What Is Data Visualization: Uses, Types, Tools, And Techniques

“A picture is worth a thousand words.” This term encapsulates the significance of data visualisation, which is the depiction of data or information in graphs, charts, or other visual formats. According to the World Economic Forum, about 2.5 quintillion bytes of data are generated every day. It can be challenging to manage these enormous data volumes, which need precise analyses and a lot of computing power. Data visualisation is one technique to make such information more understandable. In this article, we will explore the basics of data visualisation and cover other areas of interest like the important features of data visualization tools, data visualization in data mining, data visualization tools, data visualization techniques, data visualization examples, types of data visualization, data visualization in data science, and much more. So, keep reading!!

Introduction

Every day, data on sales income, marketing performance, customer contacts, inventory levels, production metrics, staffing numbers, expenses, and other KPIs are generated by your company. It might be quite difficult to interpret and analyse this data. Making people grasp this amount of detail can be much more challenging.

On the other hand, data visualization tools may give an accessible approach to observing and comprehending trends, outliers, and patterns in data by using appropriate visual components such as charts, graphs, and maps. All of that detailed data can be turned into readily understandable, visually compelling—and useful—business information using data visualisation.

We need to comprehend progressively bigger batches of data as big data becomes more prevalent. Machine learning makes it simpler to do analyses such as predictive analysis, which may be presented as useful visualisations. Data visualisation is not just crucial for data scientists and analysts; understanding data visualisation is necessary for any job.

What Is Data Visualization?

The art of visualising data and information in the form of graphs, charts, or maps is known as data visualisation. The goal of data visualisation is to make it simple for people to grasp their data at a glance. Its major goal is to turn huge datasets into visual images so that complicated relationships in the data may be easily understood.

Because of its complexity, using data to give meaningful answers necessitates knowledge from various professions, including statistics, data mining, graphic design, and data visualisation. Your data visualisation efforts should clearly explain the insights you’ve gained from your data, illustrate trends and patterns, and make your data available to everyone in your organisation. Answers from your visualisation should take no more than a few seconds. ​​Visualisations that can effectively explain a concept, simplify complicated data, and present insights at a look are the most effective.

Patterns, trends, and correlations that go undiscovered in text-based data may now be easily revealed and evaluated because of rapid advances in data visualisation tools. Data visualisation tools allow users to pick the best method to show data and often include a dashboard component that allows users to combine numerous studies into a single interface for maximum effect.

How Is Data Visualization Used?

Data visualisation is a great tool for sharing and presenting information because it allows you to see the storey behind the statistics. Data visualisation may be used for various purposes, including exhibiting performance, discussing trends, determining the effectiveness of new methods, and displaying linkages. In a minute, we’ll go through the many types of data visualisations and visualisation charts, but for now, here are some of the most typical applications of data visualisation.

  • Monitoring trend – This is, without a doubt, the most prevalent use of data visualisation. The majority of data includes a temporal component. As a result, most data analysis is focused on observing how data changes over time.
  • Determining frequency – Data visualisation aids in determining the frequency with which significant events occur throughout time.
  • Determining relationships – Finding correlations is a beneficial use of data visualisation. Without a visualisation, determining the link between two variables is exceedingly difficult.
  • Examining networks – Market research is an example of using data visualisation to examine a network. Marketers must determine which audiences to address with their message. As a result, they conduct a comprehensive analysis of the industry to find audience clusters, bridges between clusters, influencers within clusters, and outliers.
  • Scheduling projects – Things may get complicated when putting out a schedule for a large project. A Gantt chart overcomes this problem by clearly outlining each project job and its time to finish it.
  • Analysing return and risk – Many variables must be considered while calculating these complicated measurements. Data visualization may be as easy as color-coding a formula to indicate worthwhile and hazardous chances.

Features Of Data Visualization

Information should be conveyed using data visualisations. They should be able to tell a tale. As a result, there are key qualities that should be included in every visualisation:

  • Indicators: These depict the hierarchy and structure of a set of data on a certain topic. They emphasis the most crucial facts.
  • Simplicity: The information must be simple to understand. The reader instantly understands the information.
  • Brevity: The message should be brief and to the point, with no extraneous information accessible.
  • Originality: Data from seemingly unrelated sources must be collected and presented in a way that gives readers a new perspective on the issue.
  • Colour: The most significant bits of information should be highlighted in data graphics. It must employ colour palettes that are simple and easy to grasp.
  • Aesthetics: The graphics must be lively, well-designed, and pleasant to look at.

Importance Of Data Visualization

Consider how much data we generate daily. According to data specialists, each person generates around 1.7MB of data per second. This fast data production is also happening at a roughly exponential rate, which means that approximately 90% of all data has been produced in the previous two years. When we think about visual analytics and data visualisation, we think of how they can help us manage, comprehend, analyse, and communicate data.

Rich graphics may be fantastic tools for expressing ideas for organisations, decision-makers, and all types of analysts. The ending advantage of data visualisation is its capacity to promote improved decision-making. Here are some data visualisation examples that may aid strategic decision-making:

  • See the big picture – The transaction, interaction, process, and behavioural data saved in your systems contain a clear picture of performance. You can detect the larger context and higher-level situation inside data visualisation. As a consequence, you’ll see trends and patterns that you wouldn’t notice if you were just looking at data.
  • Identify the significance – Adding visual clarity to the storey presented by your data aids in the discovery of insights that lead to better decision-making, planning, strategies, and actions. What has to be changed in your firm, and where should you concentrate your resources? Understanding the value of your data leads to more efficient operations and choices.
  • Make informed decisions – You can be certain that your judgments are supported by data when you have solid figures and practical insights. Having a thorough understanding of performance indicators gives you the knowledge and tools you need to make the best decisions possible at the appropriate moment.
  • Track trends over time – Trends will begin to appear once you’ve set a baseline. Keep track of your progress, look for patterns, and start applying what you’ve learned to make more educated, smart decisions. Shifts in patterns reveal whether things are going off track as you construct your trends, allowing you to address any signs of poor performance right away.

Benefits Of Data Visualization

Important insights might be found in your data to help you move your company ahead. However, looking at raw data alone does not necessarily allow you to connect the links. Patterns, relationships, and other insights arise when you look at your data in a visual manner that would otherwise be hidden. Data visualisation puts statistics to life, allowing you to become the master storyteller of hidden insights. Data visualisation aids users in rapidly and effectively developing meaningful business insight through live data dashboards, interactive reports, charts, graphs, and other visual representations. It also has other advantages, such as:

  • Organisations may use data visualisation to determine where their performance is strong and space for development.
  • Data visualisation has been found to shorten meetings, minimise the time it takes to obtain information, and increase overall productivity.
  • Organizations that use data visualisation see a boost in their profits.
  • The ability to see patterns allows leaders to understand the company’s performance better. It gives them the knowledge they need to build on positive trends and reverse bad ones.
  • People who aren’t accustomed to massive data sets can use data visualisation to extract information and develop conclusions.
  • Visual aids as a teaching tool have been found to encourage thinking and improve learning conditions in studies. It helps to clarify content and make it more engaging. 

Data Visualization Types

Modern data visualisation tools go beyond the restrictions of Microsoft Excel’s fundamental charts and graphs, showing data in more complex ways. Data visualisations are divided into five categories:

  • Temporal – If data visualisations meet two criteria, they fall into the temporal category: they must be linear and one-dimensional. Lines that may stand alone or overlap one other, having a start and finish time, are commonly used in temporal visualisations. Scatter plots, polar area diagrams, time-series sequences, timelines, and line graphs are examples of temporal visualisations.
  • Hierarchical – The hierarchical category includes data visualisations that organise groupings inside bigger groups. If you want to present clusters of data, especially if they come from a single source, hierarchical visualisations are the way to go. These graphs have the disadvantage of being more intricate and difficult to understand, which is why the tree diagram is so popular. Because of its linear course, it is the easiest to follow. Tree diagrams, ring charts, and sunburst diagrams are examples of hierarchical representations.
  • Network – Visualizations of network data reveal how they relate to one another inside a network. To put it another way, it displays links between datasets without relying on lengthy explanations. Network visualisations include matrix charts, node-link diagrams, word clouds, and alluvial diagrams.
  • Multi-dimensional – Multidimensional data visualisations, as the name implies, have numerous dimensions. This means that to generate a 3D data visualisation; there must always be two or more variables in the mix. These sorts of visualisations are the most colourful or eye-catching due to the multiple concurrent layers and datasets. These visualisations may condense a lot of information into a few crucial points. Scatter plots, pie charts, Venn diagrams, stacked bar graphs, and histograms are examples of multidimensional visualisations.
  • Geospatial – Geospatial or spatial data visualisations overlay familiar maps with diverse data points and link to real-life physical places. These data visualisations are typically used to show sales or acquisitions over time and are particularly well-known for their usage in political campaigns or to show market penetration in multinational firms. Geospatial data visualisations include flow maps, density maps, cartograms, and heat maps.

Data Visualization Techniques

The process of appropriately expressing visual data necessitates the use of the following data visualisation techniques:

  • Know the audience – For every business, whether small or large, the audience must be taken into account first. The project gains more proficiency and dependability by listing the audience kinds and discussing them with your colleagues for further advancement. The need of focusing on relevant facts based on the sort of audience is critical.
  • Choose the correct chart – Begin by sketching or drafting a rough draft on paper. Share it with your coworkers and brainstorm how to enhance it. Make it on your computer and continue to customise it. You should start with a conventional chart like a bar or line chart if you’re new to data visualisation. You may gradually introduce less known chart forms, such as bubble charts and dot plots.
  • To offer descriptions, subtitles can be added. A perfect detail is a two-line or three-line detail. Annotations with a two-line caption can offer contextual information.
  • Use colours – Colors have a big impact on how well the charts look. They improve the visual attractiveness of the charts. Avoid utilising Google or Excel’s default colours. Instead, take advantage of the chart’s colour scheme. Instead of printing your chart straight away, preview it in grayscale. If necessary, check the adjustments that need to be made. For the chain’s last message, choose a dark or bright contrast.
  • Examine the draft – Various tests can be used to monitor progress. Request that your coworkers review the document and make any necessary modifications or enhancements. The finest assessment is the data visualisation checklist, which allows you to analyse your draught thoroughly.
  • Share the chart – This is the last phase, in which the chart is shared across other platforms to see if it can adapt to its environment. Presentations, webinars, handouts, and social media can all be used as platforms.

Data Visualization Tools

With the amount of computing power accessible in today’s environment, turning enormous amounts of data into meaningful visualisations is easier than ever before. You’ll need the correct data visualisation tools for this, of course. We’ve compiled a list of some of the most popular data visualisation tools accessible right now. These include a wide range of roles and requirements, as well as technical skill levels:

  • Tableau – Professionals may use this business intelligence application to see and comprehend their data. You may create interactive graphs and charts, as well as dashboards and spreadsheets, with it.
  • Weka – The visualisation panel in Weka is a valuable tool for those who are interested in data mining. In this open step, you can learn more about Weka to visualise your data.
  • Python – Python is a computer language with many open-source packages for data visualisation. Tools like Matplotlib and Pandas Visualization can assist you in creating live, interactive, or customised graphs.
  • R – As we saw in our initial step on data visualisation in R/RStudio, the programming language allows you to quickly employ simple charting algorithms or use packages to apply more complicated functions.
  • Excel – You can simply visualise your data with Excel if you want to make basic charts and graphs. Many people already have skills in this area, making it a quick solution. 

Data Visualization Examples Of Charts

Column Chart – A column chart is a means to demonstrate a comparison between multiple sets of data and is perhaps the easiest way to depict data. You may also use them to chart the evolution of data sets over time. Only modest and medium-sized data sets are suited for column charts.

Bar Chart – Data is organised into rectangular bars in bar charts, making comparing related data sets a breeze. Bar charts are similar to column charts. They are best used to illustrate change over time, compare various categories, or analyse sections of a whole, except that the latter has a restricted label and comparison area.

Line Chart – Line charts are great for displaying data about a continuous variable, such as time or money. Trend, acceleration, slowdown, and volatility may be depicted using line charts. Use them to analyse your data for trends, patterns, and fluctuations, compare distinct but related data sets using various series, and generate forecasts.

Scatterplot – Scatterplots show how two sets of factors affect the relationship between elements. Scatterplots are important when looking for outliers or analysing data distribution since they may show a correlation in a vast amount of data.

Sparkline – Sparklines are short line graphs that don’t have axes or coordinates. A sparkline depicts the overall shape of variance in a set of measurements, usually across time. When you want to demonstrate a specific trend behind a metric, use a sparkline with a metric that has a current status value recorded over a certain time.

Pie Chart – Pie charts are more effective with a smaller data set and are best suited for generating part-to-whole comparisons with discrete or continuous data. To compare relative values, compare pieces of a whole, or swiftly scan data, use a pie chart.

Gauge Chart – Gauge charts can rapidly show how a field is doing compared to how it is predicted to behave. The depiction of a Gauge chart displays your selected measure along a scale, with colour indicating where it falls on the expected scale. The arrow beneath the value ranges indicates your current measure on the scale.

Waterfall Chart – The cumulative effect of progressively added positive or negative numbers can be seen using a waterfall chart. These intermediate values might be categorical or time-based. A waterfall chart can be used to display a number’s component or makeup.

Funnel Chart – Funnel charts aid in the visualisation of a linear process with sequentially connected phases, each representing a proportion of the total. To visualise a succession of steps and their completion rates, use a funnel chart.

Heat Map – Color intensity is used to depict values of geographic regions or data tables in heat maps, which convey categorical data. They demonstrate how the two measurements are related and offer rating information.

Histogram – A histogram is a combined vertical bar chart and a line chart. A histogram is similar to a bar graph; however, it only has one variable instead of two. A histogram can compare data sets across time or display data distribution.

Box Plot – A box plot (also known as a box and whisker diagram) visually illustrates numerical data groups in terms of their quartiles, generally across groups, using the lowest, first quartile, median (second quartile), third quartile, and maximum. Individual points can be plotted to represent outliers. Use a box plot to visualise or compare data distribution and to find the lowest, maximum, and median values.

Map – When you need to study and show data connected to geography and exhibit it on a map, map visualisation is the tool of choice. They let us see the distribution or proportion of data in each region visually. When geography is an important aspect of your data storey, use a map.

Table Chart – The data table effectively analyzes classified items for comparative data analysis. Tables enable you to display data as well as images like bullet charts, icons, and sparklines. Display two-dimensional data sets that may be sorted categorically or enormous volumes of data in a table.

Area Chart – A time-series connection is shown by an area chart. They may depict volume, unlike line charts. Area charts are commonly used to compare two categories. Use an area chart to see how different pieces fit together.

Radar Diagram – A radar is a two-dimensional chart plot various quantitative variables with one or more data series. They’re useful for figuring out how things in your data differ compared to one another.

Treemap – Treemaps are visualisations for hierarchical data made up of nested rectangles with sizes proportionate to the relevant data value and divided down into 2-3 levels to demonstrate the hierarchical link between objects.

Bubble Chart – A bubble plot is a scatter plot with bubbles best used for viewing values for particular geographic regions. In contrast, a bubble map is best used for visualising data for specific geographic regions.

Data Visualization Use Cases

Everywhere, data visualisation is employed. Pick a sector, and you’ll almost certainly discover a data visualisation application. The following are some examples of data visualisation: –

Travel – To give the best possible service, the travel business has traditionally relied on data. Companies can give the precise service their clients require at the best time and the lowest price by using data to forecast when, where, and how they will travel.

Energy – The energy business must maintain a continual balance between supplying the appropriate quantity of energy and reducing greenhouse gas emissions. Power plants can estimate minute-by-minute, hour-by-hour energy demands based on everything from the season to the time of day by examining previous demand and then utilise this information to supply the necessary energy.

Insurance – Risk would be estimated using criminal records, credit ratings, and loss history like traditional insurance. Using more advanced data analytics technologies, on the other hand, you may include an even larger range of sources to provide a more detailed picture of risk for a single consumer.

Finance – Finance has always been about numbers, but advanced algorithms that can gather data from an ever-increasing number of sources may help traders make better judgments. Use live and historical data sources to detect new possibilities quicker than people can understand them and achieve a competitive advantage by discovering new chances.

Razor-thin margins characterise agriculture.

Agriculture – Agriculture is characterised by razor-thin margins. Farmers will be able to construct a far better picture of their predicted expenses and losses year after year if they can estimate variables like crop prices, pesticide dosages, and animal health.

Health – It’s critical to deliver the correct healthcare at the right time; thus, evaluating huge, up-to-date information to uncover demographic trends can assist provide greater public safety support. Data may be used to examine long-term patterns, such as the ageing of populations in industrialised countries, and assist policymakers and practitioners in reorienting their skills and approaches to meet the demands of a new kind of patient.

Retail – No industry better encapsulates the fundamentals of supply and demand than retail. Customers have always utilised data to understand how they buy, but data analytics will enable this to become much more precise.

Frequently Asked Questions

What is data visualization, and why is it important? 

Data visualisation is the art of utilising visual elements and data to create a storey with data. Data analysis in huge data sets is difficult, and you can’t expect everyone to comprehend it as well as data analysts. The simplest approach to communicate analytical results to everyone is through data visualisation. By properly visualising information, you may speed up your business.

What is data analysis and visualization?

The process of transforming data into information is known as data analysis. This method identifies patterns in your data. As a result of this analysis, you can get a streamlined data set. These findings are more likely to apply to data visualisation software. As a result, you may create clear and straightforward images and reflect specific information.

What are the challenges of data visualization?

While certain technologies can assist you in making better decisions and tracking business success, you should be aware of some substantial risks.

  • Users won’t acquire benefits from your images no matter how appealing they are if the underlying data doesn’t communicate the proper storey.
  • Trying to squeeze too much information into a graphic might confuse and annoy consumers.
  • Many folks are at ease creating presentations with spreadsheets and other analytics tools.
  • When users manually manipulate data in spreadsheets to produce visualisations, they risk making data and mathematical mistakes, wasting hours of productivity, and disseminating incorrect information.

Conclusion

No matter what industry it is in, your company is likely to generate massive volumes of data. Good data visualisation does a lot more than just showing a bunch of numbers. It tells a storey and gives a straightforward solution to a specific topic, omitting the details. The ultimate aim is to use your insights to educate and engage your audience.

You may start asking more relevant questions once you’ve connected your datasets and seen them for the first time in visualisations. Your firm can become a data-driven organisation when every decision-maker has access to data.