By John Walubengo
Last week, we laid the groundwork for data governance by exploring essential components like data discovery, catalogues, metadata, and policies.
This week, we delve deeper into the critical role of business glossaries, data dictionaries, and data classification schemes in the public sector.
The Business Glossary: Getting Everyone on the Same Page
A business glossary might sound like some corporate jargon, but in the public sector, it can be a game-changer. Think of it as a universal translator that ensures everyone, from one government agency to another, speaks the same language. It is about defining key terms consistently so that when different departments collaborate, they are not talking past each other.
In a public sector setting, where collaboration across agencies with different mandates is often the norm, miscommunications can be a recipe for disaster. Without a business glossary, one is setting the stage for confusion and errors, all because of differing interpretations of the same terms.
Take, for example, a scenario where the national statistics office (say, KNBS) and a ministry of communication department are both tasked with reporting on the “Number of Internet Users” in the country.
If KNBS defines “Internet users” as anyone who accessed the internet in the past six months, but the ICT ministry counts only those with active subscriptions in the last month, then one is heading straight in for conflicting figures.
A business glossary, where relevant agencies agree on the definitions of key data elements, would eliminate this type of confusion and ensure consistent, reliable and ‘shared meaning’ statistics.
The Data Dictionary: Your Technical Playbook
Closely related to the business glossary is the data dictionary. Think of it as the technical counterpart—a centralized repository that defines the meaning, relationships, and attributes of data elements within the databases.
If the business glossary is the language guide, then the data dictionary is the rulebook that ensures the technical layer is playing the same game.
In short, while a business glossary keeps everyone on the same page at a high level, a data dictionary dives into the technical details, ensuring consistency and clarity at the data element level.
Data Classification: Determining Levels of Protection
Data classification is not just about sorting files—it is also about safeguarding sensitive information by categorizing it based on its sensitivity.
Let’s break it down into three common categories in the public sector:
- Public Data (Low Sensitivity): This is the low-risk data—think public service announcements, open government data, and press releases. Public data should be freely shared and accessible to everyone. Security measures here are minimal, focusing primarily on ensuring data accuracy and integrity.
- Internal Data (Moderate Sensitivity): This includes internal communications, employee records, and non-public financial reports. It is the data that needs to stay within the government, accessible only to staff, partners, and maybe contractors. For internal data, security measures include user authentication, role-based access controls, and encryption at rest and in transit. Regular security audits and monitoring for unauthorized access are imperative for this data.
- Restricted or Classified Data (High Sensitivity): This is where things get serious—national security level type of information, citizen health records, and law enforcement data all fall into this category. This data set demands the highest level of security such as multi-factor authentication, strict access controls limited to specific individuals, full data encryption, and possibly isolated networks. And that’s not all, every access or modification would be logged, monitored, and reviewed regularly.
In conclusion, having a robust data governance program in place isn’t just a bureaucratic exercise—it’s the backbone of any effective public sector operation. Without it, one is flying blind, opening the door to data breaches, inefficiencies, and a loss of public trust.
Good data governance ensures that data is treated as the valuable asset it is—protecting it from misuse and leveraging it to drive better decisions and services.
Done correctly, data governance can transform public sector data from a potential liability into a strategic powerhouse. It would help meet compliance with regulations, enhance transparency and build the trust that’s essential in the public sector.
John Walubengo is an ICT Lecturer and Consultant. @jwalu.
