While data compliance is the practice of organizations ensuring that all sensitive data is managed and organized in a way that enables them to meet their business rules alongside legal and governmental regulations, data governance involves the process of managing organizational data’s usability, security, availability, and quality using the internally set rules and policies.
Data compliance pertains to the privacy of personal information and how businesses and organizations store, retrieve, and secure this sensitive data. Organizations and businesses, especially those that work with personal information, are responsible for safeguarding this personal data.
Data governance looks at creating an environment where data can be effectively used for useful insights that enhance business processes. Data governance is considered a must for any organization seeking to use its data to draw insights after analysis. Without data governance, data fails to meet regulations and the quality standards needed to extract usable insights. The data also risks being exposed to security threats that would compromise its integrity. This puts the business or organization at the risk of being in breach of the set standards.
How do the principles of data governance compare with those of data compliance?
|Data Governance Principles||Data Compliance Principles|
|All decisions, processes and controls that relate to data governance must be auditable. These must also be accompanied by proper documentation that supports the audit requirements.||Lawfulness, fairness and transparency:
This concept states that all processes relating to personal data must meet the requirements described, especially in the GDPR standard. This includes processes such as data collection, data storing as well as data processing.
|All personnel in an organization who deal with data governance must exercise integrity in their work. They must demonstrate integrity when they are dealing with the options, constraints and impacts of decisions relating to data.||Purpose limitation: This principle states that data can only be collected and used solely for those purposes that have been declared to the data subject and in which consent was granted.|
|Transparency: All processes relating to data governance must be transparent. All data related decisions must be explained clearly to all personnel how, when, and why they are introduced.||Data minimization: This is the principle stating that any data, especially personal data, to be collected must be adequate, relevant, and only limited to what is necessary in regard to the intent for which the data has been processed.|
|A good data governance structure must define who is incharge and accountable for data-related decisions that are cross-functional.||Accuracy: The accuracy principle states that data should be accurate and when necessary, must be kept up to date. Organizations and businesses must make sure that they do not keep old and outdated data by ensuring the deletion of inaccurate data.|
|A good data governance structure must also define the personnel accountable for leadership activities and assigning responsibilities to individual contributors or groups of data handlers.||Integrity and confidentiality: This principle requires handling of data in a manner that ensures appropriate security. This includes protecting the data against unlawful handling, processing or accidental loss, damage, or destruction.|
Comparing the benefits: data governance vs. data compliance
Benefits of Data Governance
Benefits of Data Compliance
While no one denies that data governance and compliance are hard, a wise organization or business takes this challenge by using agile data governance and compliance platforms such as DQLabs, and goes above doing the bare minimum to comply. The benefits defined in this article are realized after a sound data governance and compliance practice.