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.
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.
Key term: data-driven policy
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’.
Turning Data to Policy: Combining policy and data solutions to address development challenges
Step 3: Policy Formulation and Validation
Policy formulation is directed towards the identification of shared objectives and alternative options for intervention in relation to the problem defined in the previous phase.
In this step, predictive and prescriptive analysis methods can be employed. For a given policy problem several possible policy measures might exist. In this step, the pros and cons of every measure are investigated and eventually one policy measure is chosen. Experimental iterations in the policy making process can be used to diagnose the effect of different scenarios. At the same time, these iterations are a good opportunity to gather data, validate, and refine predictive models.
You now have your policy issues and pain points identified, some corresponding data (and other) resources mapped, and you have some policy solutions and measures identified. It’s time to start putting these pieces together to formulate your policy as follows:
Once you’ve formulated your data-driven policy, you may decide to get, policy validation done by socializing and embedding feedback loops for your policy intervention with political leadership, citizens, and other stakeholders, using data visualization maps that display the outputs of the proposed policy interventions.
Some value addition approaches you may want to consider in your policy formulation:
Step 4: Policy Implementation
After the scoping, design, and formulation phase of turning data to policy, you’re now ready to roll out your policy implementation. Governments have their own internal, customized guidelines and protocols for policy implementation. Unlike traditional policy implementation strategies, your data-driven policy intervention needs more concurrent planning and monitoring. Begin by designing a Policy Implementation Plan (PIP) and an implementation system/platform that enables you to track progress of your policy both quantitatively and qualitatively:
The implementation plan is necessary for policy implementation to be as effective as possible. While doing this the opportunities for data-based monitoring activities should be considered. When a data collection plan is included in the implementation plan, data collection infrastructures need to be designed together with data analysis for the implementation step.
Since you’re now familiar with the step-by-step process of turning data into policy, let’s apply the steps to a particular policy challenge to find solutions to your policy challenges.
You have recently joined as the head of the Youth Employment Department. You realize that there are limited policy interventions on mapping skills of the youth to help them understand their career choices better - whether they are employable for jobs or possess entrepreneurship skills? You want to use data and take a policy decision that helps the youth make informed choices about their careers keeping in the mind the last decade where the skills needed in the market have considerable evolved.
You’re about to design policy intervention(s) on mapping skills of the youth to help them understand their career choices better - whether they’re employable for jobs or possess entrepreneurship skills.
Here’s how to apply the data to policy navigator steps to this policy problem:
Step 1: Define the problem
You have defined the problem and listed all pain points of the youth employment policy:
Step 2: Identify data sources
Step 3: Analyse the data
When looking at the data, you find:
Step 4: Combining Policy and Data
Step 5: Policy Formulation and Validation
Based on the scenario above, you could plan policy formulation and validation as follows:
Your mandate is to bridge the skills mismatch and improve the rate of unemployment in the country. You aim at targeting X% rate improvement and Y number of youth making informed career choices. After setting the vision, mission and KPIs you could move backwards to design an action plan.
You ensure you’re looping in all stakeholders in the employment ecosystem, especially the ones who could help you with data collaboration and chart out terms of references and scope of work towards achieving the mission of X% rate of unemployment by bridging skills mismatch of Y number of youth. Together, with stakeholders, you design policy interventions to meet your targets:
Step 6: Policy Implementation
Based on the platform design, existing policy interventions, and human resources delivering your policy on the field, design a policy implementation plan. Different countries have a different set of policy implementation strategies and protocols. Set goals – short-term, medium-term, and long-term for your policy. When you roll out implementation and start getting interim data, you’ll be able to take larger policy decisions:
Now that you have designed and formulated your policy, you want to ensure its smooth implementation and monitoring as well as its success with impact. Read our next pieces on: