What Your Employees Must Learn to Work With Data in the 21st Century

WebinarNovember 2021

The data revolution is well underway. Regardless of the industry or department you manage, working with data will soon be an essential part of all of our jobs, if it isn’t already. This could take the form of basic data analytics, data science, machine learning, or artificial intelligence. And it can be overwhelming: what do all these terms mean and how can they be leveraged to impact your employees’ work, whether you're in finance, healthcare, tech, the public sector, or another industry entirely? This webinar gives you a primer for understanding how data can impact your employees’ work, what they need to know, and how to go about educating them.

You can find the slides here.

Summary

In an era where decisions based on data are transforming industries, understanding data literacy and fluency is becoming a must for employees across all sectors. Dr. Hugo Baum-Anderson, a data scientist and educator at DataCamp, highlights the need for spreading these skills throughout organizations. He introduces key concepts in data science, such as the data science hierarchy of needs and the Gartner Hype Cycle, to explain the current state and future potential of data science and AI. By using tools like Excel, SQL, Python, and R, employees can convert business questions into data science inquiries and derive actionable business insights. Hugo emphasizes that these skills not only foster individual growth but also position organizations to thrive in the data revolution. With examples from finance, healthcare, and agriculture, the webinar displays the broad reach of data science and the important role of data literacy in leveraging its potential.

Key Takeaways:

Data literacy is vital for every employee to participate in the data-driven conversation.
Machine learning and AI are impactful but require basic data capabilities first.
Understanding the data science hierarchy of needs is important for successful AI implementation.
Organizations should focus on developing a culture of data fluency across all departments.
Gaining basic statistics knowledge may be more universally important than coding skills.

Deep Dives

The Data Science Hierarchy of Needs

The data science hierarchy of needs, developed by Monica Rigardi, outlines the must-follow steps organizations need to take to achieve AI ...
Ler Mais

transformation. This hierarchy starts with data collection, followed by data storage and movement, exploration, and transformation. Only after these basic steps can organizations proceed to optimize with basic analytics and visualization. Once these steps are established, machine learning can be implemented, leading eventually to advanced deep learning and AI applications. Dr. Hugo Baum-Anderson highlights the importance of this step-by-step approach, stating, "Before AI, AI is the top of this hierarchy of needs, and we need to build all these capabilities out before we even get to AI."

The Gartner Hype Cycle and Data Science

The Gartner Hype Cycle is a model that tracks technological progress against societal expectations. Dr. Hugo Baum-Anderson uses this model to evaluate the current state of data science, suggesting that data science is approaching the peak of inflated expectations. This phase is followed by the trough of disillusionment and the slope of enlightenment, where technologies become more realistically integrated into everyday operations. Hugo poses a thought-provoking question to his audience, "Where do you think data science is in the Gartner Hype Cycle currently?" The answer varies across industries, indicating the varied progress and adoption levels of data science practices.

Skills for the Data-Driven Workforce

Employees need to acquire both technical and non-technical skills to succeed in a data-driven environment. Essential skills include asking the right questions, transforming business queries into data science questions, and explaining complex results to non-technical stakeholders. Technically, proficiency in Excel, SQL, Python, or R is important. Dr. Hugo Baum-Anderson encourages learning coding as a starting point, especially for those in disciplines traditionally reliant on Excel. He also emphasizes the importance of statistical intuition, noting that "learning basic statistics is actually more important for everyone in businesses and in society" than coding itself.

Integrating Data Literacy Across Organizations

Data literacy should be present at every level of an organization, similar to how email became ubiquitous. Dr. Hugo Baum-Anderson points out that data literacy is not binary but a spectrum, and developing a culture of data fluency is critical for organizational health. He quotes Dave Robinson, DataCamp's chief data scientist, who says, "Data literacy isn't either you are or you aren't ready to work with data; it's really a spectrum." This approach not only aids individual professional development but also strengthens the organization's ability to leverage data for strategic advantage.