Every organization, public or private, makes numerous decisions every day. Daily life in an organization is a series of continuous decisions on how to allocate funds, what to buy and sell, who to talk and communicate with, and what tasks are to be completed. While good decisions bring prosperity to an organization, bad ones can be fatal. What does one mean by “good” or “bad” decisions for the public sector? Good decisions are made objectively and rationally, overcoming the parochial interests of particular social and economic segments. A decision is objective and rational when it is based on evidence through a scientific, systematic analysis of valid, reliable data. As the value of data-driven information is being rediscovered, “data analytics” has become a catchphrase for every public and private organization. Rapidly changing environments are requiring organizations to make well-informed decisions supported by data analytics.
While the private sector seeks to use data analytics to improve productivity and uncover new business opportunities, public-sector organizations expect to utilize them to innovate work styles as well as design and deliver better-quality service to citizens. However, the public sector in many countries is slow and unprepared to optimize the valuable opportunities derived from data analytics. In new data-driven environments, as best represented by big data, the private sector is moving forward fast in data analytics not only to improve its managerial and operational efficiency but also to understand the needs of customers and predict market behavior.
The public sector is not an exception in this regard because some countries have also applied data analytics to policy areas including traffic control, healthcare services, and others to capture the complex nature of problems and to come up with better solutions. Public transport policy is one example. The transport authority is able to collect real-time traffic data from various sources such as road surface sensors, traffic cameras, in-vehicle GPS, and SNS messages. New analytic techniques allow the transport authority to process and analyze these data to provide drivers with real-time information about traffic congestion. When data on traffic density, volume, and speed are integrated with weather conditions and road quality, the transport authority can predict the risk of accidents and, accordingly, alert drivers to be cautious. This kind of a big data-driven intelligent transportation system is one component of smart city infrastructure.
Despite its efforts, however, the public sector has not taken full advantage of the benefits of data analytics. If one agrees on the importance of data analytics, the success of public-sector organizations can be determined by the extent to which they are able to extract new knowledge and insights from analyzing data.
Meaning of data analytics
In ordinary usage, “data analytics” is often interchangeably used to refer to “data analysis.” Merriam-Webster’s Dictionary defines “analysis” as “a detailed examination of anything complex in order to understand its nature or to determine its essential features” and “analytics” as “the method of logical analysis.” When these lexicographic definitions are applied to the field of data science, the scope of data analysis becomes broader to include data analytics, even if they are interconnected. To be more specific, data scientists conceptualize data analysis as the process of “inspecting, cleansing, transforming, and modeling data” to discover useful information.
From an organizational perspective, the ultimate purpose of data analysis is to maximally utilize the results of analysis to gain insights for good decision making. By contrast, data analytics is related to the effective use of tools and techniques. Although conceptually different, data analysis and analytics cannot be separable in actual practice. In particular, if any organization wishes to derive value from data, the patterns and relationships of structured or unstructured data should be analyzed through applying relevant analytical tools and techniques. To accomplish this, basic knowledge of mathematics and statistics combined with computer skills is indispensable.
Significance of data analytics in the public sector
Public organizations, whether they are government agencies, state-owned enterprises, or other entities, comprise a key pillar in the national economy. Over the years, the public sector in many countries has grown rapidly, accounting for a significant portion of employment and spending in the national economy. Considering the increasing demand for diverse public services, the role of the sector will not diminish, at least in the near future. This is why the public sector should be run in a more efficient, effective, accountable, responsible, transparent manner. One of the approaches that the public sector can take to meet these demands is to make decisions based on evidence.
In the past, when reliable data were limited and data collection and analysis required time and money, evidence-based decision making was not easily implemented. Due to the advances of a new environment where real-time data are generated exponentially through, among others, social media, web searching, and the Internet of Things, public organizations are now able to collect data at an unparalleled speed. Furthermore, new data analytic techniques allow the public sector to glean insights from data with which it can make better decisions.
It is certain that these changing environments provide unprecedented opportunities for the public sector. If it fails to embrace such changes, its “business as usual” approach, without significant progress, will weaken competitiveness. Under these circumstances, it is not a matter of choice but a must for the public sector to embed data analytics deeply into its operations and management to achieve its mission goals.
The usefulness of data analytics goes beyond better-informed decision making. The public sector faces difficult challenges that arise from the expectations of people who want to find new data-driven business opportunities. In other words, the private sector believes that public data can provide the market with a chance to create value, demanding a higher level of open government. While how to use public data is the choice of the private sector, the public sector, at the same time, must utilize data analytics to determine the kinds of data to be released to the public.
Key challenges of data analytics in the public sector
Despite the importance of data analytics, the public sector in many countries is not yet ready to mine value from public data. There are several barriers that prevent the sector from realizing the potential gains of data analytics. Below are some foundations of the effective use of data analytics which are sometimes neglected by the public sector.
Understand the basics of data analytics
There are many new tools available allowing the public sector to analyze data. For example, SAS and R programming are common tools in statistical analysis and data modeling, while Tableau public and Python are widely utilized for data visualization. Although data analytics can be carried out by in-house data scientists and external experts, public-sector employees must possess basic knowledge of the process, in which statistics is a core discipline. Without knowing how data are collected, analyzed, interpreted, and visualized, their capacity to extract useful insights for better decision making cannot be ensured by merely observing the results from data analytics.
Build digital infrastructure to collect, manage, and open data
Data analytics start with data collection. As the public sector carries out various functions, it has ample access to diverse sources of data. Despite the potential for data collection, the public sector at times does not pay attention to the standardization of data, for which a common format and structure are necessary. If there is no data standardization, it is difficult to integrate data and extract valuable information from it. In addition, collected data should be digitized and stored at an organizational as well as national level. To do this, as many private organizations do, the public sector needs to appoint a chief data officer (CDO) and establish a team dedicated to managing data quality. Collected data should be open to public access. Data release not only increases the transparency of the operations of the public sector, but also creates new business opportunities.
Create a data integration and sharing culture
The public sector must realize that data are an important asset to design better policies and implement them more effectively. To take advantage of this, data collected by multiple units and organizations should be unified through cleansing, mapping, and transformation. However, data in many cases are fragmented and spread out within an organization and across organizations, which hinders effective use. Among the reasons for data fragmentation lies a perception that data and information are in the power of data holders and seen as proprietary, not to be shared. Both organizational leadership and a CDO should make efforts to eliminate this antiquated perception and to create a new culture of data sharing.
Build the capacity for data analytics
New environments, led by big data, are revolutionizing the volume, variety, and velocity (aka the 3Vs) of data. Accordingly, new tools have been developed to assist organizations to conduct data analytics easier and faster. One of the prerequisites that enable the public sector to incorporate these new analytical techniques successfully into its organizations is continuous investment in the capacity for data analytics among employees. Building data analytic capacity is not restricted only to the learning of fast-changing analytical tools. It includes a clear vision that aligns data analytics with organizational mission, basic knowledge of statistics, and data ethics.
How data are handled, managed, and analyzed is essential for the public and private sectors to manage day-to-day operations more efficiently and to make well-informed decisions to serve citizens and clients better. Considering that the problems to be solved are far more complex and multifaceted in the public than in the private sector, data analytics are seen as an unavoidable path that every public organization must take. There are multitudes of examples and practices that demonstrate how the public sector has benefited from the use of data analytics. For organizations in the public sector which are still reluctant and unready to deploy data analytics in their decision making, it is never too late to realize the gains of data analytics and to start from the basics.
Prof. Jin-Wook Choi is the resource person of the APO e-learning course on Data Analytic Courses for the Public Sector. Course description is available here.
Prof. Jin-Wook Choi, who received a Ph.D. in Political Science from the University of Chicago, is a professor in the Department of Public Administration and Director of the Institute of International Development Cooperation, Korea University. His expertise includes regulatory policy, public-sector corruption and integrity, government reform, and official development assistance. Professor Choi is actively engaged in public affairs as a policy adviser to government ministries in the ROK and as an international government consultant.