Limitations and Practical Applications of Corporate Databases

Understanding the Potential and Limitations of Databases

Limitations and Practical Applications of Corporate Databases

Intrinsic Limitations of Data

Corporate databases have fundamental and unavoidable limitations. It is essential for users of these databases to avoid the illusion of data completeness and to recognize the dynamic nature of data.

  1. Challenges of Real-Time Data
    ・Data becomes “past” information the moment it is created or recorded.
    ・The status of a company is constantly in flux and can change within seconds.
  2. Incompleteness of Information
    ・A 100% accurate corporate database does not exist anywhere in the world. This is because recorded data represents the “past” at the time of recording and may differ from the “present.” Moreover, the pace of change often outstrips the frequency of data updates.
  3. Data Freshness
    ・Corporate information can change instantly due to factors such as:
     - Executive changes
     - Organizational restructuring
    – Mergers and acquisitions
     - Business expansion or downsizing
     - Relocation
     - Fluctuations in financial conditions
     - etc.

A Database is a “Snapshot,” Not a “Live Stream”!

Limitations and Uncertainties of Public Databases

Sources of Potential Errors in Public Databases

  1. Human Input Errors
    ・Simple entry or recording mistakes made by registrants
    ・Errors in numbers or text during transcription
    ・Inaccuracies caused by carelessness
  2. Ambiguity in Industry Classification
    ・Complex business activities of companies
    ・Inconsistencies with standard industrial classifications
    ・Delayed adaptation to rapidly evolving business models
  3. Limitations of Self-Reported Information
    ・Reliability of information provided by companies themselves
    ・Deliberate omission or embellishment of data
    ・Delays in updating to the most current information

Examples of Errors

  • Misrecorded Employee Numbers … Employee counts are overstated or understated, leading to misrepresentation of company size and capabilities.
  • Inaccurate Revenue Reporting … Revenue figures are reported incorrectly, either due to calculation errors or intentional inflation/deflation.
  • Incomplete Business Descriptions … Business activities are partially described, omitting key sectors or services, causing misunderstandings about the company’s operations.
  • Outdated Contact Information … Phone numbers, email addresses, or physical addresses remain unchanged despite relocations or personnel changes, making communication difficult.
  • etc.

Public Databases Are Not a “Source of Truth” but a “Compilation of Reference Information.”

Regional Disparities in Data Quality

Overseas corporate databases consolidate business data from around the world. However, the accuracy and quality of the data vary depending on the country or region.

Factors Influencing Data Quality

  1. Stages of Economic Development
    ・Developed Countries: Advanced information management systems.
    ・Developing Countries: Underdeveloped infrastructure for data collection and management.
  2. Business Culture and Transparency
    ・Differences in cultural attitudes toward information disclosure.
    ・Regional disparities in the level of corporate information openness.
  3. Legal and Institutional Environment
    ・Maturity of data protection laws.
    ・Variations in regulations governing corporate information disclosure.

Characteristics of Data Quality by Region (General Overview)

  1. North America & Western Europe
    ・High-quality and highly reliable data.
    ・Strict information disclosure standards.
    ・Advanced electronic information management systems.
  2. Japan
    ・Sophisticated information management systems.
    ・Detailed corporate data availability.
    ・Information disclosure with careful consideration of privacy.
  3. China
    ・Rapidly developing data infrastructure.
    ・Strong government influence on information management.
    ・Transparency issues with information on unlisted companies.
  4. Southeast Asia
    ・Significant variations in data quality across countries.
    ・Information gaps between urban and rural areas.
    ・Differences in the degree of digitalization progress.
  5. Middle East & Africa
    ・Underdeveloped data infrastructure.
    ・Information quality varies greatly across regions and countries.
    ・Prevalence of informal economic sectors impacts data reliability.

Global and overseas corporate databases are not “uniform” but rather resemble a “mosaic.”

Strategic Thinking for Leveraging Global Data

Approaches (Mindset) for Wise Utilization

  • Treat Data as “Reference Information,” Not “Absolute Truth”
    Use data as a tool to inform decision-making rather than relying on it as an infallible source.
  • Leverage Multiple Sources and Cross-Check Information
    Avoid dependence on a single database by combining and verifying data from various sources.
  • Regular Data Updates and Validation
    Ensure data remains accurate and relevant through periodic updates and thorough verification.
  • Access to the Latest Information and Direct Verification
    Prioritize obtaining up-to-date information, update records after use, and verify details directly when possible.
  • Understand Regional Characteristics
    Develop a deep understanding of the business culture in each country or region and interpret information within its contextual framework.

Recommended Complementary Approaches

  1. Utilizing Databases as Foundational Information
    Treat database information as a starting point for deeper insights and analyses.
  2. Supplementing with Direct Information Collection
    Conduct direct outreach to companies or stakeholders to gather additional, precise details.
  3. Leveraging Real-Time Information Sources
    Use live updates from news outlets, corporate websites, and other dynamic sources to stay informed about recent developments.
  4. Accessing Updates Through Networks and Connections
    Rely on professional networks and trusted contacts to obtain timely and nuanced information not readily available in public databases.