📊DataPeek Facts

Data Methodology

Transparency is a core principle at DataPeek Facts. Every number displayed across our 18 specialized platforms traces back to an official federal or state government data source. This page explains how we collect, process, normalize, and present that data so you can evaluate its reliability and make informed decisions.

Data Sources

We rely exclusively on authoritative public-sector datasets. The following agencies and programs serve as our primary data providers:

  • Bureau of Labor Statistics (BLS)— Occupational Employment and Wage Statistics (OEWS) program provides salary and employment data for 800+ job titles across 400+ metropolitan areas, updated annually.
  • U.S. Census Bureau & Bureau of Economic Analysis (BEA)— American Community Survey (ACS) 5-year estimates supply demographic, income, and housing data for 30,000+ ZIP codes. BEA Regional Price Parities power our cost of living comparisons across 380+ metro areas.
  • U.S. Department of Education — College Scorecard— Institution-level data on tuition, acceptance rates, standardized test scores, graduation rates, and post-graduation earnings for 2,700+ accredited colleges and universities.
  • USDA FoodData Central— The USDA's comprehensive food composition database provides nutritional profiles, including macronutrients, micronutrients, and serving size data for thousands of foods.
  • Department of Housing and Urban Development (HUD)— Fair Market Rent (FMR) data for 3,000+ counties and 400+ metropolitan areas, published annually for the upcoming fiscal year.
  • CMS.gov & Medicare.gov— The Centers for Medicare & Medicaid Services publish healthcare cost data, procedure pricing, facility ratings, and coverage information that feeds our healthcare and elder care platforms.
  • U.S. Energy Information Administration (EIA)— State-level electricity rates, consumption data, and energy price trends inform our utility cost tools.
  • National Renewable Energy Laboratory (NREL)— Solar irradiance data, photovoltaic production estimates, and incentive databases power our solar energy calculators.
  • Social Security Administration (SSA)— Baby name frequency data going back to 1880, covering every name registered with at least five occurrences per year in the United States.
  • World Customs Organization & US International Trade Commission — Harmonized System (HS) classification codes and tariff schedules for 10,000+ product categories across 200+ countries.

Data Pipeline: From Source to Screen

Raw government data rarely arrives in a format ready for public consumption. Our four-stage pipeline transforms dense statistical releases into intuitive, searchable tools.

1. Collection

We acquire data directly from official agency websites, public APIs, and bulk download portals. Each dataset is versioned and timestamped at the point of ingestion. We never rely on third-party aggregators as a primary source. When agencies publish updates on known schedules (for example, the BLS annual OEWS release each spring), we refresh our databases within days of the new data becoming available.

2. Processing

Raw files arrive in a variety of formats: CSV, Excel, fixed-width text, XML, and JSON from APIs. Our processing scripts parse each format, validate data integrity by checking for missing values, duplicates, and statistical outliers, and flag any anomalies for manual review. Records that fail validation checks are excluded rather than estimated or interpolated, ensuring that every published data point has a verified source value.

3. Normalization

Government agencies use different geographic classifications (FIPS codes, CBSA codes, ZIP codes, county names), time periods (calendar year vs. fiscal year vs. academic year), and measurement units. We normalize all data to consistent geographic identifiers, standardized time periods, and common units so that cross-dataset comparisons are accurate. For example, when comparing BLS salary data (calendar year) to HUD fair market rents (fiscal year), we clearly indicate the applicable period for each metric.

4. Presentation

Processed data flows into our 18 specialized web platforms, each designed around a specific use case. We present data through searchable tables, comparison tools, calculators, and geographic breakdowns. Every page displays the source agency, data vintage, and any relevant methodology notes. Calculated fields such as cost-of-living indices, solar ROI projections, or student loan repayment estimates are clearly labeled as derived values, with the underlying formula documented.

Data Quality Commitments

Accuracy is non-negotiable. We maintain several practices to safeguard data quality across all platforms:

  • Every dataset includes a visible data vintage indicator so users know exactly which release they are viewing.
  • We do not fill gaps with estimates or projections. If a data point is unavailable for a given location or time period, we state that explicitly rather than publishing a guess.
  • Automated monitoring alerts our team when source agencies publish updated releases, so refresh cycles stay current.
  • All derived calculations (indices, projections, rankings) document their methodology and can be independently verified using the source data.

Limitations

Government data, while rigorous, has inherent limitations. Survey-based estimates (like ACS data) carry margins of error. Annual releases may lag current conditions by 12 to 18 months. Geographic coverage varies by program: some datasets cover every ZIP code while others only report at the metro or state level. We encourage users to consider these factors when making decisions based on the data presented on our platforms.

Questions about our methodology? Contact us at datapeekfacts@gmail.com.