Organizational and process pitfalls in data science and machine-learning projects have the potential to derail success and destroy organizationwide confidence in these techniques. Data and analytics leaders should use this checklist to avoid six critical project execution pitfalls.
- Prioritize Data Preparation by Allocating Sufficient Time, Staff and Funding, and Automating Whenever Possible
- Pitfall 1. Devoting Insufficient Time and Resources to Data Preparation
- Maintain a Multidisciplinary Data Science Team Equipped With the Most Compatible, Efficient Tools to Deliver Consumable Results
- Pitfall 2. Mismanaging the Data Science Team
- Pitfall 3. Employing the Wrong Tools and Building Everything From Scratch
- Pitfall 4. Failing to Interpret and Leverage Model Outputs
- Operationalize the End-to-End Analytics Process With Strong Collaboration With IT
- Pitfall 5. Failing to Operationalize Models
- Develop Ongoing Governance for Model Maintenance and Data Monitoring to Ensure That Your DSML and AI Models Produce the Correct Output Over Time
- Pitfall 6. Disregarding Model Maintenance and Data/Model Monitoring
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