6. Management of open government data

Management of open government data refers to both internal management by producing organizations and interactions with user communities (external actors).

This function, with greater emphasis on strategic and organisational management of the activities carried out by organizations, is dealt with in the “Open Data Maturity Model” guide, published by the Open Data Institute, which evaluates the publication and use of open data.

The formal aspects of the internal processes of the organizations are treated in the “Public Agency Guide,” available on the Transparency Portal of the State of São Paulo³¹.

This guide emphasises the management of the relationships with intermediaries and users of open government data.

Other work, such as that of Janssen and Ubaldi for the OECD, deals with policy options for the management of open data at a broader structural level of the government³².

This guide takes up some aspects of management that are particularly important for the dissemination and maintenance of open government data.

Besides the benefits of dissemination already mentioned, the management of open government data faces important barriers to adoption of cultural, institutional, economic, and technical aspects by promoting organizations and communities of users. These hindrances include:

Institutional aspects (risk-averse culture, difficulty dealing with the public, lack of resources, etc.).
Complexity of the task (difficulty identifying data and accessing it, difficulty handling it, redundancies and contradictions, standards, etc.).
Use and participation (lack of incentive and use, unforeseen costs and complexity, lack of skills for use, difficulty solving conflicts).
Legislation (privacy and confidentiality, contracts, licenses).

The Open Data Leaders Network, coordinated by the Open Data Institute, has categorised some of the cultural change challenges faced by leaders of open data programmes.³³ A 2014 report from the Open Data Institute provides 12 recommendations for open data leaders managing culture change in government.³⁴

6.1 Management of open government data as an evolutionary process

Open government data initiatives start with the creation of required legal and institutional frameworks, provided by higher administration organizations, but enforcing organizations are responsible for planning and implementing their activities:

Creating corporate strategies for open government data.
Creating technical and organisational structures.
Providing the necessary human and technical resources.

Implementation requires from the supplying/publishing organizations::

Internal mobilisation of open data – strategic, behavioural, and organisational aspects.
Acquisition of technical skills for production, publication, and dissemination of open government data.
Promotion of necessary process changes.
Viability of connections with communities of users.
Ensuring technical and organisational sustainability of initiatives.

In the case of the government of the State of São Paulo, the disclosure of government data was institutionally supported in 2010, with the creation of the Open Government SP regulation³⁵. Currently, design of strategies for the website and other innovation initiatives for disclosing data are conducted by the Subsecretary of the Innovation Unit (iGovSP) under the Secretary for Innovations and Partnerships of the State of São Paulo. Among other things, the program undertakes actions of qualification and motivational activities for timely dissemination, such as the SPUK project and iGovLab³⁶, and provision of open data through the Open Government SP Portal, which is operated by the SEADE Foundation.

At the management level of producing/publishing organizations, this legislation provides for the designation of authority for guaranteeing compliance with the regulations, which in turn also involves the creation of the Information Services to Citizens and the formalisation of procedures for assistance (Art. 9 of Law 12.527/2011 and Art. 7 of Decree 58.052/2012).

Successful initiatives have shown the importance of interaction with user communities from the beginning of projects, ensuring appropriate prioritisation of activities, technological definitions, production processes, and support to assist users in creating increased value. The focus on defined priorities from the user point of view allows more efficient allocation of resources and obtaining the best result at all times.

This strategy also contributes to the creation of a reliable culture among actors and better results for the appropriation and use of open government data.

Just like any organisational development, this management can be dealt with as an evolutionary process. This means that producing organizations do not need to have complete infrastructures for governance, open government data management and processes that are proposed in various guides from the start; and they could add new priorities over time.

This evolution can be seen from several angles that also represent the results of decisions made about the position of producers/publishers of open data, taking into consideration the growing complexity of managerial processes and the commitment of resources required by producers/publishers.

Consequently, from the angle of data made available to users, it is possible to identify the stages in Figure 5.

Figura 5: Stage model for open government data

Source: adapted from Kalampokis et al., 2011³⁷

The first stages guarantee compliance with legislation, requiring technology and management that are dealt with in the Open Data Maturity Model guide. The “5-star deployment scheme,” of Tim Berners-Lee, which is discussed in the Open Data Maturity Model guide from the Open Data Institute, introduces the stages of refining the publication of open data. The Open Data Certificates, developed by the ODI³⁸, reframe the Tim Berners-Lee “5-sta” system as part of a practical guide for open data publishers.

The remaining integration stages require more intense relationships with user communities, more advanced and participative management of technologies, and processes with greater added value. These resources could be readily available in chains of intermediaries, and communities could be motivated by agencies to carry out these types of activities.

The data integration stages match the evolutionary stages in terms of management complexity, relationships, and participation provided by producers/publishers for users and intermediaries, such as:

Increasing data transparency.
Improving open participation.
Expanding open collaboration.
Substantiating ubiquitous engagements.

Figure 6 below details metrics for evaluating the stage of participation achieved in process.

Figura 6: Metrics for evaluating open data management

The evolution of these stages will depend, in an increasing scale, on the following management measures taken by producers/publishers:

Increasing availability of open government data.
Improving infrastructure for more efficient use of open government data.
Developing the ability of internal and external actors to make more efficient use of open government data.
Motivating community participation and co-creation of valued information.

This model also indicates the increasing importance of:

Integration of government organizations and external actors.
Strengthening the organizations with specific functions:
- Distributing information.
- Sharing services for open government data structures.

The purpose is to achieve operational synergies, better-quality services, and more information and analysis resources.

Table 5 synthesises the levels of management services presented in the guide. The concepts and recommendations are detailed throughout the text.

Quadro 5: Summary of levels of service to users and managerial complexity

6.2 Complexity levels of open government data management and governance

These management services and activities involve:

Increasing volume of resources.
New actors.
Better quality of information services.
Increased complexity of internal and external relationships.
Better transparency and participation.

For this reason, it is necessary to improve process formalisation, and institutionalisation of management and governance structures that depend, in part, on senior administration levels for planning, coordination, and monitoring:

Maintenance of institutional conditions.
Creation of policies, and organisational and dissemination programme structures.
Support for creating communication and technical infrastructure.
Answers for issues of compliance, privacy, and data protection, among others.

Organisations should take care to ensure that data released as open data is not in breach of data protection laws, or IP laws. Consent will be important where government is planning to publish data that contains data owned by third parties.

6.3 Quality of data

The quality of open government data and support services can be sought in two dimensions:

Intrinsic quality (from the data itself)

The technological aspects and the access to this attribute are covered in previously mentioned publications: “Open Data Guideline” and “Semantic Web Guideline,” published by NIC.br, which include availability, resilience, technical support, and pre- and post-delivery aspects.

Regarding producers/publishers, the quality of systems depends on activities of a technical nature, which is the subject of the previously mentioned “Open Data Maturity Model.”

Systemic quality

This guide emphasises systemic quality (from the user point of view, according to their expectations and relationships with producers or chains of intermediaries).

These aspects refer to the characteristics of the context and use of open government data, and relationships of users with suppliers and chains (Figure 7), dealing with:

Evaluation of suppliers (credibility, reputation, accuracy).
Relevance, adequacy, validity, and added value.
Facility in interpretation and comprehension of the data.

These attributes are specific to users and heavily dependent on contexts of use. Levels of satisfaction also depend on the expectations of users, including a subjective component during evaluation.

Figura 7. Quality of data attributes

Producers/publishers need, then, to actively monitor chains of intermediaries and data use to manage their services. Along with technical dimensions (content, presentation, level of service), perceptions of quality will depend on the competencies of users and subjective aspects such as the image of products and chains, which in turn requires ongoing maintenance of communication channels among actors.