Core Insights - Data fluency is increasingly recognized as an essential leadership skill that enables individuals to translate data into actionable business strategies [3][4] - There is a growing demand for employees with data skills, with 57% of managers planning to hire more data-skilled personnel in the next five years [5] - Despite the increasing reliance on data, many employees feel anxious about working with it, leading to errors and decreased productivity [6] Group 1: Importance of Data Fluency - Data fluency allows individuals to frame the right questions, pull pertinent data, and implement changes that lead to positive business outcomes [2] - The ability to connect quantitative evidence with strategic leadership is crucial for effective management [3] - Translating evidence into action differentiates those who merely manage information from those who drive meaningful outcomes [4] Group 2: Current State of Data in the Workplace - A Gallup study indicates that 57% of managers expect to increase hiring of data-skilled employees due to rising data dependence [5] - The Canva survey reveals that while 90% of employees work with data weekly, 75% notice an increase in data reliance [5] - Two-thirds of employees express anxiety about data work, with 30% actively avoiding it, resulting in more errors and lower productivity [6] Group 3: Industry Applications of Data Fluency - Businesses across various industries are leveraging data to enhance operations and drive success [7] - In the insurance sector, data fluency is about connecting insights to protect livelihoods rather than complex analytics [7]

I have a lot of memories and uh I'm going to try to create some impact through this talk because I live eat and breathe data and AI and I want to make sure that if you guys who knows about data knows about numbers and doesn't even know about numbers can you do data AI and data science or not that's what I'm going to talk about today so who is she any ideas. Uh, so she's Craigslistwood, someone who studied operations research at Stanford, didn't have any idea about data, AI, numbers, and ended up being VP of ...