The Limitations of Big Data in Predictive Analytics

Jennifer Prendki | @WalmartLabs


The total amount of data available to human beings currently doubles every 18-24 months, giving data scientists an unprecedented opportunity to push further than ever the boundaries of human knowledge. This is an exciting time for data professionals. Many are hopeful that these huge loads of data will enable data-greedy algorithms like deep neural networks to unlock a myriad of new possibilities for humankind. But can big data really answer all our questions? No matter how useful and powerful, in the wrong hands, data can also easily lead to ill-informed decisions and wrong assumptions. In her talk, Jennifer will cover the reasons why better algorithms matter just as much as the amount of data available, and will describe the dangers and perils that the data scientist of the future will need to thwart using increasingly advanced mathematical knowledge, refined strategies and human rationality.

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Jennifer prendki

Principal Data Scientist | WalmartLabs

A former experimental particle physicist turned data scientist, Dr. Jennifer Prendki has worked on big data problems for more than 10 years, both as an academic and an industrial researcher. Her eclectic experience in various industries including finance, advertising and ecommerce allowed her to develop an extensive understanding of a vast range of machine learning techniques and a solid knowledge of statistics and computer science. She is currently leading the Metrics and Measurements group within the Search department of @WalmartLabs. Today, she is a strong advocate for the inclusion of scientists and engineers from all backgrounds in the data science field, and loves to promote the importance of the scientific method in predictive analytics.

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