned use.
- Confirm whether the organization has authority to access and profile the data for that purpose.
- Ask legal counsel, privacy staff, or other personnel familiar with the relevant jurisdiction to resolve project-specific questions.
Record the approved purpose and any restrictions in the profiling plan so that scope and access decisions follow them.
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Step 7: Protect privacy and constrain access
Identify personal or otherwise sensitive fields and avoid exposing them when they are not needed to answer the discovery question. The DataScienceCentral article names de-identification and user access controls as possible measures, but neither is a universal technical standard or proof of compliance by itself.
- Limit access to the people who need it for the approved work.
- Exclude sensitive or unnecessary columns from profiling where the tool and task allow it.
- Consider de-identification where appropriate, and have privacy or security specialists assess whether the chosen approach suits the data and use.
Google Cloud’s Knowledge Catalog documentation describes configurable column filters for profile scans, including the ability to narrow scanned data. This is a product-specific control, not a substitute for organizational access governance.
Step 8: Confirm sources will be available when needed
Availability is part of data readiness. For each source, find out who controls it, when the team can access it, and how long it is expected to remain available. Coordinate with data management teams so a needed source is not unexpectedly changed, archived, or deleted during profiling.
- List the source owner or team responsible for access.
- Confirm access timing, dependencies, and any expected end date.
- Ask whether planned maintenance, retention rules, migrations, or archival could interrupt the work.
Capture dependencies and timing in the plan; an otherwise suitable dataset cannot support discovery if it is unavailable at the point it is needed.
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Step 9: Validate that files and formats are usable
Check that required files can be opened and read before the analysis schedule depends on them. If a necessary file is corrupt, arrange a repair or identify a suitable alternative source. Treat this as an operational readiness check: a file that exists but cannot be parsed is not ready for profiling.
Where scans run through a particular platform, verify that the platform supports the source and relevant column types. Google Cloud’s documented profiling support is limited to BigQuery, Google Cloud Lakehouse Iceberg REST Catalog, SAP BDC Delta Lake, and Hive tables; its documentation also notes column-type limits for BigQuery. These are Google product limits, not a general definition of which formats can be profiled. Check the current [Google Cloud supported tables and column types documentation](https://cloud.google.com/dataplex/docs/supported-tables-columns) before implementation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 10: Write a profiling plan around priorities and data origins
Use the source inventory, permissions, availability, and readiness checks to define what to profile first and why. Include the discovery questions, source scope, responsible people, timing, privacy constraints, and any format or access dependencies. Plan differently for data produced by different processes: manual entry can have different error patterns from automatically generated data.
Prioritize the work
Start with the sources most relevant to the discovery goal and feasible to access under the approved conditions. State which fields or subsets are in scope and what findings would warrant follow-up. Do not assume that more rows or more columns automatically make a profile more useful.
Choose scan scope and interpret results carefully
Profiling produces statistical descriptions of data, not an automatic verdict on correctness or fitness for a business purpose. Google Cloud documents profile outputs such as null percentages, approximate distinct-value percentages, common values, and numeric summaries that can include average, standard deviation, minimum, quartiles, median, and maximum. Available results depend on column type. Google says approximate values can differ from exact values by 1–2%, so label them as approximate rather than presenting them as exact counts.
Google also documents full-table and incremental scan scope, row and column filters, sampling, on-demand runs, and scheduling for its standard scans. Sampling a smaller portion can reduce runtime and query cost, but it also means the scan examines less data. Filters can narrow the scan, including removing sensitive or unnecessary columns. Confirm current product documentation for implementation details and restrictions.
Use profile statistics to identify patterns that deserve investigation, then define quality checks and evaluate results in business context. Google states: “Data profiling recommends data quality check rules to ensure your data stays reliable.” A profile alone does not establish that the data is correct or suitable for a particular decision.
Evaluate tools against the work you need to do
If a tool is needed, compare it against the plan rather than choosing on the basis of a broad product claim. Useful evaluation criteria include source and format coverage, full versus incremental scans, sampling and approximations, filters for limiting scope, permissions and auditability, scheduling and scan history, outputs and integrations, cost, and whether the intended use is a one-off discovery exercise or ongoing monitoring.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFor example, Google Cloud Knowledge Catalog documentation describes the scan controls and supported sources noted above. DQLabs describes Prizm as a platform that profiles structural metadata and statistical patterns, identifies semantic candidates, and discovers candidate rules; those are vendor claims, not independent test results. Neither example establishes which product is best for a particular organization.
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