Blog by Rosa Lee, Research Fellow, Data Foundation
Over the past years, the United States government became a front-runner in implementing new laws and guidance on data-driven government. Starting with the Digital Accountability and Transparency Act (Data Act) of 2014, a series of relevant new laws were enacted, including the Foundations for Evidence-Based Policymaking Act (Evidence Act) of 2018 and the Grant Reporting Efficiency and Agreements Transparency Act (GREAT Act) of 2019. One of the first tangible results of this legislation has been the development of the Federal Data Strategy. However, being a front-runner does not necessarily mean that we do not need to learn from others.
In November 2019, OECD published a report “The Path to Becoming a Data-Driven Public Sector,” detailing OECD countries’ challenges and opportunities in the application of data-driven strategies. The report highlights the importance of adopting a “whole-of-government approach” to assist governments in delivering better services. A data-driven public sector (DDPS) framework is also applicable to local governments or different institutional levels.
Here are the six takeaways to achieve system-wide benefits in government.
Government-wide data governance should be open, coherent, inclusive, iterative, collective, and value-based. In short, a comprehensive approach is far better than a siloed approach.
In many countries, data leadership/stewardships are still misunderstood, leaving data governance as the exclusive responsibility of the IT department. Data governance requires securing leadership within more robust institutional fabric – policies, regulatory frameworks, and organizational culture.
The data value creation process is cyclical and iterative, not linear. Thus, different stages call for various technical skills and roles.
More robust data governance ensures safe data sharing and interoperability and thus improves cross-border public service delivery.
Putting in place of rules, laws, guidelines, and standards prevents ill-managed data value cycle.
Data is an asset. Some tangible benefits include government efficiency and potential productivity gain. Among the intangible benefits of data, public trust is often a neglected outcome.
What Are Other OECD Countries Doing?
In general, OECD member and partner countries are moving towards national data strategies. Canada, Ireland, the Netherlands, the United Kingdom, and the United States come close to the overarching data strategies and strive for more explicit institutional structures. Other countries have developed guidelines for public servants on how to use open government data to inform policy-making processes, including Austria, Colombia, the Czech Republic, Finland, France, Japan, Korea, and the United Kingdom.
Developing a comprehensive model of data governance would be prerequisite for evidence-based decision-making practices. A well-established data governance helps governments deliver better services and self-actualize the value of an organization’s assets. The role of data in government sectors can be found in the three broad stages: 1) anticipation and planning, 2) service delivery, and 3) evaluation and monitoring.
Formalizing leadership roles
Building a more robust institutional fabric, some OECD countries have formalized leadership roles – such as chief data officer or data stewards – by securing them to the existing administrative structure. There are two different approaches to how other OECD governments secure the leadership roles: 1) political leadership and 2) administrative leadership roles.
While political leadership suffers from any changes in administration, executive leadership can help to increase continuity and sustainability. New Zealand has formalized leadership roles by attaching them to existing administrative structures; the Chief Executive Statistics also holds the role of Chief Data Steward.
Other countries, such as Argentina and Mexico, open data leadership functions as the de facto Chief Data Officer. In Canada, the Data Strategy Roadmap for the Federal Public Services recommended the creation of a Government Chief Data Steward in 2018.
Data Value Cycle
Establishing leadership roles is just one starting point. To help re-engineer organizational systems, the OECD reports underscore the importance of feedback loops: Data Value Cycle (DVC). DVC is a continuum of interrelated stages, and thus ensuring data-driven efforts in government requires reassessing or re-engineering organizational legacy systems. The OECD research team developed the DVC frameworks which describes the four phases of data in government.
The first phase: collecting and generating data (where the universal data standards take an important place.)
The second phase: storing, securing, and processing of data (governments decide how to store data, access it, catalog it, and clean it. Yet, there is no payoff generated until the third stage.)
The third phase: sharing, curating, and publishing of data (where the public value starts to generate. To effectively handle requests and agreements, data interoperability platforms or licensing mechanisms become a key element.)
The fourth phase: using and re-using data (the value will be eroded when the quality of data is poor or if there are incomplete sources, unreliable access, and barriers to sharing.)
A well-functioning DVC will be grounded in two aspects of the data ecosystem: 1) data sharing and 2) data protection. An artful balancing between these two aspects is challenging. When data is over-protected, it can reduce the value of data sharing, and such practices make the delivery of cross-border public services difficult. When data is under-protected, the government will lose public trust, which is the final goal of the data-driven public sector.
When Data Cross Over
Data can go cross-sector and sometimes cross-border. When it crosses over, several actors are involved, including international organizations, business, data protection authorities, and civil society organizations. Some OECD countries have specific government requirements for data sharing, especially among public institutions, such as government agencies. In Korea and Portugal, all government data is proactively shared with all government departments. In Sweden and Denmark, all public institutions proactively shared some selected vital data sets. In Ireland, public institutions tend to make a specific ad hoc agreement. The United Kingdom has no particular requirement but suggests that data sharing happens only due to ad hoc requests.
Keeping a balance between sharing and data protection requires governments’ systems to have flexibility and scalability. Such traits can be manifested when governments have coherent sets of rules, laws, guidelines, and standards. Some countries established legal and regulatory frameworks to support the data-driven public sector, including United States, United Kingdom, EU, France, Luxembourg, South Korea, Japan, and Singapore. United Kingdom and New Zealand also utilize rules and guidelines.
When data is understood as an organizational asset, the public value out of it can be numerous.
One clear benefit can be found in the regulatory reporting sector – business-to-government reporting practices. Data standards, such as XBRL, can incentivize the market by foreseeing new trends and needs as well as monitoring ongoing implementation and adopting innovative approaches.
In this way, data standards can reduce unnecessary time and energy for data harmonization that cross over different sectors. The OECD report highlighted the shared regulatory frameworks can be used for auditing purposes within and across governments, thus facilitating data access and sharing.
Government officials and other practitioners in the United States can enhance the value of data by learning from other OECD countries. The lesson from EU could be especially useful considering that US also needs to deal with 50 states’ different data ecosystems.