Data must be the starting point for the creation of any public policy. Improving state management implies, then, improving data management: collecting, managing and using data to better understand the population. How can this be achieved? How can Argentina move forward? A national strategy to make the most of data.
Illustration: José Saccone
The State emerges as one of the country’s main producers of data. Accordingly, data constitutes a fundamental input for crafting public policies that are not only more efficient but also inherently more effective, hence making it imperative to promote its conscientious handling and utilization. Nonetheless, data remains fragmented across different agencies and its full potential remains largely untapped. Although steps have been taken to address these issues, significant gaps remain in system integration and information sharing. The failure to prioritize this issue, coupled with the lack of dedicated structure to implement a national data management strategy, epitomizes the challenge at hand. Additionally, the existing legal and regulatory framework is not fully enforced. These are the challenges we must face in order to have a smart State, capable of generating evidence-based public policies and steering governance in a more informed and efficient manner.
A smart State
A smart State is one that acknowledges data as a valuable asset. It possesses a comprehensive awareness of its data, sorts and classifies it, and identifies those responsible for generating, protecting and using it. A smart State stores and employs data to enrich its interactions with citizens and to design public policies and administer governance using the greatest amount of available information. It interoperates with other sources and makes evidence-based decisions.
A SMART STATE KNOWS. It uses administrative records and alternative sources to comprehend its target population and make informed decisions. It implements public policies consistent with the needs of such a demographic.
A SMART STATE REMEMBERS. It is aware of the data and information under its control and securely stores it, ensuring accessibility when necessary.
A SMART STATE REASONS. It values and exchanges data to make informed decisions.
A SMART STATE PUTS PLANS INTO ACTION. It uses data methodically and systematically to translate information into concrete goals and actions within the framework of its public policies.
Data management in Argentina
In Argentina, laws and regulations have been enacted to foster the creation of government bodies in charge of overseeing data governance, resulting in increased digitalization of information and the protection of its confidentiality.
However, this legal framework has been inconsistently implemented and in many cases underutilized. Despite the progress made in terms of data infrastructure and connectivity, challenges persist in terms of interoperability and standardization in public administration. To improve existing tools and systems, any future data management structure should rely on existing legislation.
Nine-task checklist to design a data strategy for the Public Administration
1) Institutionalizing data in the government's organizational chart
To address this situation, the first step involves integrating data in the government’s organizational chart. Based on the analysis of different international practices, it is essential to have an individual or a team accountable for promoting the data agenda within the government. There are three main types of data accountability structures.
- Some countries, such as the United Kingdom and Spain, have opted for a centralized data structure embodied in a single role: the Chief Data Officer (CDO). The CDO is responsible for overseeing data governance and ensuring secure and private storage and usage of the data.
- In contrast, countries like Brazil and the United States have chosen to establish an inter-ministerial CDO roundtable. Each ministry appoints a CDO representative on a council that collectively sets and monitors data management through consensus.
- Alternatively, some countries delegate these functions to a government agency with a degree of autonomy from the central administration. Unlike the arrangements discussed above, this agency operates externally. Australia and Thailand are two nations that have embraced this agency model.
Crucially, this figure must garner support from various ministries and wield sufficient power and acceptance within the government to push its agenda.
Drawing insights from global experiences, a fitting solution for the Argentine context involves establishing a management structure directly subordinate to the Chief of Cabinet of Ministers. This entity, led by a national Chief Data Officer (CDO), would spearhead and promote the data agenda across the State, affording paramount importance and relevance to data governance.
2) Prioritizing data policy at state level
Provide autonomy and stability to the entity in charge of data governance to enable it to assume a pivotal role in facilitating the exchange of data between public and private organizations.
3) Conducting a diagnosis
The existence of conflicting objectives and absence of well-established agreements, precise regulations, and protocols hinder the comprehensive management of data within the State. Hence, performing a data maturity assessment across state agencies becomes imperative, for it would unveil overlapping objectives, identify gaps and urgent focus areas, while delineating necessary changes and the extent and timing of their implementation.
4) Identifying and harnessing technical capabilities
A critical oversight lies in the failure to acknowledge the technical nature of data-related challenges. Special focus must be placed on evaluating the State’s technical capacities, particularly in consolidating and sustaining technical teams in the public sector—challenged by the substantial migration of human resources to the private sector.
5) Ensuring the credibility of data
The efficiency of official statistics heavily relies on maintaining high standards of quality and impartiality. Upholding the credibility of these statistics requires addressing both technical and political aspects. Foundational to this effort is the promotion of transparency by sharing open data and adherence to the Access to Public Information Act.
6) Cultivating a local data culture
Despite the increasing awareness of the significance of data, challenges persist, including the lack of integration between government bodies and database fragmentation. These hurdles prevent effective analysis and limit the potential application of data analytics in public policy design.
7) Guaranteeing security and privacy
In recent years, the National State has encountered vulnerabilities in systems, technologies, and information. Security should not impede collaboration or hinder access to public data. Advocating the principle of proportionality, measures implemented should align with the nature and risk of the data being protected.
8) Facilitating exchange
Inconsistent data management approaches, diverse information systems, and the lack of a common language impede interoperability among state agencies. A potential solution would be to develop regulations and a legal framework focused on interoperability and smooth flow of data. This entails designing protocols, application guidelines, and ethical principles to set up robust agreements and facilitate shared data access.
9) Ensuring Continuity
The conspicuous lack of continuity in public data policies is evident in the frequent changes in government positions and the absence of long-term plans. Recognizing the importance of treating data as a public asset is pivotal. For sustaining long-term governance, establishing a framework to carry forward a State data policy becomes essential.
