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Simplifying Difficult Concepts

Technical explanations for a non-technical audience

17 Dec 2020 by David Gates

Effective communication is a vital skill that all data professionals must master. Data science has a lot of potential for businesses. One major obstacle, however, is that decision-makers often lack the relevant background knowledge to truly understand the field. This creates a major problem for data professionals: how can you communicate technical concepts with a non-technical audience?

This problem isn’t unique to data professionals. Imagine you take your car to a mechanic after hearing a mysterious rattle or seeing your engine light turn on. After taking a look at your car, the mechanic calls you and explains the problem in a way that only other mechanics would understand. They discuss parts you’ve never heard of. You’re completely unsure what this part does, what happened to cause this issue, and what will happen if you don’t fix it. All you know is that it’s going to cost you quite a bit of money.

Image by Ulrike Mai from Pixabay

The mechanic has failed to communicate effectively with you.

For those of you who have a trusted mechanic, it’s likely that their communication skills match their ability to fix cars.

When you enter a shop you know that they’ll be able to explain any potential issues in a clear way. They can give you details on what likely caused the problem, what the solution is, and what will happen if it is and is not addressed in a timely manner.

This situation is similar to what data professionals encounter. Having the ability to discuss your work with anyone is vital. It’s a differentiating skill. It makes you approachable and an indispensable part of an organization.

Language choice

So how does our language choice influence our audience?

When you begin a technical explanation and go too in-depth too soon your audience is likely to build a “communication wall.” They’ll assume they’re not able to understand this topic.

So for example, if I use the dictionary definition of regression:

“linear regression is a linear approach to modeling the relationship between a scalar response and one or more explanatory variables.”

This type of language quickly leads to a communication wall.

So it’s vital to use language to tear down communication walls. We can do so using a 3 step process.

  1. Softening language- First, use language to prime your audience for a technical explanation. You can use phrases like “If you ever…” or “If you know how…” This language is vital because it shows your audience that you understand their situation. You know that this topic may be challenging, but are already demonstrating that you have the communication skills needed to explain it in a clear way
  2. Relatable example- After priming the audience, choose a relatable example that highlights the concepts you’re going to introduce. By choosing something the audience is familiar with, they’re going to be more receptive to your talk.
  3. Summarize- Go over the details one more time.

Putting It Together

So how can we put this process together in a concise and logical way? Let’s go back and think about how we can explain regression to a non-technical audience: The IBM Skills Developer course “What is data science” has an excellent explanation of regression. They use softening language, a clear example, and a summary. Here’s how they explain it:

“Let me explain regression in the simplest possible terms. If you have ever taken a cab ride or taxi ride you understand regression.

Here’s how it works

The moment you sit in a cab you see there is a fixed amount there. It says $2.50. Rather the cab moves or you get out this is what you owe to the driver, the moment you step into a cab. That’s a constant. You have to pay that amount if you’ve stepped into a cab. Then as it starts moving, for every meter or hundred meters the fare increases by a certain amount. There’s a relationship between the distance and the amount you would pay above and beyond that constant.”

Image by LEEROY Agency from Pixabay

And if you’re not moving, if you’re stuck in traffic then every additional minute you have to pay more. So as the minutes increase your fare increases, as the distance increases your fare increases, and while all this is happening you’ve already paid the base fare which is the constant.

This is what regression is. Regression tells you what the base fare is and what is the relationship between time and the fare you’ve paid and the distance you’ve traveled and the fare you’ve paid.

Because in the absence of knowing those relationships and just knowing how much people traveled for and how much they’ve paid… Regression allows you to compute that constant that you didn’t know was $2.50 and it will compute the relationship between fare and distance and fare and time. That is regression”

Source: Coursera IBM Data Science Certificate: What is data science?

Overall, the language choices in this example were perfect.

The softening language was used well: “If you’ve ever taken a cab ride or taxi ride you understand regression. Here’s how it works”

This immediately ensures your audience is open and receptive.

The example is relatable. Everyone has taken a taxi and understands the pricing structure. Finally, it ends with an excellent summary: “This is what regression is…”

Introducing a complex topic to a non-technical audience in this way ensures they have a clear understanding. When used correctly, this approach will ensure that your audience leaves with the ability to explain this topic to someone else. This is the true sign of success.

Putting It To Use

Now that you have the framework, you can put it to use. A great way to get started is to think: how you could explain classification in a similar way.

Image by Free-Photos from Pixabay

Are you able to use softening language? Find a relatable example? Summarize? These are the steps needed to explain any complex topic. Once you develop these skills you’ll have the ability to confidently discuss your work with anyone.

You can learn more about this topic from my talk about essential communication skills for data professionals:

You can find me on LinkedIn and on my website

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