Creating Impact
By: Mackenzie Mitchell
Tags: Success, Startup, Impact, Business, Data Science

Confidence is Key

Before a Data Scientist can be convincing to others, they must believe in themselves and their own work. When convincing others of your findings, it becomes so much easier when your confidence in your work radiates through the conversations that you have. When speaking about your work with confidence, your team will begin to feel like they would be making a mistake by not taking your advice into consideration. Confidence is contagious and will instill a sense of trust in you and your work from the rest of your team.

In order for a scientist to be convincing, they must believe in themselves. Having confidence, showing it, and radiating it could be the difference between your team not listening to your suggestions or implementing your insights into everything they do. If a Data Scientist is not confident in their work, how do they expect anyone else to believe that their suggestions are valuable or credible? However, with the right amount of confidence (and, of course, grace) a Data Scientist can move into a position where their team is wary of not considering their suggestions.

Drop the Technical Jargon

In order to be a good and effective Data Scientist, who creates an impact on their company, one must have excellent soft skills. The scientist must be able to talk to the rest of the team and be convincing while not belittling. They must explain their findings in lame terms in order to help business minds to understand why the scientist has made this suggestion. While it may be apparent to the Data Scientist that their model was a good fit based off of the R-squared value or the root mean squared error, those numbers alone mean nothing to the non-technical mind.

Rather than throwing a plethora of technical statistical and analytical terms at the rest of your team, try to use simplified definitions of these terms. For example, instead of saying “The R-squared value for this model was 73%,” a Data Scientist could say “73% of the variation (or fluctuation) in our sales is explained by this model.” By substituting the term with its short and simplified definition, you have put this technical concept into lame terms that are easily understood by everyone on your team.

Short and Sweet

Your team does not need to be bored with the whole process of how you got your results. While you may have cleaned data, joined tables, tried different models, engineered features, and remade your visualizations ten times, be careful to not merely summarize your process when presenting your results. Your team is not concerned with your process, they are concerned only with the relevant information that you found through that process.

At times it may be helpful to explain why chose to take a certain step in your process. For example, a Data Scientist may have chosen to drop a predictor from their model that may have seemed relevant prior. In this situation, it is important that the rest of the team has knowledge on why this variable is no longer important. Perhaps this variable, X, experienced multicollinearity with more than one other variable in the model. In this case, it could be that X is derived from the other variables we already have in the model, so these other variables alone give us a better model than the variable that was obtained using calculations and other variables.

Explaining important steps is critical, as it will give the rest of the team a reason as to why they should not focus on a certain aspect of the project any longer, even though it may have seemed important before. Some good phrases to throw in when doing so are things like: “I’m just as surprised as you but…” or “I know we initially thought that X was very important in our sales, however, we are able to get more accurate values using the other variables because we cannot see the true impact of X as it is dependent on our other variables.” Doing so will give your team the sense that you all were on the same page before, but new information shows that it may be time to turn the page, and you and your team will be able to turn the page together.

Utilize Analogies

Analogies are an extremely useful way to get a complex point across to others. By explaining your findings in terms of what your team is familiar with, you are better able to get into their head to change their opinions. It is very powerful when a Data Scientist is able to relate their findings to situations that virtually everyone would be familiar with, regardless of background, because more people will then better understand your findings and there will be a better chance of them implementing these findings.

Know Your Team

Knowing your team’s interests allows you to cater your analogies to them in order to help them better understand. For example, it might be more convincing to explain your findings in terms of a sports analogy to one member of your team, while another member may better understand with an analogy relating to gardening. Being aware of your teammate’s likes and dislikes, as well as their working styles can help you effectively create an impact on a company using your Data Science findings along with your communication skills.

Study Other Influential People That Inspire You

I believe that much can be learned from studying some of the most successful people’s tactics that they employ in order to become impactful. I find listening to Elon Musk’s stories to be very inspiring, and I try to study how he speaks to people in various videos to get a better understanding of how he gained the influence, confidence, and trust from others that he did to create a space company having had degrees in business and physics. I find Musk particularly interesting because he does not possess the typical way of speaking that one would expect a powerful and influential person to. Musk stutters and trips over his words, but is still able to convey that he knows what he is talking about and that his findings and ideas are important.

Elon Musk: How To Achieve 10x More Than Your Peers (1:00)

I feel that studying political candidates and how they generate votes and supporters can also be extremely useful in becoming convincing and creating impact. Other people whom I find inspiring to watch and study how they influence people are Steve Jobs, Daymond John, Jay-Z, Will Mitchell (my brother), and Scott Mitchell (my father).

I find my brother, Will Mitchell, to be one of the most inspiring people to study. Having dropped out of high school, he was able to impact the people around him to believe in his ideas. He had built businesses in the past alone but at age 22 he convinced his long-time friend Kyle that his idea of coaching entrepreneurs to achieve their dreams was worth-while. Ever since his successful online-coaching business has been doing great on the underlying principle that my brother is influencing people with dreams to do as he did and take the leap. The fact that someone with little-to-none “official” educational credentials is able to inspire, coach, and influence thousands of people to work towards their dreams is incredible to me. It goes to show that impact can be made and people can be convinced to implement your ideas where ever you are confident in your process and able to engage your audience.

Become a Great Story-Teller

Everybody loves stories. They’re engaging, easy to understand, and easy to follow. Data Science requires story-telling in order to compel people to use your findings in an effective way. Simply presenting one’s findings can become repetitive and boring, however, creating a story out of your findings shows everyone the bigger picture.

In order to become a better story-teller, it is often recommended to read as many books as possible and to watch movies. Reading fiction and non-fiction alike can help you understand how to construct stories and how to incorporate your findings into those stories, respectively.

Books I recommend:

How To Win Friends and Influence People by Dale Carnegie

Becoming a Key Person of Influence by Daniel Priestley

Song of Solomon by Toni Morrison

The Stranger by Albert Camus

Crucial Conversations by Patterson, Grenny, McMillan, and Switzler

Influence: The Psychology of Persuasion by Robert B. Cialdini, P.H.D.

1984 by George Orwell

In conclusion, much of the job of a Data Scientist is to influence others and to be convincing with their findings in order to actually create impact from their insights. While I have outlined many ways to become more convincing and reliable to others, these are not the only ways.

Let’s connect:

https://www.linkedin.com/in/mackenzie-mitchell-635378101/

https://github.com/mackenziemitchell6

Resources: