Data Analysis in Business — 5 Tips to Make Your Data Work More Impactful

October 5, 2020
data-analysis-business-communication

Learning data analysis skills is, of course, the first step for anyone who aims to work with data in a business context. But being able to impact the company's bottom line with your work requires more than just the ability to write code or query a SQL database.

Data Work in the Business World

Picture this: you're working on the data team of a large retail company. You've been building a project to segment customers by various metrics like age bracket, location, buying tendencies, etc.

Then, your manager stops by your desk on the way to a meeting and asks you to figure out who your "best customers" are. As she walks away, you realize that what "best" means isn't really clear. The biggest spenders? The most frequent buyers? Potential customers with a high chance of spending in the future?

This sort of issue crops up all the time in real-world data work. Learning the technical skills required for data analysis can be challenging. But it can also be difficult to know how to apply those effectively in business contexts.

5 Tips for Making an Impact with Your Data Analysis at Work

Once you've got the skills to do great data work, here are five tips that'll help you make sure people actually listen to what you have to say!

1. Visualize, visualize, visualize

A picture is worth a thousand words. That old cliché rings true when it comes to data. Many people have trouble gleaning insight from a spreadsheet, or a written report full of statistics. But everybody understands a bar graph.

Consider, for example, the bar chart below. It lacks clear axis labels and has no title. But even with a chart this vague and context-less, we could show anyone this chart and tell them to point to the month where spending was highest. Ten out of ten people would get it right.


The chart, by the way, is from our tutorial on analyzing your own Amazon spending.

People can understand even an unlabeled, context-less data visualization fairly quickly. Imagine how effective a well-labeled visualization in a clearer context can be!

Your bosses and coworkers may not understand how you get to the answers you bring them, and some of them may struggle to follow the implications if you bury them in a deluge of numbers. Instead, visualize it! Give them something to look at.

And while you're doing that, make sure you...

2. K.I.S.S.

We've got a full article on design tips for data visualization, but a lot of it boils down to this: keep it simple. Remove elements you don't need. Focus on the most important thing in your analysis.

This advice doesn't only apply to visuals. It's best to keep all of your communication simple and clear.

Data analysts tend to be detail-oriented, and chances are you found lots of cool tidbits as you worked through your analysis. You probably also encountered and navigated some interesting coding roadblocks along the way.

But remember: the people you're talking to may not care about, or need to know about, any of that. What your boss cares about is your ultimate conclusions, and what actions they suggest that can impact the company's bottom line. So in any kind of communication, focus on that big picture.

3. Remember your audience

Of course, what "big picture" and "simple" mean do depend, to some extent, on who you're talking to. Are you giving a report to the non-technical CEO about an important sales metric? Or are you talking to your lead Data Engineer about a data pipeline problem?

The former conversation calls for big-picture thinking, clear visualization, and a focus on actionable intelligence that relates to the company's bottom line. The latter will probably require you to get into the technical nitty-gritty. 

When you're speaking to a mixed or unknown audience, though, err on the side of keeping things simple and non-technical. You can always get into the tech details by answering follow-up questions if people have them, but you risk losing the room if you launch into a complicated, number-packed presentation in front of a room of people who just want to know the answer to a simple question: what does the data say we should do?

4. Ask for clarification

When you get unclear directions from your boss, like the "best customers" example above, there are a variety of ways you can handle it. In our Data Analysis in Business course, we'll walk you through some actual analysis that'll help you handle the problem.

But of course, the problem might be as simple as just asking: "Who are our best customers?" Your boss may want you to figure that out in the data, but it's possible she also has a specific group in mind.

When you don't know for sure, it never hurts to ask.

5. Don't cut corners

In a fast-paced, deadline-driven office, it can be really tempting to skip past parts of the data science workflow. You may be tempted to dive right into cleaning and analysis before really taking the time to get to know your data. 

But this is a trap, and a dangerous one at many companies, where executives may be just beginning to understand the value of data analysis. If you cut corners, you're liable to make a mistake. And giving your bosses incorrect information can permanently damage their trust in you, and in data analysis in general. 

(That shouldn't be the case, of course, but it often is. Especially for executives who have years of experience operating based on their instincts, being led astray by the data once or twice could be enough to convince them that data analysis isn't valuable, and they should go back to trusting their gut above the numbers).

Dig Deeper into Data Analysis for Business

Want to go deeper? We have a course that's dedicated to getting you hands-on and giving you experience with this exact topic: Data Analysis in Business.

Data Analysis in Business is part of our Data Analyst and Data Scientist in Python learning paths. (R folks, don't worry — we're planning an R version of this course, too, although we don't have a release date for it yet).

This course will help you take the technical skills you've learned in our other data science courses and apply them effectively in real-world business scenarios. You'll learn: 

  • How to deal with vague, "fuzzy" language (like in the scenario above)
  • How to assess and work with common business metrics
  • How to communicate your results effectively to non-technical audiences

In short: this course will teach you the "soft" skills required to ensure that your "hard" (technical) skills make a substantial business impact.

How do I get started?

Simply log in (or sign up for a free account) and dive right in! Completing the full course will require a Basic or Premium subscription, but the first full mission is free, so you can learn a lot without making any kind of commitment. 


Tags

course launches, data analysis


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