Vulcan - Leading with Analytics: Program Resources

Unit 1: Why Analytics Matter Faculty: Dr. Adam Mersereau and Dr. Brad Staats

Introduction: Brief history of analytics from 18,000 BCE. • Key Fact: 90% of the world’s data has been created within the past two years. • Data Analytics Today: Infographic on page 3 • Case Study: Moneyball by Michael Lewis exemplifies analytics; 2002 Oakland Athletics. Baseball is a great setting to gain lots of data with clear outcomes. Definitions of Analytics: • “The scientific process of transforming data into insight for better decisions.” – INFORMS • “The discovery and communication of meaningful patterns in data.” – WIKIPEDIA • “The broad use of data and qualitative analysis for decision-making within organizations.” – TOM DAVENPORT Analytics involves the idea of a transformation. We want to tie analytics to decision-making. Analytics can turn data into value / action. “Business Intelligence” can include analytics, querying, dashboarding, and reporting. • Predictive analytics help us to try and predict the future. • Prescriptive analytics are used to improve decision-making. The analytical approach is not always the best fit nor should be utilized in all situations – for example, ordering at a restaurant. Analytics can enable transformation, scalability, detailed understanding, and survivability. • Case study: U.S. Credit Card Business circa 1980’s infographic on page 4 o Data analytics changed the credit card industry with automation. Credit cards are not necessarily banking, but ideal for information. • Case Study: The Value of Detailed Customer Data infographic on page 5 o Signet / Capitol One transforms itself and the industry. • Challenges with data: Asking the wrong question, getting the wrong data, failing to understand the data, conducting a poor analysis of data, and making incorrect decisions. Key Takeaways: • Analytics really do matter. Get quality data, visualize the data, analyze data, and make decisions. • After the Moneyball story was released, several baseball teams copied the Moneyball strategy / approach and also had great success. • The goal is to combine analytics with other strategies. Don’t place too much emphasis on data. • Even successful teams have to reassess their strategy to find the right balance between analytics and intuition. • Understanding where analytics has the most impact and where it has very little impact. Types of Analytics: • Descriptive analytics allow us to understand what has happened in the past.

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