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Data visualization – the most important information

Mankind has long presented all sorts of data in graphical form. Hardly surprising! According to a study by the Massachusetts Institute of Technology, 90% of the information transmitted to the brain is visual! Indeed, each of us has seen old maps depicting the image of the earth at that time, or individual cities. Today we are sharing our knowledge of data visualization – how it works, how it is defined, and what are its advantages. We will also present some examples of it, as well as look at the tools for carrying it out. Data visualization is an important field in today’s business, and we will try to introduce you to it.

Old map – an interesting example of data visualization that has been used for centuries
The map is a good example of data visualization

For the convenience of readers, we have divided the article into two parts: a basic one, explaining data visualization, and an advanced one. In the latter, those with at least basic knowledge of business analytics will be able to read about specific applications of data visualization and the benefits that their organizations can derive from it.

We invite you to read on!

In the foreground, there is an open book, and in the background, there is a code
The ability to use and understand data is called Data Literacy

A brief definition of data visualization

We encounter data visualization every day. Surely everyone has seen examples of line graphs or pivot charts. Just look at the front pages of newspapers. We see the visualization of inflation levels, comparison of average temperatures in recent years, or election results. All infographics, point, line, pie, bar or area charts, pivot tables, as well as heatmaps, cartograms, or histograms that we see are just visualized data. 

The global data visualization market expected to grow

With data visualization, you can track trends and make better decisions by making the most of the information you collect in your organization. What’s more, visualization will help you gain a full picture of your company’s business situation, which will enable you to draw valuable conclusions and translate into better forecasting.

According to Statista’s estimates, the global data visualization market will be worth $7.76 billion in 2023, compared to 2017, when the amount was $4.51 billion. The growth in the value of this market is therefore expected to increase by about 72%.

Advantages of data visualization

Data visualization makes it quick and easy to present and draw conclusions from vast amounts of data. Think about how many sides we are inundated with information from every day. Even the most analytically-minded CEO will not be able to synthesize data effectively on their own. In addition to collecting the data, the information will need to be interpreted and analyzed. With a visualization tool, the viewer needs less time to do so, which means decision-makers will get tangible value from the data much faster. Thus, time-to-value – a trendy term these days – will be shortened.

A man working efficiently at the computer thanks to data visualization
Data visualization tools save a great deal of time

In a nutshell – what you gain thanks to data visualization:

  • structure to the data you have;
  • reduction in the time it takes to make the right decisions based on the data;
  • minimization of decisions made intuitively;
  • the ability to explore data from anywhere in the world;
  • ease of sharing information inside and outside the organization;
  • faster reporting;
  • the wide availability of data and its better understanding by non-specialists; 
  • easier optimization of company processes;
  • saving time in meetings;
  • data storytelling, or “dressing data in stories” to present it in a more interesting way;
  • saving time and resources – no need to engage developers to create business analytics solutions;
  • democratization of data.

Data visualization – the best way to communicate information?

We’ve known for a long time that it is easier to understand a picture than strings of numbers or excel tables. Regardless of what we want to convey – the statistics of girls’ births in Liverpool in 1987-89, the growth of the company’s sales in the last quarter, or the progress of the construction of the Sagrada Família church – in each case it will be better to do it with an image.

By visualizing the data, e.g., in the form of charts, you increase the chance of achieving the desired result: effectively sharing the information with the audience. Modern tools aggregate huge amounts of data – it would be virtually impossible to understand it without some kind of visualization.

A team of business analysts working on data visualization
Analysts working on data visualization

What should a good data visualization be like?

Now for some tips. What is worth keeping in mind when preparing a data visualization? It must be understandable to the viewers and must not tire or confuse them. The primary goal is to convey information effectively. It is, therefore, necessary to:

  • know for whom and why you are creating a particular visualization;
  • always check the correctness of the data;
  • use simple forms of charts like columns, lines, and points first;
  • combine several visualizations pertaining to the same area into one panel chart;
  • avoid three-dimensional charts;
  • label charts for immediate understanding – leaving no room for ambiguity;
  • use mostly subdued colors, highlighting the most important information with the brighter ones;
  • avoid elaborate fonts.

Finally, the most important thing: visuals should not be misleading! You will learn more about this topic later in this article.

Most interesting examples of data visualization

Let’s begin with interactive visualizations. Number one is probably the most visited site in the “hot phase” of the COVID-19 pandemic. The Johns Hopkins University of Medicine created a site where you could follow the spread of the virus in real-time.

Johns Hopkins University of Medicine Covid-19 application
Is there anyone who hasn’t visited this site during a pandemic at least once?
source: https://coronavirus.jhu.edu/map.html

In turn, NASA makes it possible to track fires breaking out around the world. It detects the fires from space using observation satellites orbiting the Earth.

A site where you can track fires, displayed on a computer screen
Tracking fires around the world has never been so easy
source: https://firms.modaps.eosdis.nasa.gov/map/#m:advanced;d:24hrs;@0.0,0.0,3z

Want to know the locations of mobile network transmitters? Nothing simpler – just check out the map below!

Screenshot of a database illustrating the location of mobile network transmitters – a great example of data visualization
OpenCelliD is the world’s largest open database illustrating the location of mobile network transmitters
source: https://alpercinar.com/open-cell-id/

Data visualization in the context of Big Data

Big Data is huge amounts of information in many datasets – tera and petabytes – that a human would not be able to comprehend. The information can come from many sources and be generated by humans or devices, such as vehicles, IoT ecosystems, or satellites. Companies need powerful tools and artificial intelligence to gather it quickly (almost in real-time), analyze it, and draw conclusions. 

Big Data is used in many fields, such as:

  • industry: to increase productivity;
  • agriculture: increasing yields, food security, better use of agricultural space;
  • public sector: statistical office, social security institutions or ministries, and their subordinate units – tax fraud detection, road network planning, unemployment prevention, etc.

Big Data also allows marketing departments to learn more about customers: what users click on (CTR), what forms of delivery they prefer, and what age groups they represent. Through data processing, city authorities can learn statistics related to traffic, drivers can find the best route, thanks to Google Maps, and in turn, banks can learn from customer behavior to prevent theft or extortion.

Abstract graphic with words Big Data written on it
Huge datasets are easier to understand with visualization

Embedded Analytics

Unlike traditional Business Intelligence, which involves the use of external applications, embedded analytics enables using analytical tools from within the business application already in use in the organization. Thus, there is no need to switch between applications, resulting in saving each user 1-2h per week (source: Nucleus Research, “Augmenting intelligence with embedded analytics” report).

This form of analytics allows you to integrate data with external applications, build dashboards yourself and customize them. Importantly, embedded analytics allows you to create and share reports with people outside your company: customers, partners, or suppliers.

Beware of misleading visualizations!

Over the years, people have become immune to advertising slogans or politicians’ promises. The case is somewhat different if we are presented with data in graphic form, and these are easily manipulated. But why would anyone want to visualize data in such a way as to mislead the viewer? If only to reinforce their narrative, although sometimes this can be an honest mistake. So what are some examples of, conscious or not, data visualization manipulation?

Inappropriate scale or change of scale

You should start the Y-axis at “0,” otherwise a difference of a few percent can seem colossal. That’s what Chevrolet meant in the – misleading – 1992 ad. It’s a classic example of cherry-picking, i.e., picking only data or events that fit the narrative.

An example of manipulation in a line graph in which the Y-axis does not start at zero
The Y-axis does not start at zero, which falsifies the message – in this case, it was a deliberate procedure

The scale on the Y-axis should be proportional to the X-axis, otherwise, the chart may not be readable. Moreover, the X-axis should cover a wider time range than, say, a few months. Otherwise, the graph may distort reality. It happens when companies want to boast of month-to-month growth, while on a yearly basis the growth would not be so visible.

Using an inappropriate chart to convey information

A good (bad) example is the pie chart and its use for data that does not add up to 100%. For example, when studying awareness of several brands among a target group, you should use the simplest column chart. In addition, in the case of a pie chart, each section of it should be described, which can make it difficult to read. Below are examples of how to present the statistics of visits to different social networks – one inappropriate, the other optimal.

An example of a Pie chart
Three-dimensional pie charts are sometimes uncomfortable to view
An example of a line chart used in data visualization
This is what a chart with multiple elements should look like

Another example is the use of a chart to present aggregate data. As a result, when presenting the sales of a product over the years, the cumulative numbers will show an upward trend, which can be misleading.

Some interesting facts in the context of data visualization

Now, here are some interesting business analytics facts. You will surely agree that data visualization makes sense and can fundamentally improve work.

  • 2.5 quintillion (10 to the power of 30) bytes are generated daily (source: FinancesOnline);
  • Facebook users post 350 million photos a day (4 petabytes, or 10 to the power of 15), and Twitter users send 500 million tweets daily (source: Raconteur);
  • British engineer, economist, and statistician William Playfair is considered the originator of the bar chart – the first known chart of this type was published in 1786 (source: historyofInformation.com);
  • human brains process visuals 60,000 times faster than text (source: University of Minnesota);
  • a Wharton School of Business study found that using data visualization can shorten business meetings by 24%.
A group of people at a meeting where data visualization has been used
Speed up business meetings with data visualization

Business intelligence tools for data visualization 

It’s no secret that the choice of tool will depend primarily on the needs of your organization. You will find many tools on the market. Some are uncomplicated, like spreadsheets or presentation software. The more complex ones include Google Data Studio, Tableau, Microsoft Power BI, and Oracle Analytics.

Logos of analytics and data visualization solutions
Which data analytics solution to choose?

What should you pay attention to when choosing a business intelligence tool? Firstly, your needs, available budget, and the size of your Data Analytics team – if you have one in your organization. It’s also important whether it will be used by professional analysts or business users, such as consultants, salespeople, or boards of directors, for whom analytics is not a passion, but a step on the way to a specific business decision.

For us, as a Salesforce partner, the obvious choice is Tableau – a solution integrated into the Salesforce ecosystem in 2019. Our Data Analytics department uses this business intelligence tool on a daily basis, helping customers gain invaluable insights from their business data. 

Salesforce logo
Salesforce develops an ecosystem of cloud solutions for managing multiple business processes, including data analytics

On our blog, you will find articles about business analytics. We invite you to read them!

Here, you can learn more about Tableau and, if you have questions, consult with our specialists who will advise you on the best solution for your organization.