Home / Services / Data Driven Innovation / Predictive Models Management

Predictive 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 AI-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 AI-driven strategy, the data sources available and your organization capabilities.

7b33087e13b58a9c067b360e0bb51c50

Predictive Models Implementation

3d37c146de105423f2b2b09f944c0855

Selecting the right predictive model is just a small part of the predictive analytics journey. There are at list 4 other tasks that need to be carefully executed for a successful implementation:

  • People alignment: make sure that all relevant stakeholders understand the business objectives associated to AI-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 predictive 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 AI-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 AI-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.