How do you build solid analytics learning culture at Fortune 100 companies?
Xingchu: Analytics learning culture starts with analytics, and building a data analytics-first culture is critical. This approach means that for every business decision, we start from data and insights, which enables a fact-based conversation. This mindset goes from daily work with each team member to senior business executives. We invest in data analytics talent, quality, accuracy, and scalability to make data and insights effective. That sets the foundation for performance reporting and insights and empowers advanced analytics and AI applications.
A learning culture is also critical, and it takes intentional effort. Analytics and AI have evolved so quickly in recent years that they bring a new lens to everything we do. That enables everyone with new approaches to think and act differently. It is essential to encourage innovation so that it is okay to challenge the status quo and question how it used to work in the past may not work in the future. For example, a traditional creative test takes the two-step approach in which the participants watch the creatives first and then answer a designed questionnaire to provide inputs to measure the effectiveness of the creatives. Today, there is AI technology that, through smart cameras, will analyze participants’ eye movements and determine where on each frame it gets attention and even the emotional reaction. That revolutionizes the time, cost, and accuracy of creative tests. The nature of innovation is experimentation, so sometimes the new approach works, and other times it doesn’t. When it fails, let it fail quickly so we learn from it and continue to refine it. Learning and innovation also require time and space. When a team does planning, we also recognize it and plan the timeline accordingly. (Please refer to slides 8 & 9 of Xingchu’s PMSA Keynote address.)
Ultimately, we need to connect our work and learning to the results. As we continue to learn and develop innovative analytics solutions that empower better business, customer, doctor, and patient decisions, this philosophy creates a positive closed loop and drives the analytics learning culture forward.
How do you help employees stay current on the latest technologies and understand how to incorporate them into their day-to-day work, particularly in the data and analytics domain?
Xingchu: With the analytics learning culture, we continuously evaluate the latest analytics and AI technology, making it seamless to bring into the work environment. At the same time, our partnership with the legal team helps ensure we follow compliance requirements. We also recognize that adopting any technology in a large enterprise environment requires more than just the technology. Well-planned training and onboarding, as well as continuous sharing of successful case studies among peers, are all critical to successful adoption. We consider people-process-technology as a three-pillar framework.
Also, depending on the business needs and nature of the technology, we may choose to do a full rollout or a smaller-scale pilot to test. For global companies, we also consider each market’s maturity and the tradeoff of cost and benefit. One category of analytics technology, for example, office assistance GenAI solutions such as co-pilot, makes day-to-day office work more effective: a no-brainer for a full rollout. Another category of applications, for example, to leverage GenAI technology to facilitate marketing content creation, needs a hybrid approach of both technology and marketing experts to make it truly work with accuracy and productivity. It will take a more comprehensive training and adoption program to realize its full power. A third category is more advanced analytics and AI technology to drive critical business decisions, which will take dedicated analytics specialists, data scientists, and machine learning experts to train the AI model and fine-tune it to generate the key insights at the level of timeliness and accuracy that meets the business need.
The booming of analytics and AI technology also requires critical assessment, so we do not spend all the time chasing shiny objects. We leverage technology so it fits for purpose. Not every AI technology fits what we need. The ability to conduct practical assessments is critical, and a deeper understanding of the technology/solution is required. That is why I always encourage my team to pay attention to the fundamentals of analytics and AI. The application of technology changes very fast, but fundamentals are a lot more robust and stable. That will give the team a deeper understanding of newer technology and be comfortable looking under the hood. It is not a simple plug-and-play. We take the adopt & improve approach; the prerequisite is understanding how it works.
Please read Xingchu’s interview in DataIQ.
Finally, you worked in both the CPG/Retail and Biopharma industries. Can you highlight the biopharma industry’s uniqueness in terms of data and analytics?
Xingchu: The biopharma industry has a much deeper understanding of its products and customers (doctors and patients) because this directly impacts people’s lives. There is very rich market research conducted on each product and patient journey, starting from awareness to being diagnosed and to being treated. Also, the breadth and depth of data and analytics could vary significantly. For example, the treatment experience for vaccines is relatively straightforward, but the patient population is massive; for some cancer drugs, the treatment steps are much more sophisticated and the timeline much longer, whereas the patient population is much smaller. In the CPG/Retail industry, due to the sheer volume of products (millions), data and analytics focus more on the scale and speed of insights and less on the depth of each product.
A key challenge for the biopharma industry (data and analytics-wise) is that it is further away from customers due to its nature as a manufacturer on the upstream side of the supply chain. There are distributors, retailers, doctors, and insurance companies. While retail directly faces end consumers, it is relatively more straightforward to understand who the customers are, what is purchased, when and where, and how (online, in-store, etc.). From a data and analytics perspective, the richness of data in retail enables key analytics insights and AI applications to empower personalized experiences, such as personalized recommendations for what to buy or homepage content for specific customers. The automation of AI applications is more prevalent. In biopharma, adopting advanced analytics and AI is not as fast due to data limitations, including timeliness, granularity, and accuracy.
There is much more we could do in biopharma regarding analytics learning culture, analytics/AI technology adoption, and investment in data maturity and curation. However, recognizing the challenge is also very important. There is no one-size-fits-all solution. We must tailor our approach to the most effective way to build and drive enterprise analytics excellence. It’s again about people, processes, and technology. I’ve already seen the early success of this approach and the tremendous impact driven by data and analytics. It will take more time to continue learning, practicing, and refining.
Xingchu Liu is the former Chief Commercial Analytics & AI Officer at Pfizer Inc. He previously served as SVP of Enterprise Data Analytics & Technology at Macy’s. Before joining Macy’s in 2021, Xingchu spent six years as President of BlackLocus, a disruptive AI innovation lab at Home Depot focused on merchandising and transforming the company into a data and AI-driven organization. Earlier in his career, Xingchu held leadership roles with TrueCar and worked as a Pricing Scientist at Zilliant. He holds a PhD in Industrial Engineering from Texas A&M University and a bachelor’s degree in industrial Automation from Tsinghua University.
Partha Anbil is a Contributing Writer for the MIT Sloan Career Development Office and an alum of MIT Sloan. Besides being VP of Programs of the MIT Club of Delaware Valley, Partha is a long-time life sciences consulting industry veteran, currently with an NYSE-listed WNS, a digital-led business transformation company, as Senior Vice President and Practice Leader for their Life Sciences practice.
Michael Wong is a Contributing Writer for the MIT Sloan Career Development Office and an Emeritus Co-President and board member of the Harvard Business School Healthcare Alumni Association. Michael is a Part-time Lecturer for the Wharton Communication Program at the University of Pennsylvania and his ideas have been shared in the MIT Sloan Management Review and Harvard Business Review.
