Data policy and data standards are two concepts that almost always surface in the world of data governance. To the uninitiated, it might sound like two sides of a scale: one stating what shall be done, the other how it ought to be done.
Contrarily, they play together and complement each other in service of effective data management.
So, are data policy and data standard enemies or friends?
As you read further, you shall understand why they constitute a match made in heaven, each fulfilling a different yet interrelated function in the multilayered world of data governance.
What is a Data Policy?
A data policy is a high-level guiding principle that establishes rules and responsibilities for using and protecting data within an organization.
In the ongoing policy vs standard discussion, the “what” of data governance sets guidelines is how data shall be treated to ensure compliance, safety, and ethics. Data policy encompasses the key issues related to privacy, ownership, access control, retention, and other facets that form the backbone of data management and fit well into organizational objectives with legal boundaries.
On the other hand, a data policy isn’t inflexible; it’s rather flexible and allows room for changes in organizations’ technologies and legal demands. Policies help maintain consistency of data practices within regulatory frameworks, such as GDPR or HIPAA, in support of organizational goals.
What is a Data Standard?
Contrasted with data policy, the data standard is a detailed technical rule set that defines the “how.” It assures consistency in formatted, structured, and/or exchanged data. Data standards guarantee that information flows effortlessly between systems and organizations, thus allowing interoperability while reducing errors.
For example, a data policy might require sensitive data to be encrypted, but a data standard would stipulate that the encryption utilizes AES-256, a particular encryption algorithm. Data standards also ensure that different parts of an organization can “talk” to each other, even when large and complicated datasets are involved.
Some common data standards include ISO formats and metadata standards, and even more mundane examples include QR codes and UPC barcodes. These ensure uniformity, reduce costs, and raise productivity across industries.
Data Policy and Data Standard: Opposites or Partners?
Taken literally, data policy and data standards can appear to be somewhat opposing concepts. Policies are general, flexible guidelines, whereas standards are specific, technical, and inflexible. How can they coexist?
The answer comes from their hierarchical relationship. Policies set out an organization’s strategic vision and objectives, while standards operationalize those objectives, precisely defining what action is to be taken to meet the policy. Rather than being opposites, they’re complimentary, helping the other work.
Without policies, standards are without direction. Consider implementing a standard for data encryption without understanding whether it meets the organization’s legal or ethical requirements. In the same vein, policies need standards to be more than conceptual; standards need policies to flesh out the details. Together, they form a seamless system wherein data governance is inspirational and implementable.
The Need to Align Policies with Standards
The most important part of any successful data governance strategy is, in effect, the alignment between data policies and standards. Poor alignment results in confusion and inefficiencies and, in some cases, will finally result in legal penalties.
For instance, an organization may have policies that require seven-year retention of customer data, but its data standard states only storage formats without considering security in the long term. The resulting gap would cause a security failure because of the non-compliance, exposing an organization to a potential lawsuit or data breach.
Therefore, data policies and standards must evolve together within an organization. A strong data governance framework is fluid, changing with technological advancements or changes in regulatory requirements.
However, collaboration is paramount across other groups, like legal and compliance teams and IT. A written policy by legal experts might ensure a regulatory basis for it, but it will more often than not draft the standard to meet those legal goals in practical and effective ways. Each needs to inform the other for a seamless whole.
Examples of Policy and Standard in Action
Consider a financial institution for which data privacy is paramount. The institution’s policy concerning data would then be to anonymize all information about customers before that information leaves the institution. This is a very general instruction to safeguard customer privacy.
Accompanying the policy would be a data standard stipulating how the anonymization must be carried out-perhaps by using certain masking techniques or encryption algorithms. Working in concert with the policy, the standard helps to ensure that customer data is not only anonymized but consistently and securely anonymized across the organization.
Government regulations about data also usually establish policies on data retention, access control, and public reporting, among other things. Then, the standards—such as XML schemas or machine-readable formats—prescribe the structure and exchange of that data to meet those regulatory policies.
Conclusion
Above all, data policies and standards are not opposing concepts. Instead, they represent the twin pillars of effective data governance. They ensure that data is handled securely, compliantly, and efficiently.
Policies set the overall direction and characterize how an organization addresses data, while standards provide the specific guidance necessary to implement the direction. Together, each provides a complete dynamic system able to evolve with the changing organizational requirements of a more data-driven world.
In other words, as the digital environment keeps changing, it is only when policies regarding data and standards relating to data join hands that an organization can comfortably get itself entrenched in various intricacies regarding data governance. Harmony isn’t about being the same but complementing each other towards a higher purpose.
Also Read: How to automate data collection with web scraping
