Databook combines multiple trusted data sources to generate company insights, performance metrics, and benchmarking analyses. These sources include official company disclosures, licensed financial datasets, and Databook-curated intelligence.
This article explains:
Where Databook’s data comes from
How we ensure data quality and comparability
How frequently data is updated
The types of data available in the platform
Principles
Databook’s data platform is built on several principles:
Use primary sources wherever possible — including official company filings, audited financial statements, and verified disclosures.
Standardize data for comparability across companies and time periods.
Combine automated checks with human oversight to maintain data quality.
Refresh data frequently so insights reflect the latest available information.
How we source data
Databook aggregates information from a combination of external data providers and Databook-curated datasets.
Most historical company financial data — such as revenue and EBITDA — ultimately originates from official company documents, including:
SEC filings (e.g. 10-K forms)
Annual reports
Audited company accounts
Some of this information is obtained through licensed datasets from data providers such as S&P Global, while additional data points are extracted directly from verified company documents.
Databook also sources information from datasets that provide coverage of:
executives and leadership teams
market data and pricing information
corporate events and key developments
regulatory filings and investor documents
technographics and business contacts
company strategic priorities and peer groups
How we ensure data quality
Databook operates a data quality program combining automated monitoring and analyst quality assurance.
Databook’s data quality program is built on top of our providers' own data quality programs - for example, S&P Global’s financial data quality initiative, which includes more than 170,000 automated validation checks.
How we ensure data is comparable
Companies report financial data using different reporting calendars and accounting conventions. Databook applies normalization and synchronization methods to ensure fair comparisons.
Financial normalization
Databook normalizes certain forecast metrics so they align with historical financial reporting.
Examples include:
Revenue forecasts
Profitability forecasts
These forecasts are derived from analyst estimates but normalized to ensure comparability with historical “as-reported” company results.
Some adjustments are also made by S&P Global to EBITDA data before it is provided to Databook.
Synchronizing reporting periods
Companies often report financial results using different fiscal calendars.
To ensure accurate comparisons:
Databook synchronizes peer company data to match the target company’s reporting period wherever possible.
For last 12 months / trailing 12 months comparisons, Databook aligns peer data with the target company’s reporting window.
If a peer company has not yet reported results for the latest period, Databook uses the most recent available period.
On the Databook app, users can view the exact time period used for any comparison by hovering over a chart data point.
How often is data updated
Databook aims to refresh company data quickly after new financial disclosures.
Company financial data is typically updated within 6-12 hours of data being available from S&P Global.
Usually this translates to updated data being available within 24 hours of an earnings announcement. However it can take longer for updated financials for smaller companies or those operating outside major markets to be available.
Related insights and metrics also update when competitors release new results that affect comparisons.
Data categories and refresh cadence
Refresh cadence varies depending on data availability.
Firmographic data
Example: company name, description
Source: S&P Global
Refresh: Checked monthly
Industry classification
Example: sector, industry, sub-industry (Global Industry Classification Standard - GICS)
Source: S&P Global / MSCI
Refresh: Checked monthly
Financial fundamentals
Example: revenue, EBITDA, margins
Source: S&P Global financials
Refresh: Checked every 6 hours
Financial estimates
Example: revenue forecasts, margin forecasts
Source: S&P Global analyst consensus estimates
Refresh: Checked every 6 hours
Market data
Example: share prices
Source: S&P Global
Refresh: Checked daily
Key developments
Example: earnings releases, M&A announcements, leadership changes
Source: S&P Global
Refresh: Checked daily
Professionals
Example: executive names, titles and profiles
Source: S&P Global
Refresh: Checked daily
Company filings
Example: 10-K, 10-Q filings
Source: SEC EDGAR
Refresh: Checked daily
Transcripts
Example: earnings call transcripts
Source: S&P Global
Refresh: Checked every 8 hours
Investor documents
Example: investor presentations and annual reports
Source: Databook
Refresh: Frequency of checks varies according to company
Strategic priorities
Source: Databook
Refresh: Checked within 5 business days after reported earnings for covered companies
Executive compensation
Source: Databook
Refresh: Checked annually
Business contacts
Source: SalesIntel
Refresh: Called on demand to answer DatabookAI queries
Technographic data
Source: SalesIntel
Refresh: Checked monthly
Web search signals
Source: Various websearch providers
Refresh: Called on demand to answer DatabookAI queries
More details
For more information about Databook's data sources, see this collection of articles.