Data projects cause transformative change in organizations: they lead you to start doing things differently than before based on the insights you uncover using your data. Framing the results in the right way, rewarding vulnerability and a mutual learning approach help you succeeding in your data projects. Communication and transparancy are key.
Never underestimate what a few different words can do to a person’s productivity. To illustrate the effectiveness of framing, let’s take a look at a study conducted by Tanjim Houssain which shows that by simply altering the way in which something is said –and thus received– you can quite drastically improve productivity in the workplace. The study found insightful data concerning our productivity after being rewarded in certain ways. Generally it was found that ‘negative’ framing creates higher levels of productivity than ‘positive’ framing. In other words “not meeting your quota” mean losing your reward. While with positive framing “meeting your quota” means gaining your reward.
Giving someone trust is like giving them tools to hurt you. Giving someone your trust implies the expectation they will not hurt you, yet the person in question retains the ability to do so. Hence, trusting is not an easy thing to do, it places us in a role of vulnerability. Timothy R. Clark puts it really well in his article “Agile doesn’t work without psychological safety”. In this article he places emphasis on the psychological safety necessary to pave the road to success. He used the specific words “rewarded vulnerability” in this article, which really gets to the crux of the matter. Instead of using the tools to hurt the person that trusted you, security should be offered, and the feeling that they could trust you again needs to be imparted.
For starters it is quite obvious how to discourage vulnerability. Ridicule, intense critique or slipping into ad homonyms are a few examples. Rewarding it is also quite simple. To paraphrase another example from Timothy R Clark: Suppose you’re in a meeting and someone throws in an idea. The best response to that is probably something along the lines of “thank you for bringing that to my attention. How do you suppose we solve x?”. It is after all, not easy to formulate an idea and become vulnerable to scrutiny by throwing that idea into the group.
Not all ideas will be great though, so you might have to give critique from time to time. There are many possible methods for giving constructive critique but in the interest of time I will only be discussing mutual learning.
A positive aspect of the mutual learning approach, is the fact that it gives more people power and insight, allowing greater chances for everyone to learn. Through the method of mutual learning everyone knows the course in which the meeting is supposed to go and can help it stay on track. As a leader, saying that you may have made a mistake somewhere along the lines as well, you allow others to point out that mistake. If this wasn’t done, then the breathing room to point out mistakes simply wouldn’t exist for your employee.This last one is particularly important in the context of rewarding vulnerability, as it is a showcase to the employees that you are willing to be vulnerable as well. And in being the role model of the project you encourage others to follow suit.
Data projects are large in scope and impact. Usually when change happens within a company or project –even on the smallest level– it is likely to cause ripple effects. Data projects tend to create transformative change and you can get into pretty hot waters pretty quickly. In no small part it will surely contribute to the success of the project if everyone, much like with the “mutual learning approach”, is aware of the path you’re trying to pave. The future and change are inherently uncomfortable and insecure, and taking that leap of faith is rightfully scary. Offering comfort along the way is therefore important to the benefit of all parties involved.
All good arguments to stress that the keys to successful data projects are communication and transparency.
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