Data validation is crucial for both new and experienced users to comprehend fully the information they are working with within the application.

Getting Started with Data Validation

As an analyst, understanding the data being reported is paramount. Our application offers several features to aid in this understanding:

  • Trends Analysis:Utilize the trend object to analyze data trends, such as loads per week or year. This helps confirm that the data matches expectations. 
  • Equipment Types: It's important to note the equipment types currently reported (e.g., Dry Van, Flatbed, Intermodal, Reefer) and recognize excluded types (LTL, Ocean, Parcel, Rail, etc.).
  • Cost Analysis Over Time:Analyzing cost over time offers valuable insights for financial planning and strategy.
  • Recent Data Examination:Leveraging the lanes object to sort and examine recent data can highlight significant trends or outliers that merit further investigation.

Deep Dive into Data

Validating your company’s data involves several practical steps:

  • Verify Expected Records: Ensure the records in the data match your expectations, including metrics like loads per year.
  • Check for Outliers: Identify any significant deviations, such as exceptionally high or low rates, and verify their accuracy.
  • Understand Inclusion and Exclusion: Confirm the types of data included and excluded in reports, such as equipment types and transportation modes.
  • Carrier Names Scrutiny: Scrutinize carrier names to catch any unexpected entries, ensuring no irrelevant data slips through.

Leveraging Built-In Validation Tools

DAT iQ Benchmark includes built-in validation features to streamline the data verification process:

  • Default Views: The default view showcases data deemed clean and reasonable, based on extensive backend testing.
  • Validation Tests: Automated tests check for logical consistency, such as matching cities with states and appropriate distance or rate figures.
  • Legacy and AI Model Enhancements: For legacy customers, familiar tests are still in place, complemented by improved tests from newer AI models, offering a layered approach to data validation.

Addressing Data Outliers

Upon detecting outliers, users can:

  • Update Page Views: A quick update can reveal outliers in the current dataset.
  • Analyze and Report:Download detailed spreadsheets for a deeper analysis of outliers, including explanations and comparisons to benchmarks