Data quality and cleanup
Turn inconsistent geospatial data into reviewable project information.
Give existing collections, incoming vendor deliverables, and production backlogs a defined path through checks, corrections, and acceptance. FieldCraft Workforce coordinates QA resources and corrective work. Tech Maven Geospatial defines technical criteria and methods around the data, intended use, and systems that need to consume the result.
What we work toward
A usable result, with a clear handoff.
- Checks tied to the intended use of the data
- Corrections tracked through a defined review process
- Clear visibility into unresolved issues and acceptance
The work
Make quality requirements part of the work.
Quality depends on the purpose of the data and the evidence needed to trust it. We help enterprise GIS teams review accumulated records and help capture or survey firms prepare consistent deliverables. The scope defines which checks are required, which issues can be corrected, and which need a customer decision. Technical validation and human review work together, producing an organized record of findings, changes, exceptions, and acceptance.
Input assessment
Review dataset structure, supporting information, coverage, and known issues to establish what the collection can support and where the project needs further clarification.
Attribute and schema checks
Compare fields, values, naming, and required records with the agreed schema. Identify missing, inconsistent, duplicate, or unexpected information for correction or review.
Spatial and technical checks
Apply the validation relevant to the selected data and workflow, including agreed reference, geometry, format, completeness, or publication requirements where they are in scope.
Human review
Organize reviewers to assess source evidence, inspect uncertain outputs, and apply project-specific rules that require judgment beyond automated validation.
Cleanup and correction
Coordinate authorized edits, normalization, restructuring, or rework. Preserve the agreed relationship between source material, corrected outputs, and the record of changes.
Acceptance evidence
Prepare results and unresolved exceptions for customer review, with clear status and responsibility for issues that cannot be resolved from the available inputs.
Deliverables
Agree what good looks like.
- A data-quality and review plan
- A prioritized findings and exception record
- Corrected datasets within the agreed scope
- A record of changes and review status
- An acceptance-ready handover package
Scoping inputs
Start with what you know.
- Source data and available supporting documentation
- Required schemas, standards, and intended use
- Known issues and allowed correction rules
- Customer reviewers and acceptance criteria
Delivery approach
A clear path from scope to handoff.
Establish the baseline
01
Inspect representative data and agree the checks, review rules, correction boundaries, and evidence required. Identify dependencies before committing the full queue.
Validate and review
02
Run the agreed technical checks and human review steps. Classify findings so errors, uncertainties, and decisions can follow the appropriate route.
Correct and recheck
03
Apply authorized corrections, coordinate source or vendor rework where needed, and repeat the relevant checks to confirm the changes address identified issues.
Present for acceptance
04
Deliver reviewed data, change records, and remaining exceptions. Make acceptance decisions and any follow-on remediation scope explicit with the customer's designated reviewers.
Questions
Before we start.
Can you start with an older data backlog?
Yes. Existing collections are a core starting point. We assess a representative sample, identify available evidence and constraints, and define a manageable work package around the intended output.
Can you check deliverables from another provider?
Yes. Review can be scoped around customer-appointed vendors or internal teams. The plan defines the criteria, evidence, correction responsibilities, and process for resolving disputed or incomplete submissions.
Does QA guarantee a particular accuracy level?
Acceptance criteria must reflect the source data, intended use, and agreed validation methods. Any accuracy requirement and the evidence needed to assess it are established during scoping rather than assumed.
Connected services
Build the right scope around the work.
Geospatial Asset Tagging & Human Review
Turn imagery, video, and other suitable source collections into structured asset information.
Geospatial Staffing & Managed Teams
Bring GIS technicians, reviewers, asset taggers, data engineers, and project resources into the work that needs them.
Geospatial Data Engineering & Publishing
Connect existing imagery, vectors, point clouds, video-related information, and asset records to useful services and applications.
Start a conversation
Bring one project. Define the next step.
Tell us what you have, what needs to work, and where delivery is getting stuck. We will help define the people, work packages, and technical path around that outcome.
Existing data · New capture · Managed teams · Ongoing support