Simplifying Communication in Data Science

Have you ever been asked, "Can you explain this to me like I'm a 5-year-old?" We all nod along, understanding the gist—make it simple. But how do we truly put this into practice? Well, sometimes the best lessons come from unexpected places.

In my quest to unravel the art of simplifying complex ideas, I found inspiration in observing a kindergarten teacher. What intrigued me wasn't just her effortless connection but her knack for simplifying without jargon. It got me thinking—how does she do it? And more importantly, how can I apply this in my world of data science?

In my line of work, it's not just about crunching numbers, it's about bridging gaps between departments, aligning expectations, and making the complex seem simple. My observations of the kindergarten teacher shed light on a crucial aspect of effective communication: simplicity without losing substance.

She distilled ideas to their essence, no frills, jargon or technical terms. And it worked! The kids understood, engaged, and learned. This revelation struck a chord. How could I leverage this in my conversations with stakeholders, executives, and teammates?

Finding the balance between simplifying information without losing its essence or oversimplifying is crucial, and for us data science folks, it's a delicate task. We are wired for facts, evidence, and a touch of imagination and creativity. Striking this balance can be tricky.

Observing a kindergarten teacher reveals her ability to recognize what the child already knows and build upon it. It's like finding a common ground and expanding from there. As a data science expert, that's gold. Finding relatable examples or concepts to simplify intricate algorithms or analytics suddenly seemed achievable.

Perhaps the most impactful lesson was in avoiding information overload. As data professionals, we live and breathe the details, but drowning others with technicalities doesn't always lead to understanding. Just like the teacher didn't overwhelm her students, I learned to prioritize and present only the essentials..

Up until now, this story sounded like any other that people would tell you. The concept is simple right? Simplify information and pick what is absolutely necessary and then string that into a quick story. However, often simplifying is difficult. I'm here to provide you with an example from my life.

Here is a project overview (Example1):

A recruiting team is actively seeking positions for candidates whose projects have concluded. The objective is to place a candidate in a suitable role within 30 days of the end date of their current project. The success of a recruiter is measured based on the effort invested in finding a position and the success rate of placing candidates, with success defined as lasting in the position for at least 40% of the project lifecycle.

Upon receiving the data for analysis, it became apparent that there are duplicate recruiter names, posing a challenge in differentiation. To address this, a unique identifier, such as an employee ID, is required. This missing information is causing a delay in progressing with the metrics.

Your Initial statement: The data provided includes columns with recruiter names, candidate names, and other attributes. I observed that multiple recruiters share the same name, making differentiation challenging. To facilitate accurate analysis, I need a unique identifier, such as an employee email or ID, to be included in the provided dataset. This hurdle is causing a delay as I cannot proceed without this information.

What people from the other teams understand: There is an issue in the data, and the person handling the data is requesting the addition of information in the reports. The data is inaccurate, and tracking progress may be compromised. This request implies additional work for the team.

An alternate statement you can make: Hi team, thank you for providing the data. I understand that each recruiter is managing their work. Could I kindly request you to include your employee ID and email address in the dataset? This will enhance the details in your entries and facilitate the progress of building metrics.

People respond: Let me add that information right away. Thank you for the clarification, and feel free to ask if you need more details from us.

?In summary, distilling information involves simplifying communication. The initial iteration included excess information and jargon, while the second iteration was concise and to the point. Our goal is simplicity, making the communication process smoother and more effective.

Project Review Overview (Example 2):

Conducting a project review is your opportunity to present findings in dynamic real-world scenarios In a perfect situation, your project manager would pick the audience based on their interests. But in reality, you might have to talk to a mix of people.

In this exaggerated example, you find yourself addressing a CEO, an executive from operations, the head of recruiting, and the recruiters themselves—all within a tight 20-minute timeframe. Navigating such a broad audience defies the conventional instructions and principles of presenting your work. However, by understanding the roles of those attending, you can tailor your message effectively.

CEO:

• Requirement: Super high-level overview.
• Focus: Why resources are invested, the project's essence, outcomes, and its alignment with the big picture.

An executive from Operations:

• Requirement: People-centric understanding.
• Focus: Why the project started, who was involved, goals, and key findings, especially concerning individuals.

Head of Recruiting:

• Requirement: Team-centric insights.
• Focus: Understanding the team's effort, translation into success rates, and metrics for coaching and leading the team effectively.

Recruiters:

• Requirement: Results for showcasing, observation backing, and workflow improvement.
• Focus: Demonstrating the fruits of their labor, using results to support observations, and possibly making recommendations for workflow enhancements.

Now, armed with this understanding, let's build our story:

“This project revolves around our people and talent, with a focus on measuring recruiters' efforts and tracking success rates. This contributes to loyalty, satisfaction, and employee retention, building a robust talent pool. The Recruiting team collaborated with the data science team to develop and track metrics, completing the project in two months with x resources. It's scalable globally to track success rates across recruiting teams, currently standing at x%. Our goal is to follow employees for at least 40% of their project timeline. If you're interested, we can dive into the numbers and details.”

?This statement provides a global perspective, delivers relevant details, and opens the floor for further discussion. In conclusion, understanding the priorities of each meeting attendee is crucial. Rely on intuition and have a flexible guideline to tailor your communication effectively.

I hope I've been able to offer some helpful guidance on effective communication. Now you know what people actually mean when they say "Can you explain this to me like I'm a 5-year-old?"

Simplifying complex ideas, understanding your audience, and honing both technical and storytelling skills are key aspects to navigate this ever-evolving landscape successfully. Remember, keeping things simple isn't just a goal—it's a strategy that empowers you and your team to tackle challenges with confidence and purpose in the world of data science.