MBA@UNC San Francisco Immersion: Data Analytics
Students at the 2015 MBA@UNC San Francisco immersion had the option to participate in a data analytics track to explore how Bay Area businesses, large and small, are integrating data analytics with their everyday operations.
Ruben Sigala, Chief Analytics Officer for Caesar's Entertainment
Here are some of the highlights from the data analytics track:
The New Face of the CMO: Maximizing Marketing Impact Through Data
Sandy Ono, Deloitte
Sandy Ono of Deloitte took the stage to talk about maximizing marketing impact through data. According to Ono, the explosion of data and digital channels is shifting the way we must engage customers and changing the future of marketing at the same time. Big data and analytic skills are in demand because personalization, the hallmark of today’s marketing world, is grounded in analytics. At the same time, the role of the chief marketing officer (CMO) has expanded to encompass the roles of both executive and marketer. Today’s CMOs must wear a number of hats, including customer champion, capability builder, innovation catalyst, brand keeper and growth steward.
Analytics informs customer conversation via recognition, reach and relevance. Ultimately, however, a marketer must drive conversions that matter. To that end, there's a question brewing in marketing circles: How do we answer big business questions while not getting bogged down by imperfect data? Ono cautioned that marketers must not discount data visualization; how the data is presented in order to get people on board is very important.
After her talk, Ono walked students through a case study of Disney Studio Analytics, which wants to bring science to decision-making and evangelizes the power of analytics. With the information given, students split into small groups to discuss priorities and opportunities for Disney Studio Analytics; specifically, which data capabilities should be their main focus and where in the business model might analytics inform studio decisions. Ono reminded students that analytics initiatives should follow a crawl/walk/run model, emphasizing that organizations don't need to be running (nor do they have the time and money to do so) in all areas of the business. They must prioritize what’s important and focus efforts there; the rest will follow.
Sandy Ono's presentation provided some fascinating insight into the role of the CMO in an organization; how it has evolved into various facets and how they utilize data to do their job effectively. The recurring theme we heard in Sandy's (and others’) presentations was: Good data has to have volume, velocity and variety. With it, a good analyst is able to deconstruct and mine through the various channels to understand the perspective of the customer, and then with this knowledge we can start to make decisions about how to shift the perception in a positive way. She recognized that once we have the story we need from the data, the most challenging piece is the execution at the operational layer. She explained that data analysts need to be able to present the data in an influential way to close the gap and get everyone on the same page. We also enjoyed a case study on one of my favorite companies, The Walt Disney Company. We got a peek at the types of things their analytics teams are focusing on and some were very surprising. It was exciting to try and think about priorities from the perspective of such a vast entertainment company with a huge variety of customers.
—Jennie McGuire, October 2014 Cohort
Analytics in Action at Google
David Hixson and Abby Bouchon, Google
David Hixson and Abby Bouchon each talked about analytics in action at Google. Hixson’s talk, “Data and Decision-Making,” focused on how Google uses data on the ground. Hixson said Google usually collects data because they have a problem to solve (rather than relying on "found" data). Data decision-making at Google, as at many companies, is a mixed bag; product, finance and “decision support” teams must all weigh in for each decision.
Hixson is a fan of exploratory analysis (by looking for correlations) but warned it can “lead to some beautiful lies.” There’s a risk to analysis in the abstract, he said. If decision makers don’t understand what’s going on, it’s easy for the data to lie. In a similar vein, he cautioned that it’s important not to factor in gaps in data into final predictions—incomplete data doesn't mean a product was broken, just that there’s missing information.
Abby Bouchon, K–12 Education Outreach Coordinator at Google, was up next and spoke about Google’s use of data analytics to solve the problem of gender equity in technology. Currently, less than 1 percent of high school girls plan to major in computer science. Google commissioned a study to prioritize the factors that lead to a girl’s choice to study computer science and found that 61 percent of the girls make the decision pre-college. Things like social encouragement, self-perception, academic exposure and career perceptions take top billing when it comes to leading girls into (or out of) computer science. In addition, Google found that 95.1 percent of factors that influence a girl's decision to go into computer science are controllable. With these stats in mind, Google is aiming to change the outcome—getting more girls interested in science, technology, engineering and mathematics (STEM) fields and working to correct gender equity in tech.
What I Wish I Knew When I Started in Data Analytics
Mireille Mallouh offered students wisdom and advice gleaned from her early career. Her key takeaway: Learn all aspects of the business. Data analytics sits between the development team and the business owners at the junction of user experience, business and tech. Because of this, it’s important to understand the wide range of business factors that play into analytics decisions. By knowing and monitoring key performance indicator (KPI) numbers, analysts can present a unified front to the rest of their organization.
Mallouh describes her analytics process as a “data cake,” where layers of data provide information, which leads to presentation and, ultimately, knowledge. Following the data cake metaphor, she said, data doesn’t come to analytics teams prepared for consumption; instead, analysts must be ready to shape it into a presentable format. Mallouh cautioned that oftentimes data is unstructured, undefined, inconsistent and sometimes dirty. “Accept, embrace and maximize it the way it is,” she said. Also, analysts must expect to use multiple tools to find the best fit for each project, so it’s important to understand as many tools as possible. After all, once data is shared, people want more data presented differently. Mallouh said nothing makes analysts happier than seeing data used for business decisions. Data leads to insight, which leads to action. An analyst’s job is to enable the business without getting bogged down in reporting.
MBA@UNC Students Give Back
Over the course of the immersion weekend, the innovation and data analytics tracks competed to see which could raise more money for Delancey Street, the country's leading residential self-help organization for substance abusers, ex-convicts, homeless and others who have fallen on hard times. Students raised more than $10,000 for the foundation, and the funds were presented to the organization at a dinner held on the Delancey Street property. Through this initiative, MBA@UNC students upheld the UNC Kenan-Flagler core values of excellence, leadership, integrity, community and teamwork.