What is Data Governance
in the Public Sector?
Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements.
With so much focus on compliance, democratizing data for self-service analytics can present a challenge. It’s not uncommon for analysts to struggle to access mission-critical data— even if they need it for urgent projects. This is why public agencies are increasingly turning to an active governance model, which promotes data visibility alongside in-workflow guidance to ensure secure, compliant usage.
An active data governance framework includes:
- Assigning data stewards
- Standardizing data formats
- Identifying structured and unstructured data
- Setting data management policies, like tagging data
- Quantifying effectiveness with metrics
- Leveraging automation with governance tools
A comprehensive data governance strategy ensures that you have quality data so you can leverage insights for data-driven decision-making.
Why is Data Governance in the Public Sector Important?
Public sector departments and agencies traditionally collect data so that they can support citizens and deliver services. In today’s analytics-driven society, the public sector can transform this historic information to reduce operational costs and improve public service to better address the needs of a given community.
Data governance is the foundation for these strategies. To unlock your data’s value, you need a data governance program that addresses:
- Data ownership
- Privacy concerns
- Data breach mitigation measures
- Dataset availability and integrity
- Transparency over data usage
A firm data governance foundation doesn’t just prevent private data from being stolen— it builds trust and accountability within data teams and across the public they aim to serve.
What are the Data Challenges Facing the Public Sector?
While digital transformation is imperative, the public sector faces significant data governance challenges that limit its ability to benefit from analytics. A core challenge here involves delivering the right data to the right person within the right context to truly enable a successful self-service environment.
Securing Data
Threat actors target the personally identifiable information (PII) that public entities collect, store, transmit, and use. Further, the public sector also manages high-value classified information. To secure this data, entities need to control where it’s stored, who has access, and how it’s shared.
Staying Within Allocated Budgets
Most public entities have strict budget requirements that limit their ability to purchase tools and hire qualified staff; both of which are mission-critical for ensuring data quality, security, and privacy. Often, this means the public sector needs to make trade-offs to stay within budget.
Changing Regulations
Since the public sector manages sensitive information, it needs to maintain compliance with various regulations. Furthermore, the public sector needs to keep pace as legislatures enact new privacy laws with extraterritorial jurisdiction.
Scattered Data
Datasets used by government entities are often dispersed across a fragmented landscape. Public entities are unable to identify data stored in these scattered registers, sometimes unable to determine what registers exist.
Inaccessible, Paper-Based Systems
Since public entities have collected data for so long, many registers still contain paper copies of data. This increases the costs and administrative burdens associated with using data.
Prevents Collaboration Amongst Different Agencies
Coordinating services requires agencies to share the same data. However, the data silos and paper-based registers limit the ability to collaborate.
What are the Benefits of Data Governance in the Public Sector?
As the public sector continues to digitize its operations, data governance is becoming increasingly important to achieving desired outcomes.
Improve Public Experiences with Public Service
When government data is interoperable and connected, government actors are better equipped to deliver superior experiences to citizens. A consolidated view of public information supports better customer service and community programs.
Enable Data-Driven Policy Creation
How do you drive meaningful change with public policy? You follow the data. A well-governed data landscape enables data users in the public sector to better understand the driving forces and needs to support public policy— and measure impact once a change is made.
Efficient Access To Data
Citizens, companies, and government employees need access to data and documents. Making data more accessible means that people only need to submit data once. This saves time and money by reducing manual inputs and enabling automation of services.
Enables Information-Sharing and Centralizes Data
When governmental entities standardize data formats, they collaborate more effectively. With interoperable and connected data, agencies eliminate duplicate data and ensure data quality so they can cohesively deliver public services. With all agency data in a single location, public entities can make data-driven policy decisions. They can build complex analytics models that leverage data from multiple registers for more robust insights.
Creates Transparency
Engagement and collaboration across agencies, citizens, and companies builds trust through transparency. With data governance, public sector entities can publish data ethically, using open-data portals and giving citizens visibility into how their data is used.
Modernizes Data Systems
Data governance enables structured and secure data exchanges for systems built on the premise of privacy-by-design. With modern data systems, constituents actively manage their consents while entities can track what data is stored, where it’s stored, and who has access.
Helps Identify Potential Problems
A data governance framework enables entities to detect potential problems with data. When using a data governance tool, like a data catalog, entities can find deprecated or outdated data, which is not fit for wider consumption or analysis.
Reduces Fraud
With data governance, public sector entities mitigate fraud risk by aggregating data across registers to ensure consistency. They can leverage fraud prevention analytics tools to detect when funds go to the incorrect recipient, the wrong amount has been transferred, or if there was an improper payment.
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Best Practices for Creating Effective Data Governance in the Public Sector
When governments and agencies implement connected and interoperable data governance strategies, they unlock the benefits their data provides. While it might seem overwhelming at first, the public sector can achieve these goals by following best practices for establishing an effective data governance framework.
1. Identify Existing Data
Before you implement a data governance framework, you need to know the data you already have. This means you need to:
- Inventory data: Know all information resources and relevant metadata
- Classify data: Organize structured and unstructured data into relevant categories
- Curate data and knowledge: Use active metadata management and data catalogs to organize and manage registers
2. Balance Defensive And Offensive Data Strategy
While a defensive data strategy works to minimize risk, an offensive data strategy supports your larger policy and constituent experience objectives.
Your data management strategy should enable you to protect data security and privacy. It should also give data analysts and scientists the ability to locate, understand, trust, and use data efficiently…
3. Choose Storage Options That Centralize Metadata
For agencies to collaborate, they need a storage option that provides the scalability and flexibility needed for data analytics.
The chosen storage options should centralize metadata so that agencies can:
- Collect information across many platforms
- Reuse metadata productively
- Gain visibility into data history
- Effectively govern data
- Assign data stewards
4. Build A Modern Data Governance Model
Your governance model defines how users create, store, maintain, and dispose of data. A modern data governance model should:
- Begin with a data catalog, on which you establish your governance framework
- Make people central to its operations, empowering data stewards and learning from their behavior to automate stewardship with time
- Encourage innovation by curating useful data assets for wider reference
- Adjust to context and integrate continuous improvements while driving community engagement and collaboration
- Provide a flexible and dynamic strategy across the ecosystem, integrating defensive, compliance-focused activities with offensive, analytics-driven tasks
- Proactively manage risk, with ongoing oversight into policies and controls
- Monitor and measure performance, integrating new insights into the governance framework so it improves with time
5. Invest In Data Governance Strategies That Democratize Data
By democratizing data, you make digital information accessible to non-technical users so that they can gather and analyze data independently in a self-service environment, without having to depend on data stewards, system admins, or IT staff for data access.
A data catalog with active data governance will learn from user behavior and metadata to help people self-service more effectively. It hunts for patterns in user behavior by leveraging artificial intelligence (AI) and machine learning, automatically adding governance guardrails that guide compliant usage. This way, everyone who needs the data can access it, with the guidance they need to use it compliantly, reducing the risk of violations.
6. Establish and Maintain a Strong Data Culture
When you have a strong data culture, you’re able to focus on making data-driven decisions. This streamlines the decision-making process and enables more informed decision-making.
A strong data culture requires the following capabilities:
- Data search and discovery: People find what they need, when they need it
- Data literacy: People know how to interpret and analyze data
- Data governance: You have the ability to appropriately manage data so that people use it in the right way
7. Troubleshoot For Potential Risks
From data breaches to privacy laws, potential data risks are important to address from the beginning. Two key risks that your data governance strategy needs to consider are:
- Access: Excess access creates privacy compliance violations. Therefore, you need to ensure everyone has the least amount of access needed, even at the data field level, to complete their job functions.
- Data storage: Storage locations must have security controls that prevent cybercriminals from accessing or stealing sensitive information.
8. Continuously Adapt And Improve Your Data Governance Framework
As you unlock your data’s potential, your data strategies change. It’s important to continuously adapt and improve your data governance framework to address changes in how you use data and the regulatory environment— and the right product can help automate and integrate key improvements.
Using a data catalog with automation enables you to track and measure things like:
- Policy conformance
- Data usage
- Data quality
- Curation progress
- Top users
- Analyst productivity
9. Facilitate A People-Centric Approach To Data Governance
Data means nothing without people. Your data governance strategy should enable people to drive your data culture.
Taking a people-centric approach means:
- Identifying and assigning data stewards
- Identifying people who use an asset the most
- Encouraging data stewards to share knowledge
- Giving people confidence that their knowledge is trusted
- Empowering users to leverage data
This original post appeared as an Alation blog.
