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Data Science Models Management

Choosing algorithms and models you can trust

There is most of the time a trade-off to be made between models interpretability and prediction accuracy. As a matter of fact if managers don’t understand or trust predictive models the risk is to implement data-driven operations that won't be supported by the organization.

Least complex models can sometimes bring better results as they are easier to implement and maintain. The selection of the most appropriate models always need to be made according to business objectives, the organization culture and the capabilities and data maturity of the managers that will integrate new data-diven features in their operational processes.

Comfiz consultants and Comfiz partners can help you analyze the benefits and risks associated to each type of algorithms. They will work with you to select and implement models that have the best fit with your data-driven strategy, the data sources available and your organization capabilities.


Data science Models Implementation


Selecting the right algorithms and stack is just a small part of the data science journey. There are many other tasks that need to be carefully executed for a successful implementation of data science models:

  • People alignment: make sure that all relevant stakeholders understand the business objectives associated to data-driven services and that they don't consider them as a threat to their expertise. Senior level management engagement is key here.
  • Data availability: smart models cannot deliver good results with data sets of poor quality or incomplete data sets. Data sourcing and data integration are key success factors of all data-driven processes.
  • Training: unfortunately this is often an overlooked step. People may be reluctant to rely on processes they are not familiar with. Training is essential to develop your company’s data capabilities and avoid a mismatch between your organization’s existing culture and the will to exploit data-driven processes successfully.
  • Maintenance: models need to be evaluated on a regular basis to take into account the evolutions of your competitive environment and customers behaviours.