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3 Tips You Need to Be Successful in Data Visualization

Your data is good – but you can make it better.

Photo by Studio Republic on Unsplash
Photo by Studio Republic on Unsplash

I like to see data visualization as something similar to painting. The artist has a story to tell – he has everything pictured – the layout, concept, and inspiration needed to show his masterpiece to the world. The picture is still in his head but all that is remaining is to get his gear and start painting.

For your audience to get the full meaning of that story, your ability to visualize data must be top-notch. You need to present your analysis in the simplest and most understandable way possible.

The aim is to tell a story with your analysis.

After switching careers from a regular computer scientist to a data analyst, I realized that developing profitable skills in the Data Science industry was part of the switching process. Don’t get me wrong, a solid background in computer science is essential for every data scientist.

Nowadays, everyone utilizes the benefits of big data – financial institutions, small businesses, and thriving companies all rely on the concepts of big data to structure and simplify their database.

The data analyst collects and analyzes the data, but it doesn’t all end there. The information collected still needs to be presented to end-users who require that data to learn from or make valuable decisions. That’s where Data Visualization comes in handy.

To increase professionalism in data visualization, there are essential tips you need to get yourself familiar with. These tips aren’t just a one-time cheat code to success, to maximize your effectiveness you need to practice and make these tips part of your visualization process.

Without further opening, here are three tips that will aid you to take your data visualization career to the next level.


1. Focus more on your audience than the job.

Put your audience first.

There is always the drive to throw efforts into the content of your analysis, absolutely normal to want to make the project as neat and effective as possible. But a major mistake most data professionals make is neglecting the audience.

As James Stewart rightly put it,

"Never treat your audience as customers, always as partners."

It is necessary to make every visualization project meet the information needs of your audience. Assuming our analysis was going to be utilized by us the data scientists, we can basically visualize the components of the data using scientific methods.

I can even decide to leave the data sets in my notebook – I’m still going to get the best out of the information I need.

But we have an audience, which is probably a mixture of technical and non-technical people. Put yourself in the shoes of the audience.

  • Who is your audience? Are they tech-oriented or just random people based in diverse industries?
  • What do you want to be the first impression of your audience, cringe or fascinated?
  • When do they want to stop learning? Personally, I have a learning limit, once I exceed that limit everything just seems boring. Most people also possess similar traits, make some research and ask yourself how long you want your presentation to be – particularly if it is a video or slide presentation.
  • Why would they want to pay attention to you? According to research humans don’t like information presented with boring visuals. Hence, they tend to lose focus when they encounter information presented with bumpy or jagged visuals. The key is to maintain balance and keep it simple.

2. Nail the right concepts needed in your graphics.

Getting the perfect graphics is just as important as the data you’re presenting.

In data visualization, most graphics aren’t one size fits all – not all graphics work for everything – sometimes you need to think, look and go out of your comfort zone and select the right graphics that simplifies the essence of your data.

There are certain concepts binding the idea of graphical representation, all these work simultaneously to get the best out of data visualization. Let’s point out a few:

A. Charts:

Are charts dated?

From a personal perspective, charts are the most effective basic method of data visualization. Most times, going a bit ‘traditional’ might just be what you need to maximize your data science potential.

I worked on a data accounting project with a group of some of the best data professionals in the country – I’m talking a couple of talented men and women with trending workshops and stacks of publications to back up their work.

After data sorting and analysis, the project was concluded. But we were going to be presenting to people with little/no tech background, a group of business moguls who basically spent most of their lives studying the roots of economics and finance.

Since we already know our audience, the key was to narrow our data visuals to something more subtle. Again, more benefits to understanding your audience.

B. Patterns for layout:

As important as each concept is, pattern choices remain a firm category in data visualization.

Simply put, a good pattern will set the right tone, while an inappropriate pattern might misguide the narrative of how your audience will receive the data.

The human mind is constantly looking out for objects, people, colors, and even habitual actions we have come across – either recently or in the past. We are naturally visual observant. Our eyes and brain are quick to process certain pointers that illustrate or display the information we need.

As data scientists, how can we take advantage of this?

As much as you would love to diversify while selecting patterns, it’s best if you narrow your selections to predictable and common patterns.

Plain and effective is better than complex and confusing.

C. Colors:

I hate colors.

Just kidding.

Colors are great. Apart from being just great, they can be your ticket to a successful data visualization project. In data visualization, colors are the best alternative to words or numbers.

Applying the perfect blend of colors in your data visualization design will give the viewer a quick glimpse of the data and its objectives even before you start presenting.

While selecting keep these points in mind:

  • Limit your colors to 2–3, too many colors will just make the project amateurish.
  • If you’re working with temperature-related datasets, use red for hot and sky blue for cold.
  • Use green for positivity while red for uncertainties.
  • In the beginning, make the designs as captivating as possible.
  • Instead of using multiple colors. Choose one and modify the contrasts to match your datasets.

3. Stop being a cheapskate around data visualization software programs.

There are tons of software out there made specifically for data visualization. A good chunk of these tools are free to use, others require payments to access and some have a combination of both – free versions with limited features and paid versions that give you full access to premium features.

"There’s got to be another software, offering similar features for less the price"

We love to play economists on Wall Street when it comes to making purchases on application programs.

Everyone does that. We often tend to consider other options before making financial decisions.

On the contrary, going into data visualization with this mindset is not a good idea. Working with cheap tools possibly will ruin all the efforts you put into data analysis.

The results will not be professional and with the high level of competition in the data science industry, unprofessional work is the last thing you want in data visualization.

"Without professionalism I’d be an amateur, and the clients I want don’t hire amateurs."

Choose the right tools that perfectly meet the solutions you need. If it requires you to splash a couple of bucks on getting the maximum features, do it.

Tableau and SPSS are effective tools you can use for data visualization.


Applicable takeaway

The art of data visualization remains very delicate and requires a good level of technicality.

There are hundreds of blogs, online communities, and forums dedicated to data visualization that can help sharpen the skills you had previously acquired as well as develop new ones.

Data visualization is not just a skill, it’s a lifestyle. Keep learning and find new ways to get better.


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