Companies that excel at advanced analytics and data science maximize the value of their data. They unearth hidden opportunities and become innovators in the industry. Although each organization has different goals, the underlying processes and tools to become successful at analytics remain somewhat the same. In this episode, Alan Jacobson explains them one by one and finishes off with his top three recommendations.

Alan Jacobson is the chief data and analytics officer (CDAO) of Alteryx, driving key data initiatives and accelerating digital business transformation for the Alteryx global customer base. As CDAO, Jacobson leads the company’s data science practice as a best-in-class example of how a company can get maximum leverage out of its data and the insights it contains, responsible for data management and governance, product and internal data, and use of the Alteryx Platform to drive continued growth.

Alan was recognized as a top leader in the global automotive industry as an Automotive Hall of Fame Leadership & Excellence award winner and an Outstanding Engineer of the Year by the Engineering Society of Detroit, and works with the National Academy of Engineering and other organizations as an advisor on data science topics.

Key Takeaways:

• Having a data scientist or a team of data scientists is critically important to get value from analytics.
• Data scientists need to understand the pains of business so that they can find the right projects.
• Data scientists shouldn't chase problems where data isn't available, and analytics isn't applicable.
• Set up conditions so that data scientists are required to embed in business teams.
• Training the organization to get ready for digital transformation is more complicated than math and statistics.
• Executives who speak the language of business and have enough data literacy are a good fit for data science training.
• Using a federated model helps reduce the data science attrition rate.
• Rotating data scientists helps apply tested analytics solutions to new business problems.
• If a company considers outsourcing data science talent, they would need at least one person who understands what data science can do for business.
• Listing the data science skills of everyone on the team helps the company see who is better equipped to solve a problem.

• Using certifications and training helps apply safeguards when giving access to a new tool.