In health care data management, data governance and management are fundamental to ensure that data is valid and secured. Through the development of a proper data management and governance plan, it helps in ensuring data quality. The quality of data is examined by timeliness, relevance, context definition, completeness, accuracy and trustworthiness. To create a data management and governance plan (DGMP) for Independence Medical Center, it is critical to consider the concept of enterprise information management, the importance of data governance plan, as well as the relationship between data management and governance.
Concept of Enterprise Information Management
The idea of enterprise information management specializes in recognizing solutions for optimal application of information by supporting the decision-making process requiring knowledge availability. Primarily, EIM associates with business process management (BPM), business intelligence (BI), and enterprise content management (ECM) in that both BI and ECM aid in managing structured and unstructured information.
Importance of a Data Governance Plan
A data governance plan sets a foundation by defining goals, roles and responsibilities, metrics, communication strategies, and also serve as a guiding principle (Cheong and Chang, 2007). First, a data governance plan enhances data quality. Through the implementation of an effective data governance plan, it helps deliver accurate, consistent and complete data. High-quality data is critical in health care to ensure effective data analytics and accessibility. Further, a data governance plan helps with compliance. When dealing with health care data, regulation and standards offer strict requirements for data security. By developing a data governance plan, it enables identification of regulated data and privacy to establish proper controls. Overall, a data governance plan helps in supporting health care operations since the involved knowledge workers can gain insights and make decisions based on the data (Koltay, 2016).
Relationship Between Data Management and Data Governance
In the relationship between data management and data governance, quality of data is the primary objective for both. As data governance involves the organizational structures, policies, metrics, and other processes it directly interacts with data management considering that data management is the technical implementation of data governance. Interchangeably, data governance forms a key component in any data management strategy as it relates to how data is protected and managed as an asset (Koltay, 2016). Most importantly, data management involves the entire data asset lifecycle (Koltay, 2016). In this case, concerning Independence Medical Center, data management could include fields such as data architecture, metadata management, health care intelligence and analytics, data quality management, and data security management. Comparatively, data governance goes beyond information technology while including stakeholders from the organization, and it is possible to overlap with data areas such as compliance, privacy, and integration (Ladley, 2019).
While data governance and data management are distinct entities both in practice and in concept, they are critical to maintaining high and valuable data usability. Their objectives are similar as they create a robust, reliable, and trustworthy data structure that is critical to empower knowledge workers in the organization (Ladley, 2019). In the implementation and relation between both, it is only when proper data identification, classification, architecture, quality control strategies and integration means are developed that a data governance plan can be fully implemented.
Structure of the Data Management and Governance Plan
In the creation of a DGMP for Independence Medical Center, the included sub-disciplines are data architecture, data integration, data intelligence, metadata management, data quality, as well as data security. For the data governance approach to mandate enterprise data management, the data architecture will design and document data structures, practices, and their descriptions, and data integration will manage reusable resources. The data intelligence will ensure necessary data is available for the knowledge workers who will ensure that data management responsibilities are under the data management practitioners rather than the IT workers.
Figure SEQ Figure \* ARABIC 1: Data Management and Governance Plan Structure
The DGMP for Independence Medical Center will be based on the following as shown in figure 1.
Data Governance Council - The members of the data governance council will involve different executives from different departments in Independence Medical Center interested in asset data management. Their responsibilities will include; developing policies, resolving issues, engaging the information technology council, and aligning the health practices with IT goals.
Data Custodian - Managing the DGMP on behalf of Independence Medical Center, the custodian will remain accountable for the asset data quality. Working with the data governance council, the data custodian will ensure data is fit for the intended purpose.
Data Steward- Led by the data custodians, the data steward will translate IT knowledge with the requirements of Independence Medical Center. Also, the stewards will oversee tactical plans and act on behalf of the stakeholders. The data stewards manage users by training and educating them. The data stewards include data integration director, intelligence director, data architect, and Independence Medical Center supervisors.
User Groups - The key data stakeholders include individuals collecting data, process, and reporting off data. Technical staff in the health care centre will consist of the uses. The user groups are responsible for the reporting of data and collecting needed requirements.
In conclusion, data management and data governance play a vital role in many organizations, as developments in information technology have enhanced companies to gather structured and unstructured data.
Cheong, L. K., & Chang, V. (2007). The need for data governance: a case study. ACIS 2007 Proceedings, 100.
Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA Journal, 42(4), 303-312.
Ladley, J. (2019). Data governance: How to design, deploy, and sustain an effective data governance program. Academic Press.
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