In an earlier post, we discussed the need for teams to build data-related skills and how that can help improve team and business performance. The first step in this direction is to record, manage, and clean the data, and ensure that all members in your team have equal access to it. Once that’s done, here are the next steps to take:
- The second step: understand the difference between correlation and causation
Distinguishing between correlation and causation might sound like something out of a college statistics class. But it’s worth repeating this lesson, purely because we can easily fall into the trap of thinking that Event A is caused by Event B merely because Event A is correlated with Event B. We forget the possibility that Event B is caused by Event A, Event A and B cause each other, both Event A and B are caused by Event C, or that any correlation between Event A and B is purely coincidental.
In the real world of work, one could consider the correlation between having team meetings and increased productivity. If there is a high correlation between these two events in your team, there could be a natural assumption that meetings increase productivity. However, a data-science-informed take on this would lead everyone to understand that there are multiple possibilities — people are organizing meetings on days when they feel highly productive, both the meetings and productive output happen on days when the team comes in early, or perhaps the findings are purely coincidental and having a meeting every day will not increase productivity. Knowing this can prevent teams from making erroneous decisions that can affect productivity.
- The third step: apply root cause analysis
After grasping the concept of correlation and causation, your team can find out the exact cause behind an incident using root cause analysis. Fishtail diagrams and the ‘five whys’ are two very robust methods recommended by data science experts like Thomas C. Redman to identify the core issue methodically instead of relying on intuition.
The key here is to approach every problem from a learning and growth mindset, rather than as an opportunity to assign blame. Otherwise, the team will not take part in this activity wholeheartedly and honestly. Make sure they understand that engaging in this activity will eventually help them proactively identify and resolve business issues at the individual and team level.
Once the team has the basics of data collection and analysis in place, find a simple data project for them to work on. Many teams find it difficult to understand the relevance of some of the bigger issues that are spoken about at the managerial or C-suite level, and it might be good to start with a project that they understand and resonate with. For instance, consider an exercise to see whether meetings start on time (as shared here) and apply the skills learned to collect quality data, understand correlated events, and deep dive into causation. The same principles can be used to study daily working hours, trainings conducted, interruptions, and much more. Starting at the level of personal and team development can help your team familiarize themselves with data and solve pressing issues that matter to them and are also broadly within their control.
It’s also important to invest in the right data tools and training to get your team data savvy. So do take the time to browse around for some free tools and simple data-science courses to help your team grow stronger in this function, incrementally, day after day.