Brian Lamkin & Margaret Mason

To kick off the symposium, Luckie and Co. Senior VP of Business Intelligence and Integrated Solutions Brian Lamkin and Lead Analyst Margaret Mason presented on The Problem with Relevance. Using Brian’s wife as an example, they illustrated the challenges of companies using data to drive marketing. They showed that while 3/4 of customers find detailed profiles valuable if they allow more personalization of the buying experience, the customers still demand that companies “show me that you know me”–they want content and products that are relevant to their real interests and needs. Additionally, the presenters said that the marketing tech stack has grown 5000% in the last ten years, and 98% of companies claim to be “data-driven,” while a mere 40% have adopted best practices. Ideally, business data should tell you “where, when, or how you will win.”

A Few questions with Brian & Margaret

  1. What’s a typical day in your work life?
  2. You said in your presentation that the marketing tech stack has increased almost 5000% in ten years. How have you watched that evolve?
  3. What are some of the most interesting challenges in the field right now?
  4. What advice to you have for students who are early career in the field, or thinking of moving into the field?
  5. What’s your favorite part of your job?

CAmeron Jagoe

ProcureVue CEO & Founder Cameron Jagoe presented on A Certain Business Case for Analytics During Uncertain Times. He asked, “what would you do differently if you knew the future?” Would you invest in masks before Covid-19? Divest real estate before the 2008 real estate collapse? While it’s not possible to know the future per se, Jagoe argued that by following the OODA loop decision-making model (Observe, Orient, Decide, and Act) and iterating quickly, executives and business leaders could respond to changing conditions quickly enough that it would seem as if they were looking into the future. He drew upon examples as diverse as airborne dogfighting in the Korean conflict; employee empowerment in Toyota factories; and the unexpected resilience of Ukraine in the current Russia/Ukraine conflict.

A Few questions
with Cameron

  1. What does your company, ProcureVue, do?
  2. And the idea for the company came from a family-owned bakery?
  3. How are some ways that you’ve seen the OODA loop, and the philosophy of iterating quickly, applied in your own work?
  4. In your presentation, you talked about the interpersonal challenges of working with executives as a data professional. You even used the phrase, “humble yourself.”

Raja Chakarvorty

Data Science Profession: Required Skills and its Usage in the Insurance Sector was the title of Protective Life Corporation Chief Data Scientist Dr. Raja Chakarvorty’s presentation. After showing showing that data has been used in decision-making as early as 1858 with Florence Nightingale’s Rose Diagram, Chakarvorty offered an overview of the role of data science within the insurance industry. He then provided example skillsets expected of data scientists, such as ethics, business knowledge, data visualization and business concepts, as well as common coding languages data scientists should know (hint: if you are heading for a data science role, brush up on Python and SQL now). He reminded the audience of the importance of ROI vis a vis analytics: business only cares about whether the work is translating into dollars.

A Few questions
with Raja

  1. You took over at Protective at a fairly early stage of the data science program, and one of your roles has been to grow it. Can you talk about that?
  2. In your presentation, you mentioned ethics. What are some ethical challenges you’ve observed that data scientists need to contend with?
  3. Could you distill a few pieces of advice for a business student who is interested in data science as a career?

Bart Masters

Bart Masters, who is Director, Data Science at Greenlight Financial Technology, Inc., presented on Data Science at Greenlight: Growing with our Families. He reflected on his experiences as a leader of a small, fairly new team within a growing fintech company. Composed of mainly machine learning practitioners, Masters’ team is accountable to strict ethical standards, especially since their work affects families and children. For Masters, his team’s directives can be distilled to three items: “Move fast. Tell stories. Make impact.” Moving fast means showing application of data products and being able to pivot quickly; data storytelling focuses on context and action, not just data points; and making an impact involves creating measurable results that drive organizational KPIs.

A Few questions
with Bart

  1. What has been your experience going from larger teams to leading a smaller team?
  2. You said a couple of times in your presentation that “data is a story, not just a chart.” What do you mean by that? How do you put it into action?
  3. You also mentioned the idea of data scientist as a thought leader, not a “data appliance.” Can you unpack that?
  4. What advice would offer business students who are considering a career in this field?

Ryan Anderson & Rubina Ohanion

Presenting on Building the Intelligent Supply Chain, Accenture Managing Directors, Applied Intelligence Ryan Anderson and Dr. Rubina Ohanian highlighted the ways data analytics can impact supply chains, especially in the wake of Covid-19 disruptions. The presenters drew on “six signals” that are “essential to the future success of supply chain organizations”: proactively anticipating the future; decentralizing decision-making; building in sustainability; breaking physical limits of supply chain fulfillment; embracing virtual goods, environments, and prototypes; and becoming scientific (data-driven) companies. “There is nothing we do today from a methodology perspective that we didn’t know 30 years ago,” Ohanian argued. “What changed was speed of tech.” However, most companies still suffer from incomplete or inaccurate data.

A Few questions with Ryan & Rubina

  1. “Transformation” was in your presentation, your bios. What are some of the unique challenges of leading companies in data-driven transformations?
  2. You mentioned that lot of companies are gathering data that is either siloed, inaccurate, or both. What do we do about that?
  3. Can you further unpack the relationship between business analytics and social responsibility?
  4. What advice would you give to business students who want to enter the field?

Erik Johnson

Culverhouse Assistant Professor of Economics Dr. Erik Johnson, who is also CEO of several AI/ML-based startups, presented on Using Analytics to Detect Blight and Enforce Codes in Cities. He recounted how his interest in helping communities in tangible ways led to his work in AI and ML, and how his conversation with Tuscaloosa city officials revealed a community concern about urban blight. Using ML imaging technologies, Johnson and his team were able to develop a solution that attaches cameras to garbage trucks, which then take photos of residential properties throughout the city. The ML algorithm compares the photos to an international database of residential issues, from overgrown weeds to cracked paint, and weighs them against a community-specific urban blight scale. Communities are then empowered to respond before blight worsens in specific areas.

A Few questions
with Erik

  1. You’re an economist by training. How did you find your way to data analytics and machine learning?
  2. Associate Dean for Faculty and Research Jim Cochran recently said that he thought that Culverhouse is developing a reputation as an institution that addresses social problems with data. Do you agree?
  3. What do you see some interesting challenges and problems to solve in the field?
  4. What should students be doing to prepare for a career in this field?

Octavio Flores

Finally, Nestle’ USA Vice President, Data and Advanced Analytics Octavio Flores presented on Value-Driven Data and Advanced Analytics Strategy. After providing a brief history of the Nestle’ corporation, Flores turned his attention to the company’s social values, including sustainable nutrition, climate change, packaging and plastics, and improving livelihoods. Finally, he discussed analytics strategies to maximize data analytics value at scale. The four pieces of an advanced strategy included democratizing data; generating “always-on” insights; scaling AI across the entire enterprise; and scaling analytics innovation. Flores emphasized that data scientists must stay at the cutting edge. “If I’m not strategic on what’s next (next 24-36 months),” he said, “I will become irrelevant.”

Mr. Flores was not available to be interviewed.

stay up to date

Sign up for email updates on the 2022 symposium, and other analytics news within the Culverhouse College of Business.