Being “innovative” is risky business. And you don’t have to look past the meaning of the word to realize why. The word “innovative” is derived from the Latin word innovare which means "to renew or change". The human brain seems to be wired to have a involuntary response to change, which is along the lines of “WHAT THE...!, somethings not right here”. And it doesn’t seem to matter how much preparation has been performed in setting up for the change, the initial response is the same (“something is not right”). Following this initial response comes our instinctive nature to place it into either the “good” or “bad” category. When we are talking about changes involving digital data and software we also have a fair bit of cognitive bias to overcome, due to the years of bad memories of loss of valuable data, loss of countless hours converting formats and unmeasurable amounts of brain-strain due to things consistently changing. Therefore, it is very likely that a change (when experienced) gets immediately placed in the “bad” category by those experiencing it, and stays there until it proves itself otherwise. In addition, our tainted memories provide an untapped energy for our cat-like minds to be ready for the pounce once something goes wrong, and seems to exclaim “AH HUH!, I knew this was a bad idea!” every time a minor challenge emerges. And conversely when we see an improvement its more of a hesitant “hmm okay, this might be better... I’ll keep trying it and see”. So to be labeled as “innovative”, when dealing with digital data and software, it seems, that a fair bit of convincing needs to happen before the change is honored with the precursor of “a change for the better” status.
The Data Scientists’ 3 step process for combating this is:
1. Lead by example in embracing change. Next time you find yourself reacting to a change, observe your mind habitually labelling it as “good” or “bad” and then move to take a “neutral” perspective before making the final judgement. It still might end up in the “bad” category but giving it a neutral playing field allows for a more accurate evaluation. By embracing change you see it for what it really is.
2. Let the change be initiated by the users. It might feel counterintuitive but in some cases its best to let sleeping dogs lie. If no-one is complaining about any problems then don’t “wake up the dog” so to speak. This is not to say that you should hide anything from anyone, but make sure you’ve got user support before “looking the dog in the eyes”.
3. Present the value of the change wherever and whenever possible. Getting reminded of why a change was initiated is always a good thing, especially if unforeseen challenges have emerged. It provides an opportunity to validate that the value the change brings is still worth pursuing.
Using this 3 step process you can reduce the risk of implementing a positive change only to have it seen by those that use it as a negative change. Technology is advancing at such a rate that it is out running the evolution of our hard wired circuitry of the human brain. The data scientist role is perfectly positioned to initiate positive change, however, you don’t need to be a data scientist before you start practicing The Way of a Data Scientist.
The Data Scientist Culture contains the following principles:
1. Embrace change.
2. Learn to love learning.
3. Create transparently.
4. Serve to inspire.
5. Teach for action and transformation.