Sign In

Scoping Your Project

Jun 27, 2019

The importance of asking the right question is the first step in getting the most out of your data project. Before digging into your data, you should have an overall goal in mind and one or more question(s) that are both solvable and impactful. Determining this can often be as challenging as implementing the analysis and it is well worth spending a little time on scoping before rushing in.

Fortunately, the University of Chicago Center for Data Science and Public Policy (DSaPP) has developed several tools to help organizations implement data-driven projects. These tools are based on their experience completing hundreds of projects over several years including those implemented through their renowned Data Science for Social Good Fellowship.

Three Fundamental Conditions

The DSaPP approach to scoping projects is based on the premise that three fundamental conditions are already in place:

  1. A problem has been identified that is important and has social impact
  2. Data can solve the problem and the organization has access to the data
  3. The organization is ready to tackle the problem and can act on what is learned

Not sure if these conditions are in place at your organization? Take their data maturity framework questionnaire to find out.

pen on paper

4 Steps to Scoping Your Project

Once you have a problem, the data to solve the problem, and the readiness to tackle the problem, you can focus on scoping the project. The DSaPP approach consists of four steps:

Step 1: Goals - Define the goal(s) of the project
Step 2: Actions - What actions/interventions do you have that this project will inform?
Step 3: Data - What data do you need? What data do you have access to internally? What can you augment from external and/or public sources?
Step 4: Analysis - What analysis needs to be done? Does it involve description, detection, prediction, or behavior change? How will the analysis be validated?

The project that you implement will be based on conducting an analysis (step 4), using data (step 3), to inform actions (step 2), to achieve goals (step 1).

Learn More:

NOTE: Permission to share this granted by Rayid Ghani, University of Chicago