Subscribe to DSC Newsletter

IMPACT - Part 3: Become a world-class communicator

This is part 3 of a 3 part series: “How to make your mark on the world as a talented, socially conscious data scientist.”

In the first post in this series, we explored the kinds of global, systemic problems that data scientists should look to solving, to make the most difference for the greatest amount of people.

In the second post, we examined the importance of asking the right questions, thinking big picture, and understanding how to use your tools as a data scientist as a means to an end - rather than the other way around.

In this post, we finish the series with a discussion around why the best data scientists are world class communicators.

***

“Seek first to understand - then to be understood” - Stephen Covey

As a data scientist, you may often feel you live in an entirely different world than your stakeholders. Your experiences, skill sets, and interests may be vastly different (at odds, even) with those who rely on you. To be an exceptional data scientist, you must seek to understand others - not demand that they seek to understand you.

When you join a new organization, you will be inundated with hundreds of new terms, acronyms, processes, and the general enormity of that specific business apparatus which is yet unknown to you. You must seize upon these unknowns with relish, and regard the lot of them as an opportunity to learn, to educate yourself, and to improve yourself as a data scientist and businessperson. If you do not, you will likely fail to understand what really matters, and fail to deliver a product that really makes a difference.

In business, ignorance is not bliss. Ignorance is an inexcusable product of laziness and short sightedness, and serial ignorance is a fireable offense.

It’s not the ‘not knowing’ that is a problem. It is ‘knowing that you don’t know, and not seeking to fix that.’ You must be willing to ask questions. You must be willing to look stupid. You must be willing to do so often. As a data scientist, your job is to be as well versed in your company’s business model as your CEO. This simply won’t happen if you do not ask questions.

Frame every aspect of your communication in terms of why it is important to your audience.

Know your audience. Understand what motivates them. Understand what is important to them. If you work at a SaaS company, and your stakeholder is the Director of Customer Support, their job depends on being able to stop customer churn. That’s the first, second, and third thing they care about.

Communicate everything in terms of how it affects the other person:

“This model will allow us to predict 75% of customers who will churn, at least one month before they try to do so. It has a precision of 50% - meaning that one out of every two predictions will be a false positive. With a company-wide historic churn rate of 4%, on a base of 100,000 customers, this means that our model will identify 6,000 potentially at risk customers each month - half of which will really be at risk. The model will miss 1,000 at-risk customers each month.”

You should have already worked with the support team to identify that their agents can contact an average of 20 customers a day, and that they have an available team of 12 people. They can therefore contact 240 people a day, or about 5,280 customers per month. Communicate the implications of this math to the stakeholder:

“I understand your team can reach out to an approximate maximum of 5,280 customers per month - 420 per agent. You will therefore need to devote all available resources to acting on this model, and increase capacity by a minimum of two more agents, in order to execute.”

Eliminate jargon when communicating with non-technical people.

The Director of Customer support does not care about the inner workings of the churn prediction model you built for him. Do not try to explain it in technical terms.

In the same way, your CMO does not care about the process by which you fine tuned the hyper-parameters on your lead prediction model. She probably doesn’t know what a hyper-parameter is. It’s not her job to know. But it is your job - and your responsibility - to make sure she understands the implications of your model’s performance. She must be equipped with a clear understanding of what your model will do, where it will perform well, and what shortcomings to look out for.

Ask yourself, “What decisions will my work influence? How can I equip my stakeholders to best make these decisions?”

If you are the kind of data scientist that provides business insights upon which decision makers rely, do not trick yourself into thinking your job is to “just provide the data.” Your job is to provide a logical, evidence-driven presentation of facts, which executives will use to make decisions. Your job is to make sure that the strategic direction of the business is built upon sound premises, and the tactics employed to execute that strategy are informed by good data.

To quote an executive I used to work with: “Metrics are third order. Metrics answer questions, and questions drive decisions.”

People will often ask you for metrics. What they really want are answers to questions. Anticipate this. Think a few steps in advance. If someone asks you to perform a cluster analysis looking at your existing customer base, ask them what they intend to do with the clusters, once they have them. If you discover anomalies, investigate them and provide a clear and comprehensive analysis of your findings.

Aim not just to be understood, but to not be misunderstood

The worst thing is to be misunderstood. Miscommunication can cause more harm than no communication whatsoever. You must aim to eradicate all potential areas of misunderstanding from your communication. Refine your written and oral communication alike. Obsess over this.

Distill your message to its essence.

“Brevity is the soul of wit,” says Polonius. Stephen King popularized the expression, “to write is human, to edit is divine.” There is a reason for this. In communication, less is often more.

While I obsess over understanding my stakeholders, and make a point of asking as many questions as possible, I try to keep my own communications brief. I do not inundate people with surplus or irrelevant information. Communicate as little as possible to communicate everything necessary. Make yourself understood.

Views: 217

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Videos

  • Add Videos
  • View All

© 2019   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service