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Data QA for Non-Spatial Data with FME

Tandra Geraedts
Tandra Geraedts
  • Updated

Introduction

This tutorial introduces how FME supports data quality assurance (QA) for non-spatial data. While spatial QA is well-established, many organizations also require reliable methods to validate tabular, structured, and API-based data, including CSV files, spreadsheets, JSON responses, and database tables.

You’ll learn how common non-spatial QA concepts—such as data cleaning, validation, business rule enforcement, and schema compliance—map directly to FME workflows, transformers, and automations.

By the end of this tutorial, you will understand how to:

  • Validate non-spatial data automatically
  • Enforce business rules and consistency checks
  • Perform schema compliance testing using structured definitions
  • Generate repeatable QA reports (Excel, CSV, HTML)
  • Automate data testing using FME Flow

Understanding Non-Spatial Data QA in FME Terms

Data Quality Assurance (QA): Processes that ensure data is accurate, complete, consistent, and fit for use. Workflows that test incoming data against defined rules before it is stored, shared, or consumed downstream.

Data Cleaning: Correcting or standardizing values. Transformers that normalize formats, fix common errors, and standardize values.

Data Validation: Checking whether data meets expected rules. Attribute-based tests that flag or separate invalid records.

Automated Data Testing: Running the same validation rules every time data is processed. Scheduled or triggered QA workflows in FME Flow.

Business Rule Validation: Ensuring data follows organizational logic. This is done through conditional tests such as:

  • If a status is “Active,” required fields must be populated
  • Values must fall within approved code lists
  • Certain attribute combinations must be unique

Schema Compliance: Ensuring data structure matches expectations. Validating attribute names, types, required fields, and structure.

FME Capabilities for Non-Spatial Data QA

Validating Attributes and Values

Attribute and value validation ensures that individual fields meet basic quality expectations, such as being populated, correctly typed, and within acceptable ranges. These checks identify common issues, such as missing identifiers, invalid dates, or incorrect codes, before the data is used downstream in other systems or workflows. Validating attributes early helps prevent errors from propagating into reports, integrations, or automated workflows.

FME can automatically validate:

  • Required vs optional fields
  • Data types (string, number, date, boolean)
  • Value ranges
  • Null or empty values

This is commonly used for CSV files, spreadsheets, database tables, and API responses before loading data into analytics or operational systems.

Learn More:

Customer Story:

North Ayrshire Council utilizes automated validation and synchronization of enterprise data across HR and business systems to ensure consistent and reliable records. 

Enforcing Business Rules

Business rule validation ensures data follows organizational logic, not just structural rules. These checks often involve relationships between multiple attributes, such as conditional requirements or uniqueness constraints. Automating business rules helps apply logic consistently and prevents invalid data from entering production systems.

Non-spatial QA often depends on rules that span multiple attributes.

Examples include:

  • Conditional dependencies between fields
  • Logical checks across records
  • Temporal rules involving dates
  • Uniqueness constraints

FME workflows can apply these rules consistently, routing failing records for review or reporting while allowing valid data to continue downstream.

Learn More:

Customer Story:

Balfour Beatty VINCI uses FME Flow Apps to enforce data standards and business rules across vendor submissions, ensuring consistent and validated project data. 

Schema Compliance Using Structured Definitions

Schema compliance ensures that incoming data conforms to an expected structure, including field names, data types, and required attributes. This is especially important when integrating data from external systems or standardized exchanges. Validating against structured definitions helps detect breaking changes early and reduces the likelihood of integration failures.

FME can validate incoming data against known schemas, including:

  • JSON Schema
  • YAML-based schema definitions
  • Expected database schemas

This is commonly used for:

  • API integrations
  • System-to-system data exchanges
  • Standardized reporting formats

Schema checks help detect missing fields, unexpected attributes, and incompatible data types early in the process.

Learn More:

Customer Story:

Portland Public Schools aligned disparate CAD, GIS, and TRIRIGA schemas into a single authoritative structure, supporting consistency and reliable analytics.

Automated QA for JSON and API Data

JSON and API-based data sources can evolve over time, introducing new fields or unexpected structures. Automated QA ensures responses remain complete, correctly structured, and usable as dependencies change. Validating API responses helps detect issues before they impact downstream processes.

For non-spatial APIs, FME supports:

  • Parsing nested JSON structures
  • Validating arrays and objects
  • Verifying required fields and formats
  • Detecting malformed or incomplete responses

These QA checks can be automated and scheduled, ensuring data from external services remains reliable over time.

Learn More:

Customer Story:

The New Zealand Transport Agency (Waka Kotahi) automates the processing and validation of survey and operational data feeds to ensure timely and reliable downstream use.

Generating QA Reports

QA reports provide visibility into data quality by documenting which checks were applied and which records failed to meet the criteria. Clear reporting helps stakeholders understand, prioritize, and resolve data issues efficiently. Generating reports also supports auditing and continuous improvement of QA processes.

FME can automatically generate QA outputs such as:

  • Excel reports summarizing errors and rule failures
  • CSV files listing invalid records
  • HTML summaries for quick review

These reports are useful for:

  • Data stewards
  • Analysts
  • External data providers
  • Audit and compliance processes

Learn More:

Customer Story:

The Ohio Department of Transportation (ODOT) generated repeatable QA outputs as part of automated validation workflows, improving data quality and reporting efficiency.

Automating Non-Spatial Data QA with FME Flow

FME Flow enables QA workflows to run automatically in various ways to suit your needs. Workspaces can be set to run on a schedule, and automations can be created to run when files arrive. Automated QA ensures consistent enforcement of rules, early detection of issues. It also enables data quality checks to be audited.

Learn More:

Customer Story:

Auckland Council automated validation and packaging workflows reduced turnaround time while ensuring data requests met quality and approval requirements.

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