Instruction article objective:   

Turning data into policy is often looked upon as a task partially or completely outsourced to external entities. While experts and data scientists play a crucial role in turning data into policies, the most important role in this ecosystem is played by policymakers who design and deliver crucial mission-oriented policies. Once you have managed to define the problem, identify the data sources, and refined it for analysis and visualization, the next step is to design/redesign your policies using this data. This article gives you an overview of suggestive steps you can take to turn your processed and refined data into a resilient and future-fit policy.

Structure and Content   

How to get started:

Definition and Differentiation: A traditional policy v/s data-driven policy

A traditional policy is generally defined as a rule, a regulation, a law, an administrative action, or an initiative led by governments at all levels. The traditional policymaking cycle is a full-fledged process that involves assessment of previous policies, systems, and services; design and delivery of new policies and rules, validation and political approvals, implementation, monitoring, and impact evaluation of these policies with an objective to achieve short-medium and long-term goals of a particular department/ministry. Whereas a data-driven policy is a set of rapid policy actions that lead to improvements in the  functioning of policies and influences budget decisions leading to cost-saving and optimum utilization of resources in the right geography, on the right challenge(s), and for the right set of beneficiaries within appropriate timelines. With the Data to Policy Navigator, our aim is to support you with the journey of translating data to policy actions (and not formulating an entire policy from scratch). Data-driven policy actions help you with evidence-based decision making, supporting ‘proactive’ policy decisions rather than ‘responsive’ decision making. Backed up with data analytics and visualization, your policy actions won’t just help you save time and resources, but also help you build better political buy-in and demonstrate deeper impact.  

Turning data to policies: What does the task mean for you as a policymaker?

As a policymaker, designing and delivering effective policies is your key role. What comes to mind when you think of non-traditional and more efficient ways of designing policies by turning existing data into policies? Few ideas you may be familiar with in more recent times would be policy dashboards, algorithms, and modification of public service delivery systems, platforms, and so on. Most of these words are so technical that as a policymaker, it’s obvious for you to outsource these tasks. While the task of using the data and codifying it in the form of policies, using platforms, stacks, algorithms, workflows, AI, etc. continues to remain technical. The most critical task is making functional use of data in the overall policymaking process. In your policymaking journey, you’re either trying to build on or improve existing policies. To turn your ‘data to policy’ journey simpler, the navigator will help you recap and take the next steps towards turning data to policies, using an example. Here’s how you can get started:  

• Step 1: Quick Recap

In the previous section of the navigator, you learnedhow to define the problem. By now, you have a strong understanding of the pain points of your policy. You’ve defined the problem(s) in the policy ecosystem, you have your data ecosystem mapped, and you have clearly identified the gaps by now. Until this stage, you were focussing on building data partnerships and leading your team to support you in analysing and visualizing the data. As you move ahead into the actual policy design stage, you’re all set to start putting different pieces of the puzzle together to help you design your policy.  

As we begin blending your policy pain points with data, you’ll start to experience how you can leverage data and convert data-driven policy into a win-win opportunity for yourself as well as all stakeholders of the ecosystem.

Step 2: Combining Policy and Data

As a policymaker, you want to avoid reinventing the wheel and try working on a cost-effective policy design, unless you’re designing a policy for a completely new theme or sector. Having most of the ingredients in place to turn data into policy, it’s time to begin a collaborative policy design exercise and combine your policy with data. You may like to begin this process by involving multiple line ministries, your target beneficiaries/end-users, and other relevant stakeholders in the policy design process to ensure policy coherence, collective impact, and optimum utilization of existing resources and functions. As against traditional policy designs, your data-to-policy design comprises of programme actors as well as data actors. The task is to explore how data can help solve your policy challenges and how you can combine data insights to make your policies ‘proactive’ rather than ‘reactive’.  

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