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Updated: May 14, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Developing a data governance model in health care.

Mary G Reeves¹, Rita Bowen

¹Vanderbilt University Medical Center, Nashville, TN, USA. mary.reeves@vanderbilt.edu

Healthcare Financial Management : Journal of the Healthcare Financial Management Association

|February 19, 2013

Summary

This summary is machine-generated.

Finance leaders building data governance models should establish leadership, define scope, and calculate return on investment using data confidence metrics. Identifying deficiencies and budgeting for improvements are key steps for successful data governance implementation.

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Last Updated: May 14, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

Area of Science:

Business Administration
Information Science
Financial Management

Background:

Effective data governance is crucial for financial decision-making.
Lack of clear data governance leads to inefficiencies and risks.
Finance leaders require a structured approach to data management.

Purpose of the Study:

To outline key steps for finance leaders in establishing a data governance model.
To provide a framework for measuring the success of data governance initiatives.
To guide the identification and remediation of data-related deficiencies.

Main Methods:

Establishing a dedicated leadership team.
Defining the precise scope and objectives of the data governance program.
Implementing a metric for calculating return on investment based on data confidence.

Main Results:

A clear leadership structure enhances program accountability.
Quantifiable return on investment can be determined through data confidence metrics.
Proactive identification of deficiencies allows for targeted budgeting and resource allocation.

Conclusions:

A systematic approach to data governance is essential for financial organizations.
Leadership commitment and clear scope definition are foundational.
Measuring ROI through data confidence provides a tangible basis for investment in data governance.