This article was based on the interview with Eric Siegel, founder of Machine Learning Week and author of The AI Playbook for The Agile Brand with Greg Kihlström podcast by podcast host Greg Kihlström, MarTech keynote speaker. Listen to the original episode here:
The interview highlights the importance of bridging the gap between the business side and data scientists in order to successfully deploy machine learning projects in the enterprise. Author Eric Siegel emphasizes the need for business stakeholders to be actively involved in every step of the process, from understanding what is being predicted, to measuring performance, to making decisions based on the predictions.
One of the key challenges in bridging this gap is the discomfort or lack of training that business stakeholders may have when it comes to dealing with probabilities and data-driven decision-making. The speaker stresses the importance of business stakeholders getting a semi-technical understanding of the predictions being made, how well they are performing, and what actions need to be taken based on those predictions.
To overcome this challenge and bridge the gap between business and data scientists, organizations can take several steps. Firstly, it is crucial to involve business stakeholders from the outset of the project and ensure they have a clear understanding of the goals and expected outcomes. This can help align efforts and ensure that everyone is working towards the same objectives.
Secondly, organizations can provide training and education to business stakeholders on the basics of machine learning and data-driven decision-making. This can help demystify the process and empower stakeholders to actively participate in the project.
Additionally, creating cross-functional teams that include both business and data science expertise can help foster collaboration and communication between the two groups. By working together, teams can leverage their respective skills and knowledge to drive successful outcomes.
Furthermore, establishing clear communication channels and regular check-ins between business and data science teams can help ensure that everyone is on the same page and address any issues or concerns that may arise.
Overall, bridging the gap between business and data scientists is essential for the successful deployment of machine learning projects in the enterprise. By fostering collaboration, providing education and training, and maintaining clear communication, organizations can maximize the value of their machine learning initiatives and drive real-world improvements in their business operations.
