Mr. Al-Dhubaib is a data science thought leader in Northeast Ohio. He has led data science teams to help organizations jump start their data science initiatives and begin using AI solutions to impact the bottom line. Al-Dhubaib regularly speaks on topics in machine learning and what organizations can do to leverage their data for impact.
He has received both national and international recognition for his work in predictive modeling and entrepreneurship. As the first data science graduate from Case Western Reserve University, he works tirelessly to advocate for careers and educational pathways in data science and contributes to workforce development initiatives throughout Northeast Ohio.
Case Western Reserve University, Bachelor’s Degree, Data Science
“While it’s fun to pursue the latest in artificial intelligence and machine learning trends, organizations must constantly question the business utility.”
Upcoming Speaking Engagements
AI is here to stay – organizations are using it to gain a competitive advantage in marketing, sales, customer service. However, more than 50% of marketing AI and sales projects fall flat and don’t yield any return. This discourages business from getting AI initiatives off the ground. From navigating stakeholders to structuring the right team and identifying pitfalls that doom a project to failure, we will share lessons learned from deploying AI solutions that consistently result in impacts on the bottom line.
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Cal Al-Dhubaib on AI Marketing
Successful companies tend to leverage two different configurations for data science teams. Embedded – where data scientists are embedded as subject matter experts within specific business units. This tends to work for larger organizations that can dedicate one or more full-time data scientists to fully understand the business needs of each business unit. Centralized – where data scientists function as an internal consulting team, serving the various needs across business units within an organization. This tends to work for smaller organizations with more limited resources.
As many organizations rush to implement analytics solutions, this case of ‘data shaming’ is not all that uncommon. There is pressure to reduce noise and focus on only a handful of critical metrics that drive the business forward. Good decisions can only be made based on good information. The organization had the right technology in place, they selected what seemed like a reasonable target, and they were able to pull the numbers.
Remember to have a look at our list of who is who in AI marketing.