Sentiment analysis attempts to gauge the mood of a group based on their online conversations with each other. This is a good way to learn valuable information about your data scientists' morale.
How is your team's morale? You might turn to your change professionals for this information; however, even change experts tend to rely more on impressions and intuition than data and analyses. What a missed opportunity if you have data scientists on your team!
Leaders who employ data scientists to formulate and implement their corporate strategy are at a distinct advantage: They can turn data science back on their own leadership and management capability, and team sentiment is a perfect example. When executing a strategy that involves data science, it's useful to monitor and analyze your data science team's sentiment.
My sentiments exactly
Sentiment analysis is a useful and popular application of data science, so there's no reason why you cannot use it to monitor your team's morale as they're moving through your strategy's implementation.
In brief, sentiment analysis attempts to gauge the mood of a group based on the digital conversations they're having with each other. Unstructured text is collected from social media outlets such as Twitter and Facebook, and then data science is applied to understand if the group is happy, angry, or indifferent about an idea or product. This is valuable information for a leader who is trying to implement a corporate strategy that involves data science.
I emphasize implementation over formulation, because there's relatively little risk of low morale during the formulation stage. The very opposite is true of strategy implementation. I've been through dozens of critical efforts with large and small teams, and there's one glaring consistency: at some point, the environment gets tense and emotions flare. As a leader, it's important to monitor this for the health and welfare of your data science team and your corporate strategy. Low morale masks talent, which has a devastating effect on your productivity.
I'm good; how are you?
Monitoring your team's sentiment is not difficult when you have a team of data scientists at your disposal, though it might seem a little weird for people to architect self-analysis. Nonetheless, be very open and honest about what you intend to do and why you intend to do it. Do not attempt to do this in secret with another data science group — it will seem like spying, and you will violate the trust of your team if they find out. Furthermore, you don't have anything to hide. The purpose of setting this up is to make sure that dips in morale are caught early and effective interventions take place in a timely manner, so explain that to your team.
Your team will have the best ideas for implementation, but in general you must have a Twitter-like platform where your data science team can freely exchange ideas. To foster open and honest communication, treat this information like personally identifiable information (PII). These conversations are extremely sensitive and absolutely nobody outside of the team should have access to these data streams. Also, make sure the team understands that there will be no consequences for what they discuss — unless there's a clear violation of the law or an unequivocal threat to someone's personal safety.
Your data science team will know what to do next. Whether you're using big data analytics as a core strategy, a supporting strategy, or a means to improve organizational capability, your data scientists should be familiar with the tools and analyses that enable effective sentiment analysis. Work with them to put this analytic infrastructure in place, and you're ready to go.
Again, it will be awkward for your data scientists at first, but with the right assurances, they will quickly learn to appreciate how much you care about their well-being.
Sentiment analysis is a responsible and effective way to keep your data science team in good spirits while they move through the implementation of your corporate strategy. Not many leaders have the luxury of having this type of expertise in-house, so don't squander this opportunity.
Take some time today to talk with your data scientists about setting up an infrastructure that supports sentiment analysis. I'm sure they'll be happy about that idea.