In today’s data-driven world, organizations rely heavily on data to make informed decisions, identify trends, and gain a competitive edge. However, not all data is created equal, and certain data elements are more critical than others. Critical data elements (CDEs) are those data elements that are essential to the operation of an organization and have a significant impact on business outcomes.
Why it is necessary to identify critical data elements?
Identifying critical data elements is essential for effective data governance and data quality. Effective data governance requires the identification of critical data elements to ensure that the organization can manage these elements effectively. Ensuring the quality of critical data elements is crucial because decisions made based on inaccurate or incomplete data can have severe consequences for the organization.
How to identify critical data elements?
Identifying critical data elements is a multi-step process that involves collaboration between different stakeholders in the organization. The following steps can help in identifying critical data elements:
Centralize Business Valuable Data
This step involves creating a centralized unified database from data at source. One of the key ways to create is to have an augmented data catalog e.g. DvSum, Collibra of the entire data stack. This will create an intelligent data store of the internal as well as external data and support in identifying CDE at source
Understanding Organization’s Prerogative
This step helps understand if the CDE comes as part of the broader organization strategy including it fits into the vision, it is part of regulatory compliance etc. The next step is to understand organization’s responsibilities and obligations to protect this data. This is where the participation of key stakeholders are necessary to find out organization’s prerogative regarding the usage of data .
Assess Risks to the Data
Assess the Impact and Risk if the data is not governed. For e.g, Checking if the data has impact on other data elements, is it used for Key decision making, if quality is low, does it impact customer satisfaction etc. This will help identifying which CDE are High, Medium and Low in Priority and allow the organization to take action accordingly. Another objective here is to understand what is the currently level of security/assess provided to the CDE to help make plan of action
Prioritize the data elements
Finally, prioritize the critical data elements based on their impact on the organization. This will help in determining the level of attention and resources required to manage these data elements effectively.
How CDE helps accelerate data governance implementation and data quality?
Identifying critical data elements helps in accelerating data governance implementation and data quality in the following ways:
Prioritization: By identifying critical data elements, organizations can prioritize their resources and attention to manage these elements effectively. This helps in ensuring that the most critical data elements are managed with the utmost care and attention.
Consistency: Managing critical data elements in a consistent manner ensures that these elements are accurate, complete, and reliable. This helps in improving data quality and ensuring that decisions made based on this data are accurate and reliable.
Compliance: Managing critical data elements effectively helps in complying with regulatory requirements and industry standards. This helps in avoiding legal and financial penalties that may result from non-compliance.
Collaboration: Identifying critical data elements requires collaboration between different stakeholders in the organization. This collaboration helps in ensuring that the organization has a shared understanding of the critical data elements and the impact of these elements on the organization.
How DvSum can help
DvSum provides a centralized view of an organization’s data ecosystem and enables users to define rules and workflows to manage their CDEs effectively. DvSum’s data governance capabilities allow organizations to monitor the quality of their data in real-time, and identify potential issues before they impact business operations. The platform provides a comprehensive set of data quality metrics and rules that can be customized based on an organization’s specific requirements.
DvSum also supports data lineage tracking, which enables organizations to understand the flow of data across their systems and identify potential risks and compliance issues.
In conclusion, identifying critical data elements is essential for effective data governance and data quality. It helps in prioritizing resources, ensuring consistency, complying with regulations, and promoting collaboration. By identifying and managing critical data elements effectively, organizations can make informed decisions and gain a competitive edge in today’s data-driven world.