Engaging members with data science

This use case shared preliminary data science findings with members to demonstrate a different view of their metrics.

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Opportunity to use data science share with members a different point of view of their data.

Multivariate analysis: Provide members with more sophisticated understanding of the benchmark data by applying multivariate analysis. Discover the interrelationships between multiple variables in the study.

Predictive analysis: Members are interested in the interrelationship of the sustainability efforts. There is an opportunity to understand the causation that may be correlated to sustainability variables in the study.

External data: Provide members with greater context for the metrics by incorporating external data to understand climate, demographics, GIS/mapping and plant operations data may affect sustainability results. For example, the EPA has emissions and water flow data at a high level of detail that can inform the member’s sustainability strategy.

Facilitate a workshop with members

Engaging members with data science

Member engagement: The TI project gave the Sustainability team an opportunity to start a dialogue with members about their hypothesis and the metrics that are of greatest importance to them. The workshop with members generated 12-15 hypothesis to explore.

Predictive variables: Through our analysis, we identified variables that might be better predictors of future performance and tagged them as valid and invalid variables.

SPSS Training: Provided Morgan Scott and a her analyst with training on using SPSS Modeler so that they can continue to explore her data without restrictions and pursue the issue of interests that the members identified during the workshop.