Data-driven public sector: significance, challenges & the way forward

In today’s age of information and communication technology, data can fuel the digital transformation of the public sector. It also publishes open data and protects the rights of citizens in terms of data protection, privacy and security. It transforms the design, delivery, and monitoring of public policies and services through the management, sharing and use of data. Using data as a strategic asset is crucial for governments to boost public sector intelligence, resulting in an increased capability of developing policies and sustainable services that are inclusive and trusted.

Data-Driven Public Sector (DDPS) recognises data as an asset, integral to policy making, service delivery, organisational management, and innovation (Ooijen & Welby 2019). It removes barriers, and streamlines the access, sharing and use of data. In DDPS, data is recognized as a fundamental building block for those seeking to implement and enable digital government in their countries. In the digital age, the widespread adoption of Information and Communication Technologies (ICTs), data and analytical tools have become available and easier, providing ample opportunities to improve decision-making. Decision makers can access tailor-made, context specific information and insights gained from data analysis. On the contrary, in a pandemic situation such as Covid-19, without information on what people are experiencing on the ground, governments do not know how best to respond, what services are needed, who needs the services.

SIGNIFICANCE OF THE DDPS: During the last decades, many governments around the world have set up several initiatives for digitisation to build trust between governments and citizens and keep pace with the citizens’ expectations and deliver on the promise of the digital age. Data-driven approaches are particularly effective for meeting those expectations and rethinking the way governments and citizens interact. In most of the cases, governments across the countries use public sector data as key enablers of broader public-sector transformation and modernisation agendas, and improve citizens’ well-being in a sustainable, inclusive, and trusted way.

Furthermore, today data management and analytics provide the opportunity to assess the efficiency of existing processes and methods and modify them accordingly. Technology and digitisation of societies and governments are generating massive amounts of data. The increased availability, quantity, complexity, and production rate of data have expanded the possible data applications well beyond existing electronic service delivery. Intelligent data usage offers numerous possibilities to fundamentally transform public sector activities— how services are designed, delivered and monitored. Data analytics can reveal social and economic trends and encourage a transition from reactive to more proactive and forward-looking data-based policymaking, and service delivery. Integrated analysis of data concerning service users— both businesses and citizens – and contextual (big) data may facilitate a move from citizen-centred to more citizen-driven public service design and delivery. Better integration and analysis of up-to-date, varied, and detailed performance data could boost public sector productivity by enabling a change in the focus of policy evaluations from one-time assessments in the moment to more effective performance management.

In addition, governments can achieve considerable benefits and productivity gains through better allocation and management of money, time, human resources and materials from sharing data and analysis of data. This may allow government activities to be streamlined with a resulting reduction of operational costs. Furthermore, strategic, tactical and operational decision-making processes can run more smoothly because required data is already available, less time is needed to request or calculate the necessary information. Data-driven public administration offers new ways of addressing the tax gap, lowering procurement costs and detecting illegal and unethical behaviour of public officials, thus increase government revenues, reduce public expenditures and reduce corruption (Ubaldi, Ooijen and Welby, 2019). Availability of data is also essential for citizens to understand that the country is developing. Lack of updated and disaggregated data makes the implementation of policies difficult in the long-term.The targeting effectiveness of social safety net in Bangladesh could be improved through availability of adequate and reliable data (based on proper household survey) about who qualify to be recipients of social safety net benefits. It is known that the major sources of targeting errors in social safety net programmes in Bangladesh are mainly inclusion and exclusion errors. These exclusion and inclusion errors are often linked with targeting low-income families based on household survey data. And this quality of household survey data appears to take place because Bangladesh does not have the required administrative capabilities to screen between the haves and have-nots households (Bashar, 2020).

It is to be noted that DDPS has the potential to boost public sector productivity at all levels: micro, meso and macro levels. (Ubaldi, Ooijen and Welby, 2019). By making data accessible and understandable, public sector can better facilitate stakeholder engagement. This has been highlighted in the United Kingdom’s Open Policy Making Manual. Data provide important clues in understanding problems, engage the public and provide access to insights for improving public services that meet user needs. Most important, data create the conditions for robust, evidence-based policy making. Data analytics allows governments to identify the opportunities available to them to shape policy and services to face the future challenges in a better way. DDPS also results in increased levels of transparency, creation or implementation of new public services, and helps empower citizens and communities.

Realising the importance of DDPS in public policy making and service delivery, countries have embarked upon several initiatives to implement DDPS. Few examples of DDPS initiatives implemented in several countries are as follow:

DDPS-CROSS-COUNTRY EXPERIENCE: The Australian government uses commonwealth data  that must be true, accurate, and reliable if they are to be used to support the agency’s responsibilities and decisions (Sangkachan 2020, p.145). The Auditor-General of Western Australia used a variety of data-analytic techniques to audit payroll and other expenditure data to detect irregularities and fraud. The US President’s Management Agenda (PMA) released in March 2018 expressed its long-term vision to manage and use government data in order to modernise the Federal Government provide better public services and spend tax payer’s money to produce better value (United States White House, 2018[27]). India’s Supreme Audit Institution (SAI) established a new Big Data Management Policy in February 2016. This policy includes the categorisation of data sources into internal (created and maintained by the SAI) and external (that is available from audited entities or in the public domain). Singapore wants its public service to be largely data-driven. The country launched its government data strategy in June 2018 to remove barriers between agencies and win the trust of citizens. After a month, the country suffered a massive breach of healthcare records. This breach led to a government-wide review of data security. In Singapore, agencies were not much willing to share data initially.

The 2018 Public Sector Governance Framework made it clear before the agencies under which circumstances they can share data with each other. It helped dispel concerns agencies had about the misuse of data. The act also makes it clear that accountability of the data will flow with the data (Tay, 2020).

CHALLENGES IN IMPLEMENTING DDPS: A number of challenges hinder implementation of DDPS. These are data availability, quality, and relevance. Thus, although governments are increasingly making data available, these data are not of enough quality and as a result, data analysers tend to spend a considerable time on data cleansing activities to suit their needs (Bulger, Taylor and Schroeder, 2014). The quality of data depends on the purpose of its use. The quality of data thus presents a major challenge for the implementation of the DDPS. This is because well-structured data is imperative in order to effectively respond to a policy need in a data-driven fashion.

Data-driven public dashboard also suffers from some challenges. Matheus et.al (2020) pointed out that one significant difficulty in implementing DDPS is that data in a dashboard are not updated or the dashboard interface is no longer current. If dashboards do not perform well, citizens will abandon them and less likely trust them. This gap indicates the scarcity of a trained workforce that has the knowledge and ability required to successfully analyse and interpret data. This talent deficit is a key impediment to the widespread adoption of data analytics for different countries.

Other barriers in implementing DDPS include lack of feedback loops between public service providers and users, missing data-related skills in the public sector, lack of collaboration between stakeholders, low political priority and organisational resistance in the public sector. Among these, a clear demand from citizens and demonstrating tangible benefits can be used to counteract the barrier of low political priority. Like all other countries of the world in implementing DDPS, Bangladesh has also certain limitations including all these above-mentioned challenges such as lack of data infrastructure and resources make it difficult for corporations to acquire, store and analyse data effectively. Many companies in Bangladesh still rely on manual processes and do not have access to data that would assist them in making decisions. The underlying causes may be a low-quality or erroneous data, or a shortage of funds needed to purchase relevant information from third-party providers. It signifies that governments must support its data collection efforts to ensure data quality and policymaking activities across a variety of dimensions. These include: accuracy and precision, comprehensiveness and clarity, consistency and integrity, completeness, relevance, timeliness and validity.

A DDPS acknowledges the value of data inputs to evaluate and monitor the real-world impact of policies and services. Public sector organisations hold large volumes of data from grass roots level to central government. Moreover, relying heavily on data, performance management for governments serves as the basis for measuring and improving processes and services.

DDPS AND PERFORMANCE MANAGEMENT: Experience shows that performance management for governments is often challenging due to the large number of stakeholders, multiple offices and organisations, and disparate goals that exist within them. But these challenges also create opportunities to concentrate more on developing data-based adequate performance management. In that case, by gaining a better understanding of the public-sector data and utilizing business intelligence tools governments can ensure better performance and productivity.

The collection and reporting of good performance data is the foundation of a useful performance management programme. But it is of little value if this information is not used to make decisions about resource allocation and service delivery. The public-sector offices need to integrate performance data into its decision making. Similarly, data-driven approach in government departments provides a mechanism to build a more coherent view of how services are performed across the public sector. For example, in the case of payments, a disaggregated approach reveals data on the volume of payments, the preference for particular payment methods and the amounts themselves would be held locally in individual contracts and technology solutions.

Thus, a data driven public sector can be considered as a platform for performance management of the government.

DDPS AND BANGLADESH: In the case of Bangladesh, evidence of use of data-driven policy and service delivery is very limited. It has been found that the Government of Bangladesh has adopted few platforms namely the SDG Tracker, National Socio-economic Dashboard and National Covid-19 Data Intelligence Platform for data-driven policymaking and planning (https://a2i.gov.bd). Ministry of Foreign Affairs is also creating a data-driven intelligent architecture for better and faster service across the globe. Among these, the SDG tracker is used to monitor performance in achieving SDG goals, targets and indicators. But the data-driven platform is without data. In the absence of required data, it is difficult to know how the SDG goals, indicators are implemented.

The aforesaid example manifests that although Bangladesh has adopted data-driven platform named SDG tracker, this is inadequate and the tracker fails to capture necessary data to measure SDG indicators. This happens seemingly, due to lack of DDPS in Bangladesh. And it appears these platforms were created in an isolated way for data-driven policymaking and planning.

WAY OUT: Some countries have made significant progress in strengthening the capacity to use data strategically to improve policy making, service delivery or performance management. Individual organisations have also produced impressive results. However, the use of data is not yet viewed — or resourced – as a fundamental means of creating public value. Given that situation, countries need to develop a comprehensive model for data governance and a common framework for establishing such governance that underpins a truly data-driven public sector.  The aforesaid framework should comprise the leadership and vision to ensure strategic direction and purpose for the data-driven conversation throughout the public sector which include government as a whole and within individual organisations. Countries which intends to implement DDPS need to have formal requirements (including legislation and rules) to protect citizens across data collection, storage, sharing, processing and data opening, release and publication. Similarly, principles may be set through enactment of law for an ethical approach to data sharing, access and reuse. As a matter of relevance, it can be mentioned that Canada and the United Kingdom have established mechanism by which citizens and businesses can get an idea about which data government organisations hold about them. Moreover, the government should develop the necessary data infrastructure to support the publication, sharing and reuse of data.

Despite some advances, turning the promise of data into tangible, measurable and consistent outcomes remain largely elusive. The first and foremost requirement for implementation of DDPS is the role of data in creating trust. Public trust in government is a critical factor in citizen well-being but is far easier to lose than to build. With this end in view, countries should generate public value through using proper data in the design of policies, planning of interventions, anticipation of possible change and the forecasting of needs. Thus, these multifaceted initiatives from the end of the governments will increase their own readiness for being a full-fledged Data-Driven Public Sector.

Kowser Nasrin is a public policy analyst. She obtained her higher degree in Public Policy and Management from the University of Melbourne, Australia. nasrinnu@yahoo.com