Methodology of Data Compilation and Verification

 

      We provide the most accurate and valid data to ensure that all our best weapons are working at every stage of this strategic data compilation and validation.

Level 1: Data Collection

  • Our own AI and the manual efforts of our data specialists help gather technology, technology, demographic and demographic specific primary data
  • Information about technology use is gathered from trade association members, user group communities, technology-specific working groups, technology magazine subscriptions, user group communities, technical blogs, rights, technical specialties, case studies, recommendations, recommended customers and white papers, and more.
  • Publicly accessible social media and other portals looking for relevant primary data.

 

Level 2: Data Standardization

  • All data is cut into useful data attributes, which are then standardized for important attributes related to technological intelligence.
  • All the attributes are named and categorized based on their relevance. 

Level 3: Data Verification

This stage requires multiple teams to work in synergy.

  • Job Verification: Company job titles are verified through social media and company directories.
  • Contact information: Contact information is verified by email or phone.
  • Company Reviews: Company data is verified by tapping each topic in the data file.
  • Technology Overview: Technology Overview and Technology Landscape are reviewed for each business. This is a manual and automatic process. For manual processing, it is referred to the company with a specific product according to the script. For automated technology that is mostly run in web-based applications, it is run by the technology review team using our own AI-based mechanism. This mainly includes the web technology stack.

Level 4: Data Segmentation

The data verified is then segmented based on:

  • Technology type used
  • Technology subcategory used
  • Technology/Application name used
  • Technology Stack
  • Country
  • Title
  • Department
  • State
  • City
  • Size of the company
  • Revenue
  • Industry
  • Employee Size
  • Zip Codes
  • SIC Codes
  • DUNS

    Level 5: Data Enhancement

    Additional attributes are added to the file. The source of the branch attribute is the parent attribute. For example, the SIC code is derived from a certain industry, the department is derived from the position, the type of technology and the subcategory that is derived from the name of the technology / application used.

    When new employees at a data company join or replace existing employees, they are added to the master file to increase data power.

     When new employees at a data company join or replace existing employees, they are added to the master file to increase data power.