The ability to work with data is quickly becoming an indispensable skill set for employees. However, defining “the ability to work with data” raises many questions. What does that mean to an individual or to an organization? “Analytics Teams”, “Business Intelligence”, and other groups have sprung up within companies to serve a variety of purposes, and most recently in the space of employee-facing functions like Human Resources, Talent Acquisition, Employee Engagement, and others. But without a clear understanding of what “analytics” is or how it adds value, to the rest of the workforce their presence is at best confusing and at worst scary. Furthermore, executive leadership and popular media now spend a great deal of time asserting all the great things that data has done (and will do) to further this revolution of industry.
Frankly, we haven’t done a great service to those who feel they are not prepared to come along for the ride.
This people-data revolution needs to be humanized. Data skills, at some level, are accessible to everyone. Just like computer literacy arrived at the end of the 20th century and became a non-negotiable skillset, basic levels of data literacy can be learned for workers of the future. And in the same way basic computer literacy doesn’t make you a programmer or a database administrator, data literacy doesn’t make you a data scientist or statistician. The fundamentals of working with data are easy to understand, but more importantly differentiating between these fundamentals and advanced levels of working with data will make a big difference in how people see this revolution and where they can fit into the future of work..
In this session, I will show that data literacy is not only achievable, but that its fundamentals can augment anyone’s existing skill set. I will introduce a basic framework for thinking about your data skills and use the same framework to introduce the new people-data ecosystems growing in organizations that we must increasingly learn to navigate. This talk breaks down to answering the fundamental question, “What do I need to use data well?”