Updated: May 25, 2020
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:
Start with the end in mind and develop a simple problem statement
Reduce the scope of your data collection
Establish technical sessions between the data scientist and subject matter experts (at least twice weekly)
Iterate. Coach your team to build the first minimal solution and iterate for improvements
Accept failure as a learning but fail fast to avoid incremental cost to the project
Have an agile plan and review it every week to update the tasks and activities
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.
Contact us today to learn more about our solutions and methodologies