As a policy practitioner data analyst...

you ask questions, you formulate hypotheses, you create, gather and summarize data. You use a critical approach to understand a problem. You interpret and visualize qualitative and quantitative data, relying on sources that are relevant, appropriate and reliable.

Why is this important?

  • To define and categorize problems.
  • To deal with complex problems.
  • To base your actions on facts and objective information. 

What new avenues does this skill open?

  • Outlining the patterns that define complex problems.
  • Gaining a better understanding of the cooperation and competition dynamics that underlie complex problems.
  • Designing synergistic solutions that help in resolving several issues at once.
  • Forcing yourself to engage in a critical examination of the data to extract information and useful knowledge.
  • Drawing information from various sources of information, including tacit and coded information connected to public problems.
  • Integrating sources of existing data into the policy development process.
  • Constructing a collective intelligence system and orchestrating its development.
  • Developing work processes that make it possible to combine several sources of data in order to connect them to one another.
  • Strengthening the underlying hypotheses. 

Without this skill, what obstacles can present themselves?

  • Decisions made based on emotions and strongly-felt opinions.
  • Inability to learn from a variety of knowledge sources.
  • Inability to deal with complex problems.
  • Difficulty perceiving and understanding layers of connection between data sets.
  • Inability to translate data into useful information.
  • Inability to use the collective intelligence and orchestrate its development.
  • Inability to validate data, use it and give meaning to it in order to support the decision-making process in the pursuit of objectives.  

Examples of behaviours and aptitudes to be adopted

  • Selecting observations that are most representative of the population and then generalizing them.
  • Outlining and clarifying a problem that was not initially defined properly.
  • Deepening your understanding of a problem by asking more detailed questions.
  • Reframing the problem by taking into account the point of view of the stakeholders involved.
  • Understanding the cause of a problem and interpreting its meaning.
  • Understanding the context before formulating questions and hypotheses, all while avoiding your own biases.
  • Gathering as much data as necessary from a variety of sources.
  • Making sure that the data collection methods are relevant, appropriate and reliable.
  • Analyzing and interpreting the data and if necessary explaining the connections based on your interpretation.

Examples of behaviours to be avoided

  • Prematurely simplifying the description of the context.
  • Relying on all sources of data without first taking a critical look at them.
  • Extrapolating from available data, without first considering their complementary nature, quality and comprehensiveness.
  • Ignoring your own biases.
  • Not giving space to new ideas and hypotheses.
  • Not evaluating the validity of a measure and ignoring its limits.
  • Not documenting or disclosing the process you have used to generate and collect the data, assuming that users will be able to deduce it.
  • Considering all types of data on the same footing.