What Skills Are Required for Data Visualisation?

Data Visualization Skills You Need to be a Successful Data Analyst

Data analysts are aware of the need of having hands-on experience with data visualisation techniques when analysing complicated data, especially for a more general audience. Your audience will have no trouble understanding your material if it makes sense and is conveyed in a transparent manner.

Businesses that rely on their skills actively seek out analysts such as these, not just to evaluate data sets but also to put up data visualisations that aid in a variety of decision-making processes. 

These experts need to focus on improving not one but two essential skill sets: first, their capacity to deal with data sets; second, their ability to recognise appropriate approaches that clearly explain the outcomes acquired while evaluating the data.

For data analysts to be successful in their line of work, they need to cultivate specific talents as well as an in-depth awareness of the technology that is geared towards data analytics. Yet, you should also place an emphasis on obtaining and refining a variety of soft skills.

The Core Concepts of Data Visualizations

To put it another way, data visualisation is the practice of presenting data results via the use of graphics and other visualisation techniques. It makes it possible for non-technical audiences to acquire a clear comprehension of data-based concepts, even if they do not have a prior foundation in data analysis.

The fact that data visualisation is required for the vast majority of data science professions demonstrates that it is an essential ability that every data analyst should possess.

Consider getting more training in data visualisation if you are insightful and if you have a penchant for untangling complicated information for other people. This will allow you to hone your abilities even further.

Using acceptable visual components to express your thoughts is one example of such a talent. Another example would be finding adequate data sources and developing relevant, engaging, and enlightening visualisations.

Understanding fundamental data visualisations is not difficult, and the internet provides a wealth of high-quality educational content that can be accessed at your convenience. Taking some classes to increase your ability to use data visualisation tools is another fantastic option that you should really consider.

Understand the Data’s Audience and Purpose

When it comes to visualising data, you need to think like your target audience. What do they prefer? What measures should they take when carrying out their function? You can structure your ideas, making them more practical, by learning as much as you can about those looking to use such ideas.

Consider your own answers to the following queries:

• Who exactly makes up your audience, and what are some of the things they want to pick up?
• What are the principal concepts you’d like to showcase? What do they need to deliver or implement after considering your data visualisation?
• What further information or data do you need in order to make your argument clear?
• The answers to these questions will make the foundation for your visualisation. It’s also a useful method for assessing your final product.

What if the people listening to you aren't highly knowledgeable about the topic? A simple data visualisation helps to present the information in a way that’s easily understood. However, if the target group knows the subject inside-out, then avoid oversimplifying with basic pie charts or bar graphs.

Proficiency with Data Visualization Software

Gaining experience with the many software tools available for data visualisation is an effective method for gaining data visualisation abilities. 

In point of fact, being competent with the tools and educational materials at your disposal is frequently the determining factor in whether or not you are able to produce an effective visualisation for your target audience.

When it comes to displaying the data, it is quite necessary for you to determine how the capabilities of the software solutions can best emphasise the goals. For example, if you wanted to convey the findings of a complex study in the form of a bar chart, you may use a straightforward interactive tool as your method of choice.

Ask the Tough Questions

The common trap for data analysts is to become an order taker. Your boss asks a series of off-the-cuff questions, and you run off to answer them — only to find that the answers don’t get to the crux of the issue. The cycle continues.

A skilled analyst asks tough questions before they start getting into the data. “What actions could we take if you knew that answer?” Nancy Duarte, a leader in business storytelling, emphasises the phrase “therefore we need to…” appended to the results of an analysis. Know the purpose of your work and how it will effect change.

Develop Audience Understanding

As a data analyst, you need to get into the shoes of your audience. What are their priorities? What activities may they do in their role? 

The more you know about the individuals who will consume your insights, the more you’ll be able to mould them in a way that they are beneficial.

Developing audience understanding is a critical component of effective data visualisation. Before creating any visualisations, it's essential to understand who your audience is and what insights they're looking for.

Here are some key steps to developing audience understanding:

• Identify Your Audience: Start by identifying who your audience is. Are they executives, data analysts, or business professionals? What is their level of technical expertise? Understanding these factors will help you create visualisations that are tailored to their specific needs.
• Determine the Purpose: Once you've identified your audience, determine the purpose of your visualisation. Are you trying to communicate a specific insight or trend? Are you trying to present data to support a particular decision? Having a clear purpose in mind will help you create visualisations that are focused and relevant.
• Consider the Context: Understanding the context in which your visualisations will be presented is also essential. Will they be presented in a report, a presentation, or on a dashboard? Knowing the context will help you create visualisations that are appropriate for the medium in which they'll be presented.
• Choose the Right Visualizations: Different types of visualisations are better suited for different types of data and insights. Understanding the types of visualisations that are most effective for your audience and purpose is critical.
• Test and Refine: Finally, testing and refining your visualisations with your audience is essential. Soliciting feedback and making adjustments based on that feedback will help ensure that your visualisations are effective and meet the needs of your audience.

Critical Thinking 

It’s not enough to merely look at data; you need to grasp it and expand its ramifications beyond the statistics alone. 

If you are able to think critically, you will be able to think analytically about data, which will allow you to see patterns, as well as extract meaningful insights and information from the data you have available. 

It challenges you to go above and beyond and dedicate yourself to thinking, as opposed to merely digesting.

Becoming a critical thinker can be difficult, but you can hone such skills by challenging yourself. The following time you are faced with an analytical task or exercise, try to consider the following: What is the significance of the pattern I see? What does the data say about what has been accomplished? What kind of deficiencies does it point to? When you are getting ready for a career in data analytics, one of the most important things you can do is work on developing your critical thinking skills.

Communication

At the end of the day, you need to be able to explain your findings to others. It doesn’t matter if you’re the most skilled, intelligent data analyst on the globe - if you can’t convey the patterns you notice to others without technical understanding, you’ve fallen short.

Being a good data analyst effectively means becoming “bilingual.” You should be able to address highly technical points with your trained peers and provide clear, high-level explanations in a way that supports business-centred decision-makers rather than confusing them. In addition, you should be able to do both simultaneously. If you can’t do so, you may still need to build your skill set as a data analyst.

Key Steps and Skills of the Data Visualization Process

Working with Datasets

It is essential first to gain an understanding of the data set that is being used to create the visualisation. This is because having good data serves as the foundation of good data visualisation. 

You need to be able to conduct all of the exploratory data analysis that is necessary to see what stands out. It's important that you have this ability. If you want to be able to tell a story, you need to be able to recognise patterns and have "a-ha" moments.

These patterns may be a decline in sales at a given time of year, greater output from one particular plant, changing patterns in employee computer usage since a department transitioned to remote work, or just about anything that would be of interest to a business executive. 

The value of data visualisation is presenting this data in a way that helps business leaders extract meaningful information at a quick glance and without the need for further explanation or analysis.

A background in computer science or engineering is certainly helpful. Nonetheless, experts from industries such as economics, finance, or marketing who have completed courses in statistics should have the basic data management abilities essential for developing data visualisations.

Knowledge of the R programming language is also one of the core skills for data analysts who often create visualisations, as this is the programming language commonly used for large sets of data and for running predictive analytics.

Before a dataset can be utilised to generate a visualisation, one must possess the following three abilities in order to work with it effectively: Knowing how to handle databases, understanding how to utilise data visualisation technologies, and knowing how different audiences may use the data. 

A more organised dataset will permit more precise visualisations and guarantee that the work can be completed in a shorter amount of time.

According to Mello, some of the most significant abilities for data visualisation are the ability to manage databases and the ability to access data. 

This can include tasks such as properly naming columns in a database, quickly searching a database and joining tables. She also notes that having knowledge of how to conduct statistical tests and input data into dashboards is also beneficial.

It’s also crucial to know what sorts of data are contained inside the database, according to a lesson from the data management professional association ISACA. Are the variables ranges, such as integers between 51 and 100, or are they fixed data points, such as a particular city or state? 

From there, you may discover critical links among the variables, which might include development through time, a set of rankings, or departure from the norm.

Creating Visualizations

Once you have examined your dataset, determined which variables you’d like to compare, and prepared the data to be imported into a data visualisation tool, it’s time to create the visualisation.

The goal should be to tell a story with data. Think of the structure of a narrative—there’s background information, there’s the revealing of your critical findings, and there’s the discussion of what those findings mean for the future. 

You need to make it meaningful for the audience that’s going to see it. How do you put the story together? There’s conflict and resolution. Try to walk them through that.

Key tactical skills for this process include choosing the right type of visualisation, keeping the visualisation simple, and ensuring that it’s easy for an audience to understand.

While there are seemingly countless options for depicting data in a visualisation, ISACA’s tutorial breaks the major types of visualisations into four categories, based on how viewers are meant to look at the data:

• Comparison of variables, typically through a line graph or bar chart
• The distribution of the variables, with a histogram being used for many of the variables and a chart being used for two or three of the variables
• Composition of variables, with column or area charts to show changes over time and simpler charts for static variables
• Relationship among variables, typically done with scatter or bubble plots

Other examples of visualisations include colour-coded maps, heat maps, and box plots. It’s also possible to combine multiple types of visualisations into a single picture. 

For example, a bubble plot on top of a map can show data such as population density by state or county. A combination of a bar graph and a line graph can be used to display both constant variables (such as quarterly sales figures) and variables whose values are constantly shifting.

Design aspects such as typeface, colour, line width, and data layout may affect a data visualisation’s readability. This is important not only for pragmatic reasons but also for accessibility reasons, as visualisations should be readable for people who are colourblind and visually impaired.

Communicating the Data’s Implications

In spite of the fact that the purpose of good data visualisation is to allow the facts to speak for themselves, you will likely be required to present and defend your visualisation in front of a group of stakeholders at some point.

Reports prepared for business leaders are often the focal point of internal meetings, and you may be tasked with creating a slide deck and a short oral presentation for the group.

In the event that you are not the person in charge of the presentation, you may be requested to assist the person who is in charge of the business by supplying extra information or responding to follow-up questions.

In some instances, data visualisations are used for purposes that are visible to the public. These uses can range from giving a presentation to shareholders to creating marketing collateral such as an infographic, a post on social media, or a white paper. 

Here, you may be requested to write about the data visualization—in something as short as a 280-character tweet or as long as a research report—or to deliver a shortened version of a presentation presented to internal audiences.

Conclusion

Data visualisation is an essential tool in today's data-driven world, but it requires a specific set of skills to create compelling and insightful visualisations. To be successful, data analysts need to cultivate specific talents and an in-depth awareness of the technology. 

Get more training in data visualisation if you are insightful and have a penchant for untangling complicated information, understand the data's audience and purpose, and gain proficiency with software tools.

Data analysts should ask tough questions before getting into the data, develop audience understanding, and use critical thinking to extract meaningful insights and information. Develop critical thinking, communication, and working with datasets to become a good data analyst.

Data visualisation requires the ability to manage databases, utilise data visualisation technologies, and understand how different audiences may use the data. 

Choosing the right type of visualisation, keeping it simple, and making it easy for an audience to understand are key to making a data visualisation readable. 

Data visualisations should be readable for people who are colourblind and visually impaired and can be used to communicate the data's implications.

Content Summary

• However, creating effective data visualisations requires a specific set of skills.
• From technical skills to design and communication skills, we'll examine the critical components of data visualisation that are necessary to create compelling and insightful visualisations.
• Whether you're a data analyst, data scientist, or business professional, understanding these skills can help you communicate insights and make informed decisions based on data.
• Consider getting more training in data visualisation if you are insightful and if you have a penchant for untangling complicated information for other people.
• When it comes to visualising data, you need to think like your target audience.
• Gaining experience with the many software tools available for data visualisation is an effective method for gaining data visualisation abilities.
• In point of fact, being competent with the tools and educational materials at your disposal is frequently the determining factor in whether or not you are able to produce an effective visualisation for your target audience.
• When it comes to displaying the data, it is quite necessary for you to determine how the capabilities of the software solutions can best emphasise the goals.
• The common trap for data analysts is to become an order taker.
• Know the purpose of your work and how it will effect change.
• As a data analyst, you need to get into the shoes of your audience.
• If you are able to think critically, you will be able to think analytically about data, which will allow you to see patterns, as well as extract meaningful insights and information from the data you have available.
• Becoming a critical thinker can be difficult, but you can hone such skills by challenging yourself.
• When you are getting ready for a career in data analytics, one of the most important things you can do is work on developing your critical thinking skills.
• This is because having good data serves as the foundation of good data visualisation.
• According to Mello, some of the most significant abilities for data visualisation are the ability to manage databases and the ability to access data.
• Once you have examined your dataset, determined which variables you'd like to compare, and prepared the data to be imported into a data visualisation tool, it's time to create the visualisation.
• Key tactical skills for this process include choosing the right type of visualisation, keeping the visualisation simple, and ensuring that it's easy for an audience to understand.
• While there are seemingly countless options for depicting data in a visualisation, ISACA's tutorial breaks the major types of visualisations into four categories, based on how viewers are meant to look at the data: Comparison of variables, typically through a line graph or bar chart The distribution of the variables, with a histogram being used for many of the variables and a chart being used for two or three of the variables Composition of variables, with column or area charts to show changes over time and simpler charts for static variables Relationship among variables, typically done with scatter or bubble plotsOther examples of visualisations include colour-coded maps, heat maps, and box plots.
• It's also possible to combine multiple types of visualisations into a single picture.
• Design aspects such as typeface, colour, line width, and data layout may affect a data visualisation's readability.
• Communicating the Data's Implications In spite of the fact that the purpose of good data visualisation is to allow the facts to speak for themselves, you will likely be required to present and defend your visualisation in front of a group of stakeholders at some point.
• In some instances, data visualisations are used for purposes that are visible to the public.
• Here, you may be requested to write about the data visualisation—in something as short as a 280-character tweet or as long as a research report—or to deliver a shortened version of a presentation presented to internal audiences.