Digitization Isn’t Enough: Why Policy Needs Data Science to Drive Change

The advent of the internet has transformed all aspects of human life. From social media and ERP systems to cloud computing and online streaming, it has fueled the digitization of existing industries while creating entirely new business opportunities. The next frontier undergoing this digital transformation is public policy and governance, with countries already digitizing their departmental and ministerial processes. Take India, for instance. The country will be conducting the world’s largest, and its first ever fully digital census in 2026-27. Prior to that, the country extensively used the CoWIN digital platform to trace, track and administer more than 2 billion doses of Covid-19 vaccine. To enable data driven policy discourse, the government also launched the Open Government Data (OGD) Platform in 2012, and has made significant improvements in it since then.

That more and more government data is available digitally, is no surprise. But can it be accessed easily? More importantly, can it be used in the format it is available in? Maybe not. In simple terms, policy data is messy. Unlike financial or scientific datasets, it is often semi-structured, fragmented, and buried within layers of government portals. Policy data can be of multiple types, such as, administrative data, survey results, regulatory & compliance filings, budget & expenditure records, geospatial overlays and policy texts among others. Additionally, multiple tiers of governments, departments, agencies and institutions develop and follow different data portals and coding schemes, suited to their needs. It is also important to note that policy data is produced for program administration, and not statistical analysis.

Why Messy Data Isn’t Always a Bad Thing

However, this messy data that exists in multiple forms is not necessarily a negative thing. Such messy data encourages skepticism, iteration, and as a result, better design. The rise in data-driven policy making requires policy professionals to embrace the available data and develop tools that adapt according to the data available, instead of waiting for perfect data. In this article, we explore, through a case study, about how professionals can use data science to develop tools and systems that use policy data in the manner they are available – layered, inaccessible and messy.

Accessing data through government portals is often a tedious and time-consuming process. With most portals built in drop-down format, accessing yearly, or monthly data for a time period for multiple variables (like crops or vehicle sales) for different geographical locations (state or district level) can take weeks and months, requiring significant human effort and severely slowing down any meaningful analysis of the data. What should ideally be a simple, bulk download turns into hours of repetitive clicking and searching. In our recent work, we built a full-stack, human-less, and scalable system to extract Self Help Group data from the National Rural Livelihood Mission portal, arguably one of the most convoluted public data infrastructures in India. The problems at hand were many. Like most government data portals, it lacked an API, requiring one to select dropdown values, paginate through lists, and parse HTML tables to get the data. Additionally, the data we required had a deeply nested interface due to a complex administrative hierarchy (state → district → block → panchayat → village → group → member). Further, data is rendered through dynamic JavaScript calls, requiring precise reverse engineering of front-end logic (e.g., GroupList() functions). To access this data within a few hours and without human intervention, we treated this scraping project not just as automation, but as a data engineering and system design problem

Building an Automated Data Pipeline

We designed the process like a feature engineering pipeline in machine learning where every piece of data (state, block, group, member) was treated as a feature. We stored and transformed the data into Pandas DataFrames with consistent schemas (column names, data types). Not just that, we validated and logged errors in real-time to ensure that missing or incomplete data was identified early. Using Python’s threading and multiprocessing modules, we created a high-performance pipeline that could handle parallel block-level processing, retry and recover from unresponsive dropdowns automatically without manual intervention, and scale easily with more cores or machines if needed. We designed it as an ETL (Extract-Transform-Load) pipeline that was optimized for a human-agnostic setting.

To ensure data quality and system reliability, we incorporated a multi-layered logging framework. We built three distinct output layers that worked together like a monitoring dashboard. The first was output_df, which contained the cleaned, structured member-level data that could be used for analysis. The second was debug_df, which logged all errors encountered during the run, such as when a dropdown failed to load or a server request timed out. Finally, missing_df recorded the gaps in the dataset (for example, if a certain village or group had no accessible data). These diagnostic layers made the system more transparent and allowed for quick troubleshooting, targeted reruns, and continuous improvement of the pipeline.

The distinguishing feature of our system was its design that led to minimal manual input. All configuration parameters, such as the state, district, or block to scrape were specified in a simple control Excel sheet. Once the process was initiated, the pipeline automatically navigated through all dropdowns, handling form submissions, extracting and cleaning the data, and finally exporting the results into structured files without any further human interaction. This resulted in the development of a fully automated, “human-less” data pipeline that could run unattended, saving days or even weeks of manual effort. Figure 1 illustrates the process of developing the data pipeline.

Impact and Applications

Once the SHG data was retrieved, it was available for users to perform analysis and generate actionable insights. Users were able to generate real-time dashboards that can track which districts are lagging in SHG formation or credit linkage. This dataset was also accessible to students, NGOs, and think tanks in a ready-to-use format, saving their valuable time. The implications go beyond NRLM. This approach could work for multiple other government data portals, like, PMAY, MGNREGA, or NRHM. If such pipelines are standardized, open-sourced, and scaled, we can move towards a future of Policy Data-as-a-Service (PDaaS), where

  1. Governments can offer bulk, machine-readable data with APIs, generating new source of revenue for them,
  2. Researchers and analysts can plug into live data streams, minimizing the time spent on retrieving data manually
  3. Insights flow faster, feeding into more responsive and evidence-based policies.

Conclusion:

Public services and governance is destined towards moving online. While governments are digitizing public services to universalize their access, the potential of data so collected remains overlooked. This data can be used by administrators and policy analysts to improve public service delivery, frame data-driven policies and adapt them real-time based on flow of data. There is no doubt that governments must design data infrastructures that are usable, accessible, and analytics-ready. However, till then, policymakers must embrace the messy data and harness the power of data science to develop automated ETL pipelines. It is important for governments, policy think tanks, NGOs and policy schools to invest in data engineering skills and collaborate across ecosystems, sharing tools, code, and standards that make public data easier to work with. As discussed through the case study, data science can act as a bridge between messy public data and meaningful policy decisions.