The Paradox of Data Based Governance
Context
- Data-based Governance’s Challenges and Opportunities
- Governance based on data
- Data is now regarded as the new currency driving governance.
- Data is at the heart of everything, from hunger and other indexes to the caste census.
- How it is gathered, evaluated, and evolved into an index is hotly discussed and often contentious.
- The use of data in pandemic management, including as testing, vaccination, and tracking recoveries and fatalities, has exacerbated the data issue.
Evidence-Based Policy (EBP)
Because of evidence-based policy, data-driven techniques have become increasingly popular.
The EBP is thought to be a logical style of governance that makes decisions based on objective evidence rather than external forces.
“The pursuit of EBP is founded on the idea that policy decisions should be better informed by available evidence and should incorporate logical analysis,” Sutcliff and Court write.
Because public resources are generally sparse or limited in developing nations, EBP is stated to be vital.
Both the data and the data gathering procedure must be scientific, rigorous, and validated in both the collection and analysis processes.
The Use of EBP Has A Long and Illustrious History
When looking at the history of data collection, it’s easy to see how European countries acquired vast amounts of data on a variety of variables in the early nineteenth century, which often didn’t lead to any significant analysis.
The analysis of Foucault’s “biopolitics” into “overt” and “subversive biopolitics” in the Canadian context, where the first census of the population took place in 1666, shows how the overt biopolitical agenda, i.e. tax incentives for larger families, did not have the desired effect, but the subversive biopolitics, in the form of “categorisation” of the population, took root and continues to this day.
Analyzing the historical perspective is crucial because it demonstrates how “data regimes, statistics, and probability” have influenced state development.
Furthermore, the digital generation’s entire data collection process is dissimilar from 19th-century procedures.
Information and Communication Technologies (ICTs) and Their Role in Policymaking
As it reconfigures interactions between states, subjects, and people, information and communication technologies (ICTs) have had a revolutionary impact on the way data is now understood.
Citizens function within the frameworks of big data, machine learning, and algorithms, ignorant of how these digital interfaces convert them into data to be exploited by unknown entities.
It is important to distinguish between data politics and “data politics” in this context.
New entities, such as multinational businesses that control ICTs and social media platforms, are growing more powerful than the state in the age of data politics.
This is concerning because, unlike the checks and balances that limit the state’s power, these massive, international firms are neither bound or held accountable by any such processes.
Governance Based On Data
The collection of enormous amounts of data about the public by the state through censuses, surveys, and now digital platforms has gained traction in the context of EBP.
The goal is to make it easier to use research and evidence to make practical financing decisions.
Goal: To continue to invest in what has previously been shown to increase citizen outcomes.
The goal of data-based governance is to develop a system of reliable and validated data, as well as the infrastructure that goes with it.
Because governance results are a blend of tangible outputs and intangible processes, they are difficult to measure.
Only measuring tangible outcomes while ignoring immaterial processes leads to confusing results.
For instance, while attempting to assess women’s participation in a gramme sabha, not only the number of women who participated (outcomes) but also the nature of their participation (process) should be considered.
CHALLENGES:
States amass massive amounts of administrative data. However, because administrative data is not reviewed or updated, a considerable amount of these data goes unused or underutilised.
Various agencies gather the same data using different identities, making data consolidation problematic.
Administrative data is normally kept private and is not accessible to the general public or researchers for review or study.
Governance measurement is a difficult endeavour. Especially the aspect of governance dealing with law and order.
EXAMPLE:
Few studies employ estimates of crime rates (ECR) as an indicator, which examines the total number of recorded criminal cases.
Other studies quantify worker satisfaction using estimates of industrial disputes and strikes (EIDS).
In a similar vein, another study uses complaints regarding police behaviour as an indication.
Uncertainty is a problem.
For instance, while the central government’s education assessment ranks Tennessee fourth in educational attainment, another indicator shows that 27 of the state’s districts are educationally backward.
RECOMMENDATIONS:
If data-driven governance decisions are to be made, a system of good, robust, and trustworthy databases is required.
There is a need for a decentralised data collection system in which governments develop their own databases from the ground up.
States should invest in both human and technical infrastructure, as well as quality assurance methods, to ensure that policy decisions are based on reliable data.
Validated and scientific data are required for data-based governance.
To achieve equality and equity, policymakers should use data intelligently.
Conclusion
The mechanisms of governance are being compelled to become data-centric in the age of a data-driven world. Data-driven governance or policymaking is a step in the right direction in this situation.
However, in order to make data-centric governance more dependable, credible, and transparent, the issues connected with it must be addressed.
