About the Guest Gail Buffington is the Head of Data Science and Analytics for Millipore Sigma, a global life sciences company. Previously, she worked in retail as vice president of marketing and analytics for a women’s apparel company. She has over 15 years of experience across a wide breadth of data careers. Outside of her current role, she also serves as a training officer with the United States Army and is a Girl Scout troop leader. Connect with Gail Buffington Key Takeaways An ability to sell or promote is not limited to products; it can be applied to skills, teams, and contributions. Analytics teams should focus on quantifying their contributions to showcase their impact on the company’s success. Not all anomalies in data are harmful; some can offer positive insights and opportunities. Relying solely on internal data may limit the understanding of more significant trends and influencing factors. External economic data can provide context and predictive insights. The value of forecasting lies not just in the data itself but in understanding the influences behind that data. Challenging preconceived notions and expanding data sources can lead to more accurate and insightful predictions. Solely focusing on primary metrics like revenue might limit the understanding of a situation. Approach public data broadly and avoid restricting yourself to apparent connections. Realise that correlations may only sometimes be apparent, requiring more profound analysis and consideration. Utilise the extensive directories and resources available on government websites to access many data sets. Embrace an exploratory attitude in analytics, allowing for brainstorming and discovery. Quote “Being able to see that full picture allows us to get to a spot where we can utilise data that otherwise is very interesting, but doesn’t give us the tools we need to unlock any insights with it.” – Gail Buffington Highlights from the Episode Please give our listeners a sneak peek into your background and current area of focus at MilliporeSigma. Gail started her career in retail, specifically focusing on marketing and analytics. With her strong background in statistics from her educational journey, she incorporated a data-driven approach into marketing. Later on, she transitioned to MilliporeSigma, where she currently leads the data science and analytics team. The team specialises in various fields, including website analytics, business analytics, data science, data engineering, and machine learning operations. Their diverse skill set allows them not just to conduct analyses but also to initiate self-driven and innovative projects. Given your background in marketing and analytics, how has it aided you in promoting the importance and ROI of analytics internally within the organisation? Gail’s background in marketing, which inherently involves selling, has equipped her to promote and validate her team’s capabilities effectively. She uses her marketing knowledge to highlight the value her analytics team brings to the organisation. Gail emphasises the importance of the analytics team constantly demonstrating their value and contribution to the company’s bottom line to secure consistent support from executive leadership. How do you use a mix of internal, economic, and economic data to make forecasts that not only tell you what’s predictable and expected but also how reliable these predictions are for our future planning? During the COVID-19 pandemic, there was a significant rise in data anomalies. It became clear that understanding these anomalies required a broader perspective, combining internal historical data with external economic indicators. By analysing both data sets, more accurate forecasts were achievable, helping identify influencing factors behind standard datasets. Why is it crucial to approach anomalies from various perspectives rather than focusing solely on a single angle? Addressing anomalies requires a broader perspective than just focusing on primary metrics. Multiple indicators can hint at upcoming monsters. This includes internal metrics like session traffic or lead quality and external indicators like industry trends or grant funding that can profoundly impact customer spending. Why is it essential to partner with third-party data providers, like Demandbase, to analyse anomalies and combat data bias? How do you utilise these providers to gain a comprehensive understanding of your addressable market? Gail discussed the paramount importance of third-party data providers, specifically mentioning Demandbase, in deciphering the vast amount of unknown data. By integrating this additional data, they can comprehensively understand their target audience and activities, especially when internal data is limited or may introduce biases. Can you explain how you utilise free government data sets at MilliporeSigma, the challenges of cleaning and standardising this data, and the significance of NIH data as a starting point in your process? Millipore Sigma leverages publicly available data to its advantage, focusing on a broad market due to its diverse nature from mergers and acquisitions. They use NIH data to identify grant funding opportunities, but this data needs company-specific details. By partnering with Demandbase, they augment the NIH data with firmographic information. This combined data, integrated with generative AI insights, predicts customer purchases. Such insights not only guide marketing strategies but also reveal potential market penetration and areas of opportunity. Can you share some examples of creatively using publicly available data to inform businesses? Gail shared her team’s success in leveraging public data, emphasising the necessity to approach such data with an open mind. She noted that while some data sets have a direct association that might be immediately evident, others require a broader perspective to uncover potential correlations. Gail elaborated on the surprising relationships one might find, such as the correlation between airline travel and retail store sales during specific events like COVID-19. She advocates for a spirit of exploration and an analytical mindset to navigate the vast amount of available data. Good reads: Is there a book, blog, newsletter, website, or video that you would recommend to our listeners? Towards Data Science Stack Overflow
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