Data analysis can be fun – and horrible all at the same time. So here's a perspective from a network researcher, sociologist and former business analyst on how to improve our daily approach to data. What traps can be avoided? How do we know when we're biased? Is there such a thing as "good"/"bad" data? Let's talk, discuss and maybe change our approach.

The talk will cover some foundations: what's a bias – and how do our biases get reflected in our data collection, analysis and interpretation? The way we tackle our own biases with regards – but not limited – to gender, race, social origin, abilities, nationality and other factors shapes not only the quality of data collected, but also directly the outcome of data analysis and interpretation!

I'll explain how these mechanisms work, give some examples and then jump straight into the part where we can all get better at dealing with this challenge.

As complex as the matter is, as long is the history of researchers trying to approach biases in data handling. Come and join me in this session as I present some ideas on what to do (and what not to do) with big and small data!