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.
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