Data science is a multi-disciplinary field that has its claws spread over various fields. Even though people think machine learning and its algorithms are the most critical parts of data science, that is not really the case.
When it comes to practical usage, data visualization is as important as any other part of data science could be, especially when it comes to practical use.
As a notable Computer Science Professor, Ben Schneiderman once said:
“Visualization gives you answers to questions you didn’t know you had.”
Data visualization is nothing but merely a name given to presenting information in the form of visuals. When we represent any kind of information in the form of graphs, pie charts, histograms, and so forth, we’re essentially using data visualization; it’s as simple as that.
Data visualization also presents the analytics of data in a manner that is easy to understand. Even the beginners who aren’t aware of data’s technicalities don’t face a hard time grasping the details presented. We can easily summarize long reports with only a few visualization techniques if we use them the right way, of course.
As a famous quote by Edward Tufte goes:
“There is no such thing as information overload. There is only bad design.”
So, it’s upon us how we use data visualization, but if we use them to their full potential, there’s no doubt that the results will be excellent. We reduce efforts; we get efficient; finally, we can successfully transmit the messages we want to, in the most precise way.
What is Data Visualization?
Data visualization is the art of presenting data that captures the viewer’s attention and helps to uncover the smallest details present in the data. It helps to focus on the areas that would have otherwise been lost in other forms of raw data like tables or excel files.
In the world of data science, data visualization is much more than a word. It’s a whole process that provides solutions to a lot of problems we’re facing today. Whether it’s big data that we need to analyze or a presentation we need to make for the stakeholders, data visualization always plays a vital role.
In contrast to text, which has been used as the preferred medium for exchanging information for ages, data visualization provides us a much more interactive approach. It helps us present our ideas without needing words and makes the process much less complicated.
Why is Data Visualization Important in Data Science?
Data visualization is important in data science because it helps us make data ‘speak’ and provide all the hidden details it covers. It also helps perform the exploratory analysis quickly, giving a massive boost to data science projects and effective decision-making.
If we take a peek into human psychology, we come to know that:
“Human brains process visuals 60,000 times faster than they do text.”
Moreover, when it comes to decision-making, managers need to have a thorough understanding of the data available before they can make a conclusive decision. However, since there is a lot of data available, the easiest and the quickest way to have a complete overview of data is by using visualization techniques.
According to an estimate by Tableau,
“Managers in organizations with visual data recovery tools are 28% more likely to find timely information than those who rely on managed reporting and dashboards”.
However, that’s not all there is to data visualization, and there are multiple reasons why we cannot ignore its importance in data science. Further in the article, I’ll explain six more reasons why you should also start using data visualization.
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This section will be looking at more specific reasons why data visualization is a huge asset of data science and its projects.
Easy to Catch Patterns
Studying lots of data and finding the slightest patterns in it is a part of a data scientist’s everyday tasks. They are frequently provided with big data and find effective ways to study the data to find some patterns or trends in it that could help the business in some way.
However, since the data is available in huge quantities nowadays, data scientists cannot simply use old-school ways or tools to study the data. It could potentially take them months to go through the data. So, they use tools to visualize the data in various ways to study it more efficiently.
Not only does this save a lot of their time, but it also helps them get their tasks done without a lot of exertion. There are various big data tools out there that help you analyze big data using different visualization techniques, boosting your efficiency.
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Helps Making Business Decisions
Gone are the days when businesses used to make decisions merely based upon some top-tier personnel’s experiences or instincts.
Nowadays, most businesses are shifting to data-driven decision-making (DDDM), using data while making all their major decisions. However, to study all the overwhelming business data, visualizations are one of the major tools they use.
As we discussed in the previous point, visualizations provide businesses with considerable actionable insight that they could act upon.
Even though data visualization isn’t the only way to perform such analysis on data, it’s the most suitable way since it’s the best to reveal customer trends and the current market conditions.
Moreover, since managers are not professional data analysts and cannot make sense of all the raw business data available, visualization is the best way to view the current scenarios. This makes them able to make the best decision possible while keeping in view all the stats and figures the data is providing.
As a result, more informed business decisions are made that don’t leave anything out of consideration.
Recommended Article: The Role of Data Visualization in E-Commerce
Machine Learning Models
Building custom machine learning models are amongst the most important skills a data scientist should possess. While making models requires you to be proficient in some statistical programming language such as Python & R, it’s not all that it takes.
Coming up with efficient models requires a lot of analysis performed on the dataset as well. Because if you don’t have enough knowledge about the dataset, you could never tweak it according to its features and consequently end up making a model with good accuracy.
Developing machine learning solutions requires you to pinpoint the path you’re going to follow. Data visualization combined with exploratory data analysis is what it takes to help pick that path. Moreover, in more complex steps of developing ML solutions such as hyper-parameter tuning, data visualization plays a critical part and helps data scientists group together the variables that should be focused the most.
Conveying Information to Stakeholders
Stakeholders don’t care how data scientists are going about the model; they just need the solution they’re looking for with high accuracy. However, since they don’t have enough technical skills, how do you think the results are conveyed to them so they can provide reasonable feedback? Well, the answer lies in nothing but data visualization!
Data science projects are iterative processes, and as the data scientists proceed with the steps, it’s important to keep the stakeholders informed with every step. To achieve this, data scientists mostly use data visualization techniques that could present the stakeholders with all the information they need without getting lost in the projects’ technical details.
Data scientists use different performance and accuracy metrics to prove with enough detail how a particular project is moving forward. The visuals also give information about the direction a project is proceeding in and the areas it’s targeting. Such information is exactly what stakeholders are looking for, and there’s no better way than presenting the results in the form of visuals.
A Picture is Worth a Thousand Words
If you want to convey a specific message to someone, using a visualization instead of simple text would always be a more effective approach.
First off, using a visualization would wipe away the possibility of any jargon. Second, unlike the text that needs to be read and is psychologically not as appealing, it’s not the case with a visualization. It’ll require the minimal focus of the reader and is much more appealing as well.
Moreover, visualizations are incredibly effective when it comes to catching someone’s attention, and even a slight glimpse helps a user get the details he needs.
According to a study, 90% of the information transmitted to the brain is visual. Hence, you can see why using visualizations is always a better idea.
Summarizing Results or Presentation
Last but not least, data visualization plays a vital role in keeping stakeholders aware of data science projects and helps the data science team present results within their organizations.
When you talk about a data science project, there are multiple teams involved, and not all of them are skilled in data science. Hence, for the personnel who belong to external pillars of the business, such as project management or human resources, the data science team needs to present them with results in a simplistic manner. This way, they can view the progress without missing any important pieces of information.
Again, this is something visuals are great for. Not only can you present all the details you want within your organization, but you can also make them very simple, and someone who no knowledge of how models work can pick the details up.
Data visualization is simply a way to refer to visual information presented using some specific data. It’s quite effective in contrast to presenting dull and boring numbers that need to be studied carefully if you need to make some meaning out of it.
Data visualization has a whole lot of applications, both within and outside of data science projects. It precisely packs important information within it, and the same information that could’ve spread over multiple pages can be viewed using a single graph. However, this is just a glimpse of how powerful it is.
Moreover, data visualization helps in identifying new patterns and trends all the time. This is why businesses nowadays are looking to hire data analysts skilled in visualizations to help them make business decisions. Since big data is common nowadays, there’s no use manually going through heaps of data, wasting all the human resources to find little trends and patterns when data visualization can manage it single-handedly.
So, it’s high time everyone realizes the importance of data visualization in data science; and start adapting to it and using all the concepts it provides. Moreover, there are hundreds of tools out there that make visualization easier than ever! So, what are you waiting for? Start learning data visualization today!