7 Ways You Can Improve Data Science Project Outcomes

If you have embarked on generating insight from your operational data using data science or machine learning you might have experienced some of the following challenges:

• Slow progress or lack of results

• Missing data or data quality issues

• Lack of insight or failure of the initial hypothesis

• Inability to turn insights to actions

These situations are very common and contribute to high failure rates of data and analytic projects, but they can be mitigated to achieve better results. Integra team has delivered many successful projects with solid business value by following these 7 rules:

1. Start with the end in mind and develop a simple problem statement

2. Reduce the scope of your data collection

3. Establish technical sessions between the data scientist and subject matter experts (at least twice weekly)

4. Iterate. Coach your team to build the first minimal solution and iterate for improvements

5. Accept failure as a learning but fail fast to avoid incremental cost to the project

6. Have an agile plan and review it every week to update the tasks and activities

7. Communicate results early and frequently

If you are a project manager or just interested in further readings, we recommend checking the MLPL process described at amii's website.

Integra has also developed several project accelerators that can be utilized to speed up the data science initiatives and identify risk early. Our best practice guidelines used by Integra solution leads are designed to catch project issues early resulting in higher success rates.