Data governance and government: The need for effective and protective data management

Marc Hoogstad, Head of Product Management at Finworks, tells us about the key challenges and best practices that governments must consider when establishing their data governance framework

The importance of data governance, well recognised by the private sector, is gaining traction by national governments. Data governance allows governments to ensure that data and information are managed appropriately, providing the right people with the right information at the right time. Good data governance is imperative for governments that aim to become more data driven as part of their digital transformation strategy. It can help to extract value from data assets, enabling greater data access, sharing and integration and increasing overall efficiency and accountability.

Is governance overlooked?

Despite its importance for achieving data quality, data governance often does not receive the attention it deserves. Government departments and other public sector organisations often fail to govern, manage, and value data in the same way as the other assets relevant to their success. The storage and analysis of this data should benefit society, as it can enable organisations to improve their decisions.

A well-governed data landscape enables data users in the public sector to better understand the driving forces that support public policy – and measure impact once a change is made. Data governance principles should, therefore, reflect and preserve the value of data to society from the sharing and analysis of anonymised datasets as a collective resource.

Data sharing

Public organisations often face legacy challenges ranging from outdated data infrastructures and data silos to the lack of leadership and accountability and an organisational culture which is not open to digital innovation and change. As more access to real-time data is given in self-service analysis, there is a greater need for a common understanding of the data across and between government departments.

According to Otto, ⁽¹⁾ important formal data governance goals for public organisations are:

1. to enable better decision-making,
2. to ensure compliance,
3. to increase business efficiency and effectiveness, and
4. to support business integration.

The UK Government has set out clear principles for building consistent and coordinated cross-government data-sharing governance. The default position is where government data is assessed as not personally identifying or otherwise sensitive or restricted, it should be available for sharing. Common data governance strategies allow governmental entities to standardise data formats, and in doing so, they collaborate more effectively. With interoperable and connected data, government departments eliminate duplicate data and ensure data quality to cohesively deliver public services.

Summary of key issues

Data governance in government involves establishing and enforcing policies, procedures, and standards to ensure the effective management, use, and protection of data. Several key issues and challenges are commonly faced in the context of data governance in government:

• Data privacy and security: Governments handle vast amounts of sensitive and personally identifiable information. Ensuring the privacy and security of this data is a paramount concern, especially in the face of increasing cyber threats and data breaches.
• Compliance with regulations: Governments must adhere to various regulations and compliance standards concerning data management, such as data protection laws, privacy regulations, and industry-specific requirements.
• Interoperability: Government agencies often operate with disparate systems and databases. Achieving interoperability and ensuring seamless data exchange among different agencies is a significant challenge impacting the efficiency and effectiveness of government services.
• Data quality and accuracy: Maintaining high data quality is essential for effective decision-making. Inaccurate or inconsistent data can lead to flawed analyses and decision errors.
• Data ownership and accountability: Determining ownership and accountability for data within government organisations can be complex. Clarifying roles and responsibilities is crucial to avoid confusion and ensure that individuals or departments are accountable for the accuracy and security of data.
• Cultural change and awareness: Implementing effective data governance often requires a cultural shift within government agencies. Building awareness and fostering a data-centric culture can be challenging, especially when dealing with legacy systems and entrenched practices.

Addressing these key issues requires a comprehensive and strategic approach to data governance. Governments must invest in the development of clear policies, robust technological infrastructure, and a culture that values the responsible use of data to achieve transparency, efficiency, and public trust.

Governance framework

There is no single, ‘one size fits all’ approach to the administration of data governance. Data governance includes a clearly defined authority to create and enforce data policies and procedures. An organisation outlines its individual data governance configuration by defining roles, decision areas and responsibilities with a unique configuration, and specialised people need to be hired, trained, nurtured, and integrated into the organisation.

Four principles of data governance for public organisations have been defined: ⁽²⁾

1. Organisation,
2. Alignment,
3. Compliance Monitoring and Enforcement, and
4. Common Understanding.

The OECD presents a proposed model for data governance in the public sector, based on OECD good practices on data management and sharing within the public sector, open government data and digital government. ⁽³⁾

Experts in data management

Finworks believes a data governance framework must ensure proper data management through its entire life cycle. Our experts welcome a more technical discussion on improving data management practices, particularly the production, storing, processing, and sharing of data towards overall data openness. Finworks have successfully navigated the unique data governance considerations that apply when government departments and other large public sector organisations move operations into the cloud.

References

1. Otto, B.: A morphology of the organisation of data governance. In: ECIS, p. 1 (2011)
2. Brous, P., Janssen, M., Vilminko-Heikkinen, R. (2016). Coordinating Decision-Making in Data Management Activities: A Systematic Review of Data Governance Principles. In: Scholl, H., et al. Electronic Government. EGOV 2016. Lecture Notes in Computer Science, vol 9820. Springer, Cham. https://doi.org/10.1007/978-3-319-44421-5_9
3. https://www.oecd-ilibrary.org/sites/9cada708-en/index.html?itemId=/content/component/9cada708-en

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