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Summary
This summary is machine-generated.

Poor data quality hinders critical business decisions and processes. Organizations must improve data quality and master data management maturity or avoid structured data acquisition to prevent added costs and complexity.

Keywords:
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Area of Science:

  • Business Management
  • Information Science
  • Healthcare Administration

Background:

  • Poor data quality impedes data-driven decision-making and negatively impacts business operations.
  • Many organizations exhibit insufficient maturity in data quality and master data management.
  • Effective data management is crucial for business success and operational efficiency.

Purpose of the Study:

  • To discuss the current maturity levels of data quality and master data management in organizations.
  • To present a management cycle integrating data quality and master data management.
  • To analyze these concepts within the context of hospital benchmarking.

Main Methods:

  • Review of current organizational maturity in data quality and master data management.
  • Development of an integrated management cycle for data quality and master data.
  • Case study application in hospital benchmarking.

Main Results:

  • Identified widespread insufficient maturity in data quality and master data management.
  • Demonstrated the integration of data quality and master data management through a proposed cycle.
  • Highlighted the challenges and benefits of implementing these practices in a healthcare setting.

Conclusions:

  • Organizations with inadequate data quality and master data management should reconsider structured data acquisition.
  • Proper management of data quality and master data is essential to avoid unnecessary expense and complexity.
  • Implementing robust data management strategies is critical for leveraging data effectively in business and healthcare.