“Successful data management must be business-driven, rather than IT driven.”  (DAMA 2017)

Overview

Broadly speaking, the technologies and methods needed to make data speak to each other already exist. The most serious impediments to interoperability often relate to how data is managed and how the lifecycle of data within and across organizations is governed. Data management, “the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles” (DAMA 2017, 17), is therefore the cornerstone of any effort to make data more interoperable and reusable on a systemic scale. To be effective, data management requires that data be effectively governed, controlled with oversight and accountability, as it moves within and between organizations during its lifecycle.

As it stands, when entities relegate anything that has to do with ‘data’ to their IT or Monitoring and Evaluation (M&E) departments, without also focusing on data issues at a leadership level, they miss an opportunity. This is because they are failing to make the connection between ‘data’ and the sources of information that programmatic specialists – public health experts, education specialists, protection officers, natural scientists, etc. – rely on to perform their jobs, devising and implementing development policies, programmes and projects to help meet the targets of the 2030 Agenda for Sustainable Development.

Data issues need to be considered as cross-cutting, in the same way that gender, human rights and partnerships’ issues currently are in the development field. As such, they require far more cogent management, funding, oversight and coordination than they are currently afforded.

This section explores the concepts of data interoperability and integration, management and governance in more detail; highlighting some useful institutional tools and examples that can help practitioners in the development of their data management and governance strategies. It sets out the various institutional frameworks and models of data governance that exist, explains the need for oversight and accountability across the data value chain, and the need for effective legal and regulatory frameworks.

At its heart, this section extols the benefits of thoughtful planning, continuous strategic management and governance of data across its lifecycle, and consideration of user needs from the outset when striving to modernize IT systems and amplify the reusability and audiences of existing data.

Institutional frameworks and interoperability

Institutional frameworks refer to the overarching systems of laws, strategies, policies, conventions, and business processes that shape how individuals, organizations, and institutions behave and engage with each other. Keeping the Data Commons Framework referred to in the introduction in mind, it is clear that institutional frameworks have a key role to play in creating the environment where data, technology, and business processes fit with each other and enable the effective functioning of knowledge-driven organizations.

For the purposes of this Guide, we have broken down ‘institutional frameworks’ into three components that capture various dimensions of interoperability:

  1. Institutional models of data governance;
  2. Oversight and accountability models; and
  3. Legal and regulatory frameworks.

Institutional models of data governance

 

Figure 4: Models of Data Governance

 

There are different approaches to data governance, with some being more centralized than others. Individual organizations need to determine what will work best for them, keeping in mind the purpose for which data is being collected and used. For example, an NSO may want to develop a more centralized model for data collection, standard-setting, validation and security, given its role in coordinating the overall production and dissemination of official statistics at the national level. A more decentralized or more modular model of data governance may work better in instances where control over data is distributed.

Too much decentralization does not work well in volatile environments that require data standards and coordination to tackle global information sharing challenges. Conversely, too much centralization can hinder experimentation and the creativity needed to innovate and to respond to emerging needs of data users and the quickly changing technological landscape.

A middle ground can be found in so called “replicated” and “federated” governance frameworks. The former is when a common data governance model is adopted (usually with only minor variations) by different organizations. The latter is when multiple organizations coordinate to maintain consistency across their data governance policies, standards and procedures, although with different schedules based on their level of engagement, maturity and resources.

A replicated data governance framework is well suited to promote interoperability across independent organizations and loosely coupled data communities, each of which has ownership over specific data assets. However, this kind of governance framework requires very clear institutional and technical mechanisms for communication and collaboration, including the provision of adequate incentives for the adoption of open standards and common data and metadata models, classifications, patterns for the design of user interfaces.

A federated governance framework allows multiple departments or organizations, none of which individually controls the all the data and technological infrastructure, to constitute a decentralized but coordinated network of interconnected “hubs”.  Such “hubs” consolidate and provide a consistent view of all the data assets available across the network, reducing the complexity of data exchange management, and provides a space where disparate members of that network can engage with one another. Moreover, although the federated model provides a coordinated framework for data sharing and communication, it also allows for multiple representations of information based on the different needs and priorities of participating data communities. It leverages technology to enable collaboration and the implementation of common data governance mechanisms.

Figure 5: Federated Information Systems for the SDGs

At its 49th session in March 2018, the UN UNSC welcomed the establishment of a federated system of national and global data hubs for the SDGs. By leveraging the opportunities of web technologies, this initiative is already facilitating the integration of statistical and geospatial information, promoting standards-based data interoperability and fostering collaboration among partners from different stakeholder groups to improve data flows and global reporting of the SDGs under the principles of national ownership and leadership.

The Federated Information System for the SDGs initiative has already launched various data hubs, including the global Open SDG Data Hub available from: http://www.sdg.org/.

 

Collaborative approaches to data governance exist between organizations and institutions and can be an effective way to engender a more multi-stakeholder, open and ecosystem approach to the tackling of interoperability problems. The benefits of collaborative approaches also include greater adaptability and flexibility than the more formal models mentioned above. The Collaborative on SDG Data Interoperability is one such example, as are the Health Data Collaborative[1], the Committee on Data of the International Council for Science (CODATA)[2] and even more formalized international standards organizations such as W3C[3] and ISO[4].

A level below that of governance frameworks sit business processes; series of tasks and activities that collectively result in the delivery of a product or service. Here, interoperability can play a role in helping gel the various parts of the business process together. The Generic Statistical Business Model (GSBPM) is a case in point.

 

Figure 6: The Generic Statistical Business Process Model

The GSBPM developed by the UN Economic Commission for Europe (UNECE) on behalf of the international statistical community, is an excellent example of a systemic, coordinated and collaborative initiative that has established a common standards-based approach to business process development for official statistics. The model offers examples of good practice for handling data interoperability and integration issues from a data management perspective.

As a business model, its objective is to set out and break down into logical components the tasks and activities that should take place within a statistical office to achieve organizational objectives. It, “provides a standard framework and harmonized terminology to help statistical organizations to modernize their statistical production processes, as well as to share methods and components. The GSBPM can also be used for integrating data and metadata standards, as a template for process documentation, for harmonizing statistical computing infrastructures, and to provide a framework for process quality assessment and improvement.” (Lalor 2018).

GSBPM describes itself as a reference model and makes clear that NSOs can adopt as much or as little of it as they need to and adapt its components to their own needs. It covers components across the whole statistical production chain at two levels. Within GSBPM, as in ODW’s data value chain framework referenced above, interoperability and integration issues emerge explicitly at the processing stage of the model; however, are also more subtly present along the whole chain.

Taking a broad view, and keeping in mind the Data Commons Framework referred to in the introduction, dimensions of the GSBPM that are conducive to interoperability include: the specification of a Creative Commons Attribution License for reuse (see Annex B for more detail on licenses); it’s standards-based nature that promotes a harmonized approach; the fact that the model considers interlinkage to other business processes from the outset; it’s consideration and incorporation of a statistics-specific Common Metadata Framework (CMF); and the modular and reference nature of its components that make it possible for NSOs to align some of their functions to the common standard while retaining overall independence and the ability to choose practices that work best for them.

 

Oversight and accountability models

As repeated often throughout this Guide, interoperability is a characteristic of high-quality data that should be fostered across organizations; not just by computer scientists, technical experts, or IT departments within organizations. To embed data interoperability as a guiding principle across an organization requires careful planning of governance mechanisms, including appreciating the value and usefulness of oversight and accountability. The form that oversight and accountability will take depends on the size of the organization, the availability of resources, management structure, and the role of the organization in the broader data ecosystem.

Individual data governance officers (DGOs) and data stewardship teams (DSTs) (DAMA 2017, 91) should be clearly identified within operational departments, with the responsibility and commensurate authority to ensure that data is properly governed across its life cycle – that is, retains its value as an asset by being comprehensive, timely, supported by metadata, in conformity with appropriate standards, released in multiple formats for different audiences and in compliance with any applicable laws and regulations. DGO’s and DST’s should also bear responsibility for maintaining relationships with other organizations and entities, with a mandate to coordinate the best approaches to sharing data, keeping in mind the types of conceptual, institutional and technical frameworks that will be needed to ensure interoperability across entities.

Figure 7: Conflicting Views of Data Governance

Working out how to govern data within and across organizations is difficult, hence the variety of models that exists. Although this Guide suggests the approach as set out in the DAMA Body of Knowledge (2017), other approaches exist.

For instance, in Non-invasive Data Governance (Steiner 2014), Robert Steiner advocates an approach in which employees of organizations do not need to explicitly acknowledge data governance, because it is already happening; what is needed is that the process be formalized to some degree.

While approaches may differ, what is important is that organizations take a proactive approach to working out what would work best for them, in the context they work in, to effectively govern the data that flows through their systems.

 

 

In larger organizations, a data governance council or data governance steering committee may be an appropriate mechanism to collectively govern data across its lifecycle. Any council or committee should include a mix of technical, operational and support staff and have executive support and oversight to ensure accountability. Their functions should mirror the same functions as DGOs and DSTs. This format reflects several dimensions of interoperability: technical and data, semantic, and institutional and will ensure that there is a holistic approach to data issues. Over time, such mechanisms can help to change approaches and perceptions of the value that high-quality data holds and can help organizations and whole data ecosystems be more data-driven.

Legal and regulatory frameworks

Legal and regulatory frameworks are crucial to interoperability, especially when it comes to the sharing and integration of data assets between organizations and across national borders. Laws set the boundaries of what is acceptable conduct and what is not. In some instances, they govern how data can be shared (for instance, laws that regulate and set standards for data reporting, security and protection) and in others govern what data can, or more often cannot, be shared and integrated (for example, data protection and privacy laws).

Laws and regulations exist at many different levels; from the international to the sub-national. International normative frameworks, international laws and domestic laws all set standards for expected conduct and behavior. Meanwhile, memoranda of understanding (MOUs) and various forms of agreement, including data sharing agreements and licenses, set the parameters for specific relationships between organizations. Corporate policies, while not laws, can form part of regulatory frameworks when they set protocols and procedures for data sharing within the parameters of the law. Finally, ‘legal interoperability’ is itself an important dimension of broader ‘interoperability’ that relates to how laws from different jurisdictions can be harmonized. Refer to Annex B for further information and a listing of the types of legal mechanism that can support interoperability.

Building a roadmap: an interoperability rapid assessment framework

The following is the first part of the assessment framework produced as part of this Guide. It focuses on the relevance and applicability of conceptual frameworks and the value of institutional frameworks, with a particular focus on legal and regulatory frameworks. It is designed to help inform the development and implementation of data governance strategies and should be supplemented with the resources identified under the Further Reading heading below as well as other context-specific materials.

Action areas

Initial Steps

Advanced Steps

 

Institutional Frameworks

Identify what model of data governance would work best for your organisation (or you are already a part of) and ensure that interoperability considerations are taken into account from the outset as part of this choice.

Put in place a data governance policy that sets out how data is governed across your organisation.

 

 

Conduct internal data availability assessments/audits on a regular basis and keep a record of what data is held and handled over its lifecycle. Use this information to periodically review your data governance policy and updated it as required.

Conduct comprehensive quality assessments and data audits in collaboration with other stakeholders within the local data ecosystem.

Develop Monitoring, Evaluation and Learning frameworks that include indicators on data governance issues.

 

Oversight and accountability

Identify Data Governance Officers (DGOs) and establish Data Stewardship Teams (DSTs) within your organisation.

Convene Data Governance Councils or Data Governance Steering Committees across organizations comprised of technical, operational and support staff, and supported by the Executive to ensure oversight and accountability.

 

Legal and Regulatory Frameworks (see Annex B for further information)

Identify and map applicable laws and regulations that apply to the data you hold and process.

Identify the types of agreements (MOUs, data sharing agreements, service agreements, licenses, etc.) that are best suited to the organization’s needs and adopt templates that can be used by staff to share data, procure IT services, etc.

Devise corporate policies that incorporate interoperability-friendly approaches and strategies.

Develop bespoke legal templates for contracts, MOUs and licenses that conform to international best practices and are compatible with other frameworks (for e.g. licenses that are compatible with Creative Commons templates).

Where resources permit, provide departmental training and sensitization on how to interpret and implement corporate policies.

 

 

Common pitfalls in data governance:

  • Failing to take an organisational approach to data management and governance issues and relegating ‘data’ issues to the IT department;
  • Not developing/enforcing a clear chain of accountability specifying roles and responsibilities across departments when it comes to the effective governance of data across/between organisations;
  • Overlooking/not considering interoperability issues as a requirement when updating or procuring new IT systems; resulting in internal data silos, and multiple types of data held in incompatible formats and schemas; and
  • Not making the best use of legal and regulatory tools and frameworks that can create a safe and structured environment in which data can be shared and integrated while respecting privacy, data protection and security considerations.

Further reading on data management, governance and interoperability

 

  • DAMA International (2017). Data Management Body of Knowledge, 2nd ed. New Jersey: Technics Publications.
  • Joined-Up Data Standards project (2016). The frontiers of data interoperability for sustainable development. Available at: http://devinit.org/wp-content/uploads/2018/02/The-frontiers-of-data-interoperability-for-sustainable-development.pdf
  • Palfrey, J. & Gasser, U. (2012). Interop: The promise and perils of highly interconnected systems. New York: Basic Books.
  • Steiner, R. (2014). Non-invasive data governance: The path of least resistance and greatest success. New Jersey: Technics Publications.

 

 

 



[2] For more information see: http://www.codata.org

[3] For more information see: https://www.w3.org

[4] For more information see: https://www.iso.org/home.html

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