Pros and Cons of Data Visualization

What Is Data Visualization?

The process of representing numerical information and data through graphical representation is referred to as data visualisation. This renders the data and information more approachable and easier to comprehend. 

This makes it simple to understand patterns and trends in the data by utilising graphical components such as charts, graphs, and maps. Moreover, it offers a simple method for presenting facts to a non-expert audience while yet guaranteeing that they comprehend it.

The process of turning raw information tables into graphical representations of numerical data that tell a story is known as "data visualisation." 

The two most important considerations to make when developing a viz are the selection of the data to be shared and the method through which the data will be shared.

The display of data may take on a variety of different forms. The majority of the time, perceptions are presented in the form of diagrams, outlines, graphs, and various sorts of mathematical explanations. 

On the other hand, the depiction of information could not stop there depending on it. An extra variety of information perception is represented in the form of guides, drawings, and air pocket diagrams. 

When you look at a map and notice that some countries have been highlighted, you are looking at an information representation.

Also, the employment of intelligent gadgets is regarded as the most superior way of information representation at this time. For the most part, all this entails is the application of channels inside the framework of standard representations. 

Consider the following: you have access to a bar graph that compares the rates of economic development in the three North American countries with the highest per capita income. 

A client can switch to a different continent by selecting it from a drop-down menu, which may be included in an intuitive information perception. In the event that she decides to go to Europe, we will investigate the degree of maturity in Germany, France, and Italy.

It is imperative that we keep in mind how important the story component is. Information representation that is devoid of an underlying message cannot in any way be considered information perception. 

The ability to effectively depict information is an essential tool for managers in any industry and organisation of any size. Information perception is essential in capturing crucial facts, assisting dynamics, concluding serious exams, planning, and sketching encounters. 

This is true regardless of whether you are a startup or a global partnership.

Pros of Data Visualization

Better Understanding

In the world of business, we are frequently confronted with the necessity of contrasting the results obtained by two distinct aspects or two distinct circumstances.

The conventional method involves first sorting through the copious amounts of data pertaining to both sets of circumstances and then doing an analysis of that data. This is definitely going to be a waste of a significant amount of time.

Putting the information on both of these factors into the visual form will be an improved approach to solving this challenge. This will undoubtedly contribute to a deeper comprehension of the circumstances.

For instance, using Google Trends to visualise and analyse enormous amounts of data relating to the most popular searches or queries helps us better comprehend the data.

If the same material had been provided in figures, it would have been quite challenging for the typical individual to comprehend this topic in a quick and easy manner.

Sales Analysis

A salesperson will have an easier time comprehending the sales report of items if they are provided with the assistance of data visualisation.

He will be able to comprehend, with the assistance of data visualisation tools such as heat maps, the factors that are contributing to the rise in sales figures as well as the factors that are contributing to the decline in sales figures.

The visualisation of data helps one better understand trends as well as other factors, such as the types of customers who are interested in buying, repeat customers, the effect of geography, and so on.

Better Analysis

Since it is less difficult to comprehend and more intuitive, data visualisation naturally results in improved analysis. This is because individuals are more quickly able to comprehend vizzes and draw conclusions from them. 

While doing an analysis of the data to reach relevant conclusions, the use of a visualisation that makes it simple to see patterns, outliers, and trends may be of great assistance.

Finding Relations Between Events

It is common knowledge that several things may have an effect on a company's performance. The ability of decision-makers to grasp the challenges that are relevant to their business is improved by the discovery of correlations between various aspects or occurrences.

For instance, the market for online businesses has been around for quite some time now. During specific holiday seasons, such as Christmas and Thanksgiving, the graphs of internet firms consistently show an upward trend.

Hence, if an internet firm is conducting an average of one million dollars worth of business during a certain quarter and the sales jump up during the next quarter, then they can immediately locate the events that connect to it.

Exploring Opportunities and Trends

Business executives are now able to uncover the depth of information regarding the trends and opportunities that are present around them as a result of the massive amounts of data that are already available.

The authorities are able to detect patterns in the behaviour of their consumers by using data visualisation, which opens the door for them to investigate trends and potential for the company.

Intuitive

The average person finds it far simpler to comprehend written words or figures when they are presented in a visual format. This indicates that the majority of individuals regard reading data visualisations to be a far more intuitive method of comprehending data than any other method. 

In this approach, even those who dislike mathematics or who claim that numbers confound them are able to quickly read and comprehend data, making it much simpler to ensure that all members of an organisation are on the same page.

Simple Data Sharing

Another advantage of data visualisation is that it makes it simple and straightforward to share data. This is because you can ensure that everyone is on the same page when they are seeing your visualisation. 

This is similar to the argument that was made before. A viz pulls everyone onto the same page and makes it simple to communicate if you are utilising a business intelligence platform. This is preferable to the alternative of relying on individuals to comprehend or interpret strings of numbers or raw data.

Easy to Communicate

In every one of these scenarios, the utilisation of data visualisations carries with it an undeniable benefit to the communication process. 

When we show rates and relationships graphically, it is much simpler to comprehend both of these concepts. On the other hand, we may utilise colours and straightforward labelling to make the image more simpler and more intuitive to comprehend.

Earn Attention

In addition to this, you capture the interest of people at all levels of study. There is a sizeable proportion of the general public that does not experience a sense of ease with numerical analysis and computation. If you try to describe the nature of relationships by employing any statistic that is more complicated than "rate" or "percent," you run the risk of losing a significant number of your readers.

It's not because they lack brains or anything like that. You won't win their attention by doing so since they are not interested in the information you are providing because they are not interested in it at all. 

But, if you are able to illustrate the desired relationship using a straightforward data visualisation, then you will win their thoughts and focus, even if it is just for a brief period of time.

This raises an essential subject to consider. One of the most important duties and difficulties associated with data visualisation is the requirement to "earn" the attention of one's audience rather than "take it for granted."

Adds Credibility

The trustworthiness of any communication may be increased by including data visualisation. Marshal McLuhan and Kenneth Burke are both well-known for their contributions to the field of communication sciences, where they pioneered the concept that the medium of communication may alter the way that the reader understands the information being sent.

Text, television, and podcasts are all considered to be distinct forms of media due to the fact that they cater to distinct senses. 

To summarise, forms of media can either be hot (requiring very little cognitive input) or cold (requiring a lot of such participation) (requiring much cognitive participation).

For instance, text is considered cold and hot, but a podcast is considered chilly. The first option doesn't ask the reader to "fill in the gaps," but listening to a podcast does!

The display of data takes use of this dynamic. Since they need a significant amount of interpretation and interaction from the viewer, data visualisations are considered to be a very frigid medium. Data visualisations are more inclusive, despite the fact that dry figures are authoritative.

They engross the viewer in the chart and demonstrate the author's trustworthiness by requiring active engagement from the reader. They guide the reader through the mental process and persuade him or her in a subtle way, just like an effective educator might.

Easy to Remember

A list of the advantages of data visualization would never be complete without mentioning memory. Perhaps the most important advantage of data visualizations is how easy they are to remember.

When’s the last time you remembered the exact words from a text? When’s the last time you could recall an exact figure about an important topic (death rates, for example)? For me, the answer is “I don’t remember!” However, I can easily still remember the chart we looked at above concerning N. American Countries’ populations.

For one reason or another, data visualizations are much easier to remember than text alone. But the tricky part it that you have to pick the right data visualization. If not, it could mean you confuse your audience, in which case they won’t remember anything. Let’s look at that more closely.

Cons of Data Visualization

What possible drawbacks could potentially bring down the whole experience, given how many positive aspects there are? The problem is that individuals may occasionally distort facts inadvertently or even intentionally, and as a result, your message may get muddled. 

Conversely, if you are not diligent in the way that you construct your visualisation, you can find that your findings are not accurate or that your visualisations are not accurate. Here, we will go into further detail regarding those drawbacks.

Incorrect Conclusions

The possibility that your audience would form inaccurate inferences is a drawback of utilising data visualisation, as was discussed previously. And this is not only because the visualisations were done incorrectly. 

When this happens, the viewer may get perplexed, and as a result, various individuals in your audience may draw extremely divergent conclusions after viewing the same visualisation.

This issue may be remedied by ensuring that your visualisations are well explained and that a suitable key is provided so that the viewer can comprehend the information being shown.

Inexact

When one creates a visual representation of numerical data, there is always the possibility that the observer may get an inaccurate impression of the data based on the representation. This is an inherent danger. 

Especially in the event that there are no keys or means to hover over the effects in order to examine the specific numbers that are involved. 

It will provide the viewer with an understanding of the facts and most likely be sufficient for them to develop their own conclusions; but, there is a possibility that they will draw inaccurate conclusions based on inaccurate data.

You may prevent this from happening by ensuring that your visualisation has accurate labels or is interactive enough to enable you to hover over and display more information.

False Correlations 

The process of finding correlations in your data may be extremely useful for making decisions and formulating strategies, but it also has the potential to mislead individuals into seeing misleading associations. 

Visualizations have the potential to display inaccuracies in how distinct variables behave in relation to one another depending on the integrity of the data being used and the person doing the interpretation of the data. 

This can lead to the identification of misleading correlations among the data, which in turn can lead to the formation of incorrect assumptions and conclusions.

Axes Make the Difference

The manner in which data is understood in a graphical representation of data, such as a graph or chart, is very susceptible to being affected by factors such as the size of the axes and the number of axes. 

The magnitude of an axis might lead viewers to incorrectly anticipate that there will be a more dramatic shift or value than there actually will be. In addition, the use of dual axes might lead the reader to incorrectly believe that two variables are computed based on the same base unit, even when this is not the case.

Average Is Not the Best Statistic

Due to the fact that outliers can cause the data to be skewed in any way, the average of a list of data is not the most accurate statistic. 

Although while the vast majority of data sets that are not tied to time adhere to a normal distribution, many data sets depart from the usual bell curve in some way, shape, or form, even if only to a very slight degree. 

In order to properly visualise data, it is essential to always search for the mean and the median. If there is a significant divergence, this suggests that the visualisation might not be an accurate representation of the data.

Correlation Is Not Causation

The use of correlations leads us to see relationships where none exist. The tendency for two numbers to follow a similar pattern over the course of time is known as a correlation. They are an excellent method for observing the behaviour of several variables in connection with one another. On the other hand, neither of them suggests that one is the cause of the other.

When we say that correlation does not imply causation, we mean just this. In point of fact, there are a few alternative explanations that one might use for a connection. If X and Y are shown to have a correlation, then either:

X causes Y 

Y causes X

The pair X and Y have no connection.

An additional variable, A, has an impact on both X and Y.

Most of the time, X and Y have no connection to one another. The visual juxtaposition of the several factors is enough to make us want to assume that there is a connection between them. 

If the data visualisation is honest, it will avoid displaying these deceptive correlations and will only present falsely comparable trends side-by-side when there is a credible explanation or when the person creating the visualisation can explain that there is no correlation between the two.

Abusing the Law of Large Numbers (LLN)

The concept that only very large samples can reliably yield accurate findings is known as the "rule of huge numbers." It is possible to hear it characterised in a more formal manner as the propensity for massive data sets to more closely represent reality.

What repercussions does this have on the display of data? It indicates that any data visualisation based on a sample size that is too small is subject to the same degree of bias as the sample size itself.

The fact that the viewer is unable to perceive the sample size is one of the drawbacks of using data visualisation when dealing with sample sizes that are relatively small. Take, for instance, the scenario in which you repeatedly flip a coin. Four out of the five occasions, the result is a head. At the end, you may claim that heads appear on coins four out of every five times (4 5).

Yet, this would not be an accurate representation. You would have to give the coin a toss a minimum of one hundred times. According to the principle of the law of large numbers, the size of our sample set directly correlates to the level of accuracy of our predictions. If you do this 100 times, you might see that heads comes up 51 times, which is equal to 51 percent.

Conclusion

In conclusion, data visualization is an essential tool for organizations that deal with large amounts of data. It has numerous benefits, including improved understanding of data, better decision-making, and increased productivity. 

However, it also has some limitations, such as the potential for misinterpretation and the need for expertise to create effective visualizations.

To overcome these limitations, organizations should invest in proper training for their employees and work with experts in data visualization to create effective and accurate visualizations. By doing so, they can reap the benefits of data visualization while minimizing the potential drawbacks.

Content Summary

• Data visualisation is the representation of data in a visual and graphical format.
• In this article, we will explore the pros and cons of data visualisation, and help you decide whether it is the right approach for your needs.
• The two most important considerations to make when developing a viz are the selection of the data to be shared and the method through which the data will be shared.
• For instance, using Google Trends to visualise and analyse enormous amounts of data relating to the most popular searches or queries helps us better comprehend the data.
• This indicates that the majority of individuals regard reading data visualisations to be a far more intuitive method of comprehending data than any other method.
• Another advantage of data visualisation is that it makes it simple and straightforward to share data.
• A viz pulls everyone onto the same page and makes it simple to communicate if you are utilising a business intelligence platform.
• In addition to this, you capture the interest of people at all levels of study.
• But the tricky part it that you have to pick the right data visualization.
• This can lead to the identification of misleading correlations among the data, which in turn can lead to the formation of incorrect assumptions and conclusions.
• X causes Y Y causes X The pair X and Y have no connection.
• If the data visualisation is honest, it will avoid displaying these deceptive correlations and will only present falsely comparable trends side-by-side when there is a credible explanation or when the person creating the visualisation can explain that there is no correlation between the two.
• Abusing the Law of Large Numbers (LLN) The concept that only very large samples can reliably yield accurate findings is known as the "rule of huge numbers."
• What repercussions does this have on the display of data?
• It indicates that any data visualisation based on a sample size that is too small is subject to the same degree of bias as the sample size itself.
• The fact that the viewer is unable to perceive the sample size is one of the drawbacks of using data visualisation when dealing with sample sizes that are relatively small.
• Four out of the five occasions, the result is a head.
• In conclusion, data visualization is an essential tool for organizations that deal with large amounts of data.
• To overcome these limitations, organizations should invest in proper training for their employees and work with experts in data visualization to create effective and accurate visualizations.