How To Become A Data Visualization Expert

Business leaders depend on data to make decisions. In order to act quickly, they don't have time to sift through spreadsheets or query massive databases. Instead, they rely on data visualizations—charts and graphs that provide a pictorial summary of a dataset to show concern leaders what they need to know.

Today's businesses increasingly rely on data analysts to examine datasets and create information visualizations that aid conclusion-makers in various roles. According to Alice Mello, a professor in Northeastern's Master of Professional Studies in Analytics program, these professionals should refine 2 major information visualization competencies: the power to work with datasets and an understanding of the all-time ways to clearly depict the conclusions drawn from looking at the information.

Continue reading to learn how to meliorate your data visualization skills and the importance of effectively communicating your findings to a range of audiences.

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Key Steps and Skills of the Data Visualization Procedure

Step ane: Working with Datasets

Since proficient information is the foundation of a good data visualization, it'south important to commencement empathize the data set that's beingness used to create the visualization, Mello says. "Y'all demand to be able to bear all the exploratory data assay that'south necessary to come across what stands out," she says. "Y'all demand to see the patterns and the 'a-ha' moments in social club to tell a story."

These patterns could include a decrease in sales at a certain time of year, increased production from one particular factory, dissimilar patterns in employee computer usage since a department shifted to remote work, or just about anything that might exist of interest to a business leader. The value of information visualization is presenting this data in a way that helps business leaders extract meaningful information at a quick glance and without the need for further caption or assay.

A background in informatics or engineering is certainly helpful, Mello notes. Still, professionals from fields such equally economics, finance, or marketing who have taken courses in statistics should have the basic data direction skills necessary for creating data visualizations. Noesis of the R programming language is likewise one of the core skills for information analysts who often create visualizations, as this is the programming language normally used for big sets of data and for running predictive analytics.

There are 3 disquisitional skills for working with a dataset earlier it tin can be used to create a visualization: Agreement how to manage databases, learning how to use information visualization software, and knowing how different audiences may use the information. A cleaner dataset will enable more than accurate visualizations and ensure that the work tin can be done in less fourth dimension.

Learn how to manage databases.

Managing databases and being able to remember data are some of the most valuable information visualization skills, Mello says. This can include tasks such equally properly naming columns in a database, quickly searching a database, and joining tables. Information technology's likewise helpful to know how to run statistical tests and import data into dashboards, she adds.

It's also of import to know what types of data are represented inside the database, according to a tutorial from the information direction professional organization ISACA. Are the variables stock-still information points, such as a specific city or land, or are they ranges, such as numbers between 51 and 100? From there, you tin determine primal relationships among the variables, which could include growth over time, a set of rankings, or deviation from the norm.

Go proficient with information visualization software.

Professionals should have a adept grasp of the software tools that allow users to import data sets to create visualizations. Tableau is widely used among medium-sized and large companies, and other options include Domo and Microsoft Power BI. Mello notes that these products focus on creating data visualizations and not managing information sets, which is why it'south important to know how to work with information before it's time to put it into charts and graphs.

Sympathise the information's audience and purpose.

While examining a dataset that will be used to brand visualization, it's important to understand how the intended audition is going to apply that data . Data scientists, for example, will probable examine the dataset differently than sales managers or business analysts. What's more, data scientists may need to see much more of the dataset than other terminate users in society to draw a determination.

Knowing your audience in accelerate will aid yous utilize the correct data to create the nigh effective visualization. This requires getting to know your audience through stakeholder meetings or focus groups to understand what data they use to make decisions and what additional information they need. This volition ensure that the right data goes into a visualization, and that different needs for different audiences will be addressed.

Pace 2: Creating Visualizations

Once you have examined your dataset, determined which variables y'all'd like to compare, and prepared the information to be imported into a data visualization tool, information technology's fourth dimension to create the visualization.

The goal should be to tell a story with data , Mello says. Think of the structure of a narrative—at that place's background data, there'due south the revealing of your critical findings, and in that location's the discussion of what those findings hateful for the future. "You need to make it meaningful for the audience that's going to come across information technology," she says. "How do y'all put the story together? There's conflict and resolution. Try to walk them through that."

Cardinal tactical skills for this procedure include choosing the right blazon of visualization, keeping the visualization simple, and ensuring that information technology's easy for an audience to understand.

Cull the right visualization.

While there are seemingly countless options for depicting data in a visualization, ISACA'due south tutorial breaks the major types of visualizations into four categories, based on how viewers are meant to expect at the data:

Comparing of variables, typically through a line graph or bar chart
Distribution of variables, using a histogram for many variables and a chart for two or 3 variables
Composition of variables, with cavalcade or area charts to show changes over fourth dimension and simpler charts for static variables
Relationship amid variables, typically done with scatter or chimera plots

Other examples of visualizations include color-coded maps, heat maps, and box plots. Information technology'due south also possible to combine multiple types of visualizations into a single picture. For example, a bubble plot on top of a map can show data such as population density by state or canton. A combination of a bar graph and line graph can bear witness static variables (east.g., quarterly sales figures) and changing variables (e.one thousand., the growth in quarterly sales over time).

Make information technology piece of cake to read.

Design elements such as font, color, line thickness, and data arrangement tin all bear on a information visualization's readability. This matters non just for applied reasons but likewise for matters of accessibility, every bit visualizations should exist readable for the colorblind and the visually dumb.

ISACA offers several suggestions .

If the goal of a visualization is to compare a gear up of variables, list the variables from largest to smallest and make each variable the same color. This will focus the eye on the items that need to be compared.
Ensure in that location'south enough contrast between foreground and background colors.
Avert combinations of colors that are hard to differentiate (such as orange and yellow) or combinations that the colorblind cannot differentiate (such equally red and light-green.)
Choose color combinations that can be easily replicated in a black-and-white visualization.
Use dissimilar line styles, such equally dotted, dashed, our double lines, to distinguish patterns if necessary.

Information analytics consultancy Key2Market provides boosted recommendations .

Utilize a single font with articulate variations such every bit assuming text and larger/sizes instead of multiple fonts.
Align and sort all pattern elements to the left side, as that'southward where a viewer starts looking at a folio.
Place labels for variables equally close as possible to a chart'southward confined or a graph's lines to get in easier for the eye to link a characterization to its respective variable.

Keep the visualization make clean.

ISACA's tutorial points out that our brains have a limited cerebral load. If there'southward also much data in a data visualization, then the encephalon cannot make sense of the information. Anything that doesn't back up the bulletin of the visualization is simply going to distract viewers.

"Y'all should have a clean dashboard that doesn't include too much information," Mello says. The size of your dataset may tempt you to create a graph or nautical chart with many variables, but the most constructive data visualizations focus specifically on the information that matters.

Equally a Harvard Business Review tutorial notes , viewers typically but have a few seconds to look at a chart, interpret it, and take activity. For example, a line graph that tracks too many variables or charts a variable across a longer time flow than necessary will get in difficult to determine where viewers should focus their attention. Call back about the specific betoken that you're trying to brand with your data—the change in prices over fourth dimension, for example—and get rid of anything in the visualization that is a distraction from that bespeak.

Step 3: Communicating the Data'southward Implications

While the goal of a good data visualization is that it tin speak for itself, in many cases, you may be called upon to talk over your visualization in front end of a stakeholder audience.

Reports prepared for business concern leaders are oftentimes the focal point of internal meetings, and you may be tasked with creating a slide deck and a brusque oral presentation for the group. If you aren't leading the presentation, you may be asked to support the business leader by providing additional details or answering follow-up questions.

In some cases, data visualizations are used for external purposes, ranging from a shareholder presentation to marketing collateral such as an infographic, social media post, or white paper. Hither, you may be asked to write about the data visualization—in something as brusk as a 280-grapheme tweet or every bit long as a inquiry report—or to provide a condensed version of a presentation given to internal audiences.

Use clear, concise linguistic communication.

Regardless of the scenario, data visualization expert Bill Shander suggests that written and verbal communication about data visualizations works best when it uses clear and curtailed language. Being able to explain your work in simple terms is one of the virtually critical data visualization skills, every bit information technology ensures that a range of stakeholders and audiences can understand the information and what it means for their work and in their lives.

Build Your Data Visualization Skills

The Main of Professional Studies in Analytics program at Northeastern University helps analytics professionals prepare for a career using their skills in extracting, translating, and visualizing data to assistance organizations brand both tactical and strategic decisions. Download our guide below to learn more than well-nigh the skills yous'll need to accelerate your career and how an avant-garde degree can assist.