Standards and business regulations analysis

Data Governance
Data governance is how a firm regulates data availability, security, and integrity inside its many software applications and database management systems. According to KarkoÅ¡ková (2022), a procedure to ensure data satisfies exact standards and business regulations when it is introduced into a system” is what they mean when they talk about “data governance. KarkoÅ¡ková continues by elaborating on the following: “The ability to exercise control over the management of data assets is made possible for organizations thanks to data governance. This procedure considers the people, processes, and technologies necessary to guarantee that data is suitable for the use to which it will be put. The model that best fits the organization is the decentralized data governance model. This paper discusses the rationale for choosing this model for the organization.
In the past, the well-known data governance model was centralized data. In this case,, the management controlled the data concerning access. However, much has developed in the past decade concerning data and technology. One of the techniques commonly applied by many organizations is cloud computing (Micheli, 2020). This has allowed access to data and information over the internet. This has moved many from the traditional data governance model to the new idea of decentralized systems.
There are several benefits of using the decentralized data governance model. Instead of being forced to rely on a centralized business intelligence team, departments with decentralized ownership of their data can construct their own datasets and reports independently (Micheli, 2020). Some companies accomplish this goal using data mesh, which gives domain owners the ability to independently perform data conversions and analyses. From the ERD diagram, this will be feasible in a decentralized system as there are several tables that each department may require. Therefore, the decentralized system would allow faster and more reliable access.

Another essential importance is that the Individual departments are given the freedom to access the organization’s core datasets (“data mart”), in addition to the information gathered through the tools they use directly; as a result of this structure. The application programming interfaces (API) of currently accessible business and operational solutions, such as Salesforce, Google Ads, and Zuora, offer complete data and activity logs (Micheli et al., 2020). The transmission of data to and from data warehouses that include production data is simplified as a result of this development. With a decentralized data governance model, the organization can use such API and help improve access to data that is vital for appropriate decision-making within an organization. This will be vital for communication and resolution of customer issues.

Because the tool contains all of the analysis, it often ends up serving as the group that employs it as its primary source of truth. But this implies that two separate business divisions, say Sales and Finance, might calculate the same metric to represent monthly active users in different ways and then arrive at contradictory conclusions as a result of their efforts (Micheli et al., 2020). However, with accuracy enhancement, it is possible that the analysis can converge, which is proof of success in the modeling. The analysis would be important to provide insight into different aspects of the business, such as customer satisfaction and many others.
In conclusion, the best data governance model that would best fit the ERD diagram is through the decentralized system. The several tables that concern different people would be efficiently used through the decentralized system. The system would allow the departments to focus on the business that directly impacts them.

References
Karkošková, S. (2022). Data governance model to enhance data quality in financial institutions. Information Systems Management, 1–21. https://doi.org/10.1080/10580530.2022.2042628
Micheli, M. (2020, September 2). “Emerging models of data governance in the age of datafication” publication in Big Data & Society. JRC Science Hub Communities – European Commission. https://ec.europa.eu/jrc/communities/en/community/digitranscope/article/emerging-models-data-governance-age-datafication-publication-big
Micheli, M., Ponti, M., Craglia, M., & Berti Suman, A. (2020). Emerging models of data governance in the age of datafication. Big Data & Society, 7(2), 205395172094808. https://doi.org/10.1177/2053951720948087