Professor Tunji Olaopa

By Tunji Olaopa

As a directing staff at the National Institute for Policy and Strategic Studies (NIPSS), Kuru, I was charged, as one of my responsibilities, with bringing the participants at the senior executive courses (SEC) and other programmes up to speed on recent developments in public administration and management in the twenty-first century. One of the most significant developments, and the point of many excited discussion is public policy decision-making. Given that governments all across the world are judged on their performance of the function of policy formulation and service delivery (of course, the regulatory function is also significant), the policy architecture of any government must factor in intelligent policymaking that will transform policy intelligence revolving around governance and development decision-making processes. And if the public service must facilitate a professional and intelligent policy advice to the government in ways that orient the politics-administration dichotomy in public administration, then due attention must be given to new developments in intelligent policymaking in the twenty-first century.

However, the practical worry I usually communicated to the SEC participants is how, since its emergence, public administrators in Nigeria have failed to give decision science its right of place in administrative and management practices. The cutting-edge application of development in this fascinating field has only been exploited by industrial management. Decision science was significantly facilitated by Chester Barnard. In his groundbreaking The Functions of the Executive (1938), he outlines a theoretical framework that allows us to examine how organizations actually operate outside of any normative or prescriptive requirements. Bernard’s contributions connect between the rationality of organizational, rather than individual, decision-making processes, and the cooperative capacity required to facilitate decision-making. While organizational decision-making is more significantly logical and rational, it is often facilitated by social and strategic contexts, like clear objectives, specialization, the flow of information, etc.

The organizational rationality that Barnard was referring to does not translate into the “perfect rationality” of traditional economic models and theories. This concept assumes that in the decision-making process, the decision makers are fully equipped with the necessary information and data to make the best economic decisions. On the contrary, Herbert Simon undermined this assumption of a perfect decision-making. According to him, human fallibility makes it impossible to achieve the perfect standpoint—having the capacity and the resources to gather and process all the information and data required to make perfectly rational decisions. There is just too much uncertainty involved in how we decide, what we decide and the alternatives available in resolving our decisional dilemma. This therefore implies that in critical decision-making, cognitive limitations, policy complexity and resource constraints, Herbert Simon argues, that decision-makers only pursue satisficing rather than optimal solutions. A solution is satisficing if it is founded on rational and pragmatic principles and circumstances.

Given the many troubles and predicaments of the modern world, from climate change to political processes, and given the fact that we do not have all the answers we seriously need. This is where artificial intelligence and big data become the starting point of our attempt to understand before we can even predict or act. It is in this sense that decision science has learnt a lot from Barnard’s and Simon’s understanding of decisional rationality, and how computers and machines generally can contribute to the decision-making dynamics. We can therefore immediately see how the idea of a data-driven decisional strategy can assist governments across the world, and especially in Africa, to come to terms with critical policy intelligence required to backstop governance and development policies.

There is no gainsaying the fact that we now live in the world of big data and artificial intelligence. This is the age of the machine. The operation of social media platforms, their algorithmic underpinnings, and the emergences of enormous databases all indicates that the amount of data available in contemporary world is beyond staggering. It has been reported that Facebook, the social media platform, warehouses “300 petabytes of data about its 2 billion active users, or 150 megabytes of data per user.” Of course, the Cambridge Analytica scandal that ignited concerns about the misuse of data raised critical issues around the ethics of data usage. However, that same concern attends the extent to which governments can ethically deploy data-driven policy intelligence in the pursuit of the well-being of their citizens. And even beyond this, governments all across the world are now confronted with multiple dimensions of polycrisis that disarticulate all efforts at making sense of their governance and development circumstances and objectives. The deployment of artificial intelligence and big data—from financial transaction data to the internet of things (IoT), and so on—cumulate into the enhancement of the credibility of those governments who have the courage to exploit their usage for policy intelligence.

However, before any government can get to the zenith of credibility and legitimacy, there is the outstanding and fundamental issue of extracting meaning, purpose and direction from the enormous amount of data available. This involves designing the models that extract and interpret the data in ways that enable government to explain and predict things, situation and circumstances in the real world. Data scientists, in other words, assist the governments in formulating hypotheses, based on the identification of trends, patterns and flows that investigate current problematics, anticipate future challenges and proactively energize government policies. In a world where climate change has undermined lots of policy intelligences, where geo-spatial data analytics backstops the government’s infrastructure masterplans in terms of identifying geographic patterns, trends, dynamics and disparities so as to be able to initiate creative policy designs and formulations that prioritize investments, allocate resources more effectively, and facilitate service delivery.

We now need to confront the necessity of deploying artificial intelligence and big data at the level of public service institutions, especially in line with the imperatives of institutional reforms that have the objectives of bringing these institutions up to speed with modern technologies, procedures and administrative imperatives that can enhance the government’s capacity to efficiently deliver goods and services to the citizenry. This deployment must however go beyond the default level where pubic institutions are just compelled to adopt any reigning innovation without a significant feasibility study in terms of the fundamental utility of the innovation within an overall organizational reform blueprint. For example, as part of the institutional imperatives, public institutions have to factor in the challenges of privacy, security, data governance and data ethics.

What differentiates a good administration from a bad one is the intelligent policymaking capacity that is founded on policy intelligence. Within Nigeria’s governance framework and policy architecture, a reformed policymaking founded on big data and artificial intelligence demands that the public service adopts a change management programme that must first give attention to the (re)professionalization of the workforce, especially the senior executive service. The SES must be beefed up as a multidisciplinary elite team that is charged with the articulation of a strategic policy intelligence. This allows the SES to not only play a critical role in policymaking function, but also facilitate the strategic deployment of big data. Recruitment into this top-level administrative corps will be on the basis of intelligence, professionalism and performance rating.

The establishment of the SES will entail modifying the generalist composition of the workforce through a competency mapping that create a hub of specialists and experts specifically reskilled for the demands of new technologies, artificial intelligence, big data and data science. The second dimension of the change management is to link this generic professionalization to the reform of the department of planning, research and statistics in the MDAs. The DPRS could then serve as the effective policy hub that mediates the articulation of the demands of decision science and big data around the dynamics of data culture, data availability and action research capacity to interpret such data as input to policy and feedback.

Each DPRS will have to recruit, train and incentivize new officers who will be put in charge of regulating the new innovation. It is not just enough to innovate; it must be a guarded reform that is founded on a blueprint of how artificial intelligence, machine learning, data science and new technologies factor into the governance and development objectives of the Nigerian government. This will necessitate, for example, collaborating with research-based think tanks and institutes in ways that further enhance the quality of policy decision and the data dynamics needed to formulate policy intelligence.

The biggest requirement for harnessing the requirements of decision science is the political will of governments to go all out in traversing the challenges of institutional reforms, and especially of exploring and exploiting the intricacies of big data analytics in terms of its capacity to improve decision-making and the efficiency of service delivery to Nigerians. There is just no other alternative to the transformation of the policy architecture of the Nigerian government than the political willingness to become proactive in the institutional reform of the public service. Given the economic and governance challenges that the administration is confronting, the reality of the time is really auspicious with regard to the reform opportunities that global best practices have presented the Nigerian government with. There is no alternative to reform, and decision science is the opportunity to make it work for democratic governance.

* Prof. Tunji Olaopa, Chairman, Federal Civil Service Commission, Abuja & Professor of Public Administration. [email protected]