Data Quality Syncler-Glossar

"Data Quality" refers to the degree to which data are suitable for their intended use. It is determined by various attributes: accuracy, completeness, reliability, relevance, and timeliness of the data. High-quality data are essential for effective decision-making and operational processes in a company.

How is data quality measured?

  • Accuracy:
    Checking the data for correctness and reliability. Completeness: Assess whether all necessary data are present.
  • Consistency:
    Checking for contradictory information across different datasets and systems.
  • Timeliness:
    Assess whether the data are timely and relevant.
  • Reliability:
    Evaluating the sources of the data and their trustworthiness.

These aspects are measured by various methods, such as regular audits, data quality scoring models, and feedback from users of the data.

Factors that deteriorate data quality:

  • Faulty Data Capture:
    Errors in data entry or transmission.
  • Outdated Information:
    Data that has not been updated and has lost its relevance.
  • Lack of Data Standards:
    Inconsistent data formats and structures, e.g., recording telephone numbers in different formats.
  • Isolated Data Systems:
    Data silos leading to inconsistencies.
  • Inadequate Data Maintenance:
    Lack of data management and insufficient maintenance.

How can data quality be improved?

  • Implementation of Data Standards:
    Uniform formats and structures for data.
  • Regular Data Reviews and Cleanups:
    Continuous checking and updating of data.
  • Training of Employees:
    Awareness and skills for correct data capture and maintenance.
  • Use of Quality Assurance Tools:
    Software tools, like Syncler, that automatically detect and correct errors in the data.
  • Centralized Data Management:
    Use of data integration platforms, like Syncler, to centralize and maintain consistent data flow between applications.

High data quality is especially essential for CRM systems, as it forms the core for marketing, sales, and service activities. Thus, data quality directly affects customer relationships and business decisions.