The Data Visualization Revolution:
How to transform data into insights
In our modern era, an overwhelming amount of data is being produced. Each day, we generate 2.5 quintillion bytes of data. However, this data is only valuable if we can comprehend it. By utilizing data visualization techniques, we can interpret and analyze data to identify patterns and trends that may otherwise go unnoticed.
What exactly is data visualization and what is its significance?
Well, from the name itself, you can infer that it has to do something with being able to look at the data in some graphical format and guess what? You are not quite wrong. The major part of it is definitely being able to look at the data in a graphic manner such that it makes more sense, but it has a bit more to it.
The amount of data that is produced every day is practically insane. Data Visualization provides us with a medium to put this huge amount of data to some use. With DV, as it is called in the professional world, one can get a general idea of the day by looking at the charts, graphs, etc based on the data. Just imagine going through tons and tons of data manually to find some usefulness or patterns that can be used. It’s basically impossible to do that. There is no feasibility in putting any amount of manpower to go through such a humungous amount of data and try to make some sense out of it. Even if all the brilliant statistical minds are put in a huge room and made to go through this data, the amount of output one might obtain will be ridiculously embarrassing compared to the amount of time and manpower that was put into it.
Ironically, somebody with a good data visualization tool will be able to obtain a thousand times more useful and sensible insights out of that data than the type of setting we discussed above. Definitely, you need a good computing resource that can process such large data and you also need some good set of algorithms that actually filter this data, clean it, categorize it and puts it forward to us.
So to define data visualization with respect to all the aspects discussed above, one can say:
“Data visualization is nothing but making use of powerful computing resources to filter, clean and eventually analyze huge amounts of data, preferably in a graphical format, such that the information acquired from these insights can be put to use to develop new services and technologies which otherwise would go unnoticed.”
The Importance of Data Visualization
The importance of DV is pretty much self-explanatory. It definitely overpowers any human in data processing and presentation. But we are not only gonna discuss its advantages over manpower, we are here to talk money.
Today mostly all industries operate on data. Whether it is for maintenance, optimization, development of new services, recognizing patterns and trends, employee management, recommendations and so on. You just name it, every sector is directly or indirectly backed by some data which is used for further development. A very fine example of this could be an Ads company. For any Ads company, the data about their customers is the company’s heart and soul. Without it, these companies will face an existential crisis. Let’s discuss various fields in which data visualization is playing a huge role in transforming that particular sector:
1. Importance of Data Visualization in Healthcare:
Healthcare is an industry that relies heavily on data. From patient medical records to insurance claims, there is a lot of data that needs to be collected, analyzed, and interpreted. Data visualization plays an important role in healthcare by allowing doctors and other medical professionals to make better-informed decisions.
For example, let’s say a hospital wanted to reduce the number of patient readmissions. They could use data visualization to look at readmission rates over time, identify which types of patients are more likely to be readmitted and develop interventions to target those high-risk patients.
2. Importance of Data Visualization in Business Intelligence:
Business intelligence (BI) is the process of transforming raw data into actionable insights. Data visualization plays an important role in BI because it allows businesses to see their data in a new light and make better-informed decisions about their overall strategy. For example, let’s say a company wanted to increase sales by 10% this year.
They could use data visualization to track sales over time, identify which products are selling well and which ones are not, and develop marketing campaigns and targeted promotions accordingly.
3. Importance of Data Visualization in Machine Learning:
Machine learning is a subfield of artificial intelligence that deals with the design and development of algorithms that can learn from data. Data visualizations are important in machine learning because they can be used to understand complex datasets and identify patterns. Machine learning algorithms can then be developed to automatically detect these patterns.
For example, Google’s PageSpeed Insights tool uses machine learning to automatically analyze website performances and provides recommendations on how to improve them.
In fact, if we really talk about the use cases, I would suggest you do an experiment that involves tracking your daily expenses in a Microsoft Excel file and after a few months, click on the ‘explore data’ button somewhere in the Excel and you will get some interesting insights out of that as well.
Anyway, looking at pictures and graphics is always more appealing to us than looking at boring numbers.
Only Data Visualization could be misleading
Apart from being very versatile when it comes to use cases, data visualization is incomplete without its text. What does that mean? It simply means that looking at data visualization, say bar graphs, line graphs, pie charts, plots etc is as useless as reading raw data if you don’t have any proper context about these graphics, and their data source and not knowing what the experts in the respective field have to say about it. So definitely the graphics complement the data, but it is also the other way around. We need both.
Here’s a really good example of what I actually mean. Apple Inc. knows about this fact of data visualization and they use it in their favour a lot of times in their product launches and new feature launch demonstrations. You will always find them showing a line graph here and there comparing let us say their new graphics performance in MacBooks with other laptops in the market. You will see them displaying two line graphs being compared with each other with the graph of the new MacBook slightly being incremental than the other, making us believe that the performance is really increased, while they never disclose any labels of that data, no numeric values, not even anything about what other laptops they are comparing with. They just smartly say “Other leading laptops in the market”. Now the definition of a ‘leading laptop’ could be subjective to everyone. But Apple Inc. smartly uses this benefit of the doubt to make their sales higher and higher every year as we as customers fall into this trap because of lack of knowledge.
That’s why graphics without context could sometimes be very misleading, and when encountering such data visualization without any proper context, we must always take everything with a grain of salt.
The top Data Visualization tools available in market
Now that you have known about data visualization and its powers you might want to try it out yourself. Let’s discuss a few of the top data visualization tools that are extensively used in industries.
- Microsoft Power BI:
Microsoft Power BI is a business intelligence (BI) platform that includes data visualization tools. Power BI is easy to use and can connect to a variety of data sources. It is a good choice for businesses that want to create interactive dashboards and visualizations that can be shared with others.
2. Tableau:
Tableau is a popular data visualization tool that is known for its interactive dashboards and charts. It is easy to use and can handle large data sets. Tableau is a good choice for businesses that want to create visually appealing and informative data visualizations.
3. Qlik Sense
Qlik Sense is another popular data visualization tool that is known for its speed and scalability. It can connect to a variety of data sources and create interactive dashboards and visualizations. Qlik Sense is a good choice for businesses that need to analyze large data sets quickly and easily.
4. Google Charts
Google Charts is a free data visualization tool that is easy to use and can be embedded in web pages. It is a good choice for businesses that want to create simple and effective data visualizations that can be shared online.
5. Fusion Charts
FusionCharts is a commercial data visualization tool that offers a wide variety of charts and graphs. It is easy to use and can connect to a variety of data sources. FusionCharts is a good choice for businesses that need to create high-quality data visualizations that can be used in presentations and reports.
A brief comparison of all:
Microsoft Power BI and Tableau are my personal favourites as both offer a large variety of charts. Power BI has more than enough features for an average user in its free tier while Tableau definitely creates more beautiful dashboards.
Conclusion
In today’s world, data is being generated at an unprecedented rate. This data can be used to gain insights into a variety of areas, but it can be difficult to make sense of it without the help of data visualization. Data visualization is the process of transforming data into graphical representations that make it easier to understand. This can be used to identify patterns, trends, and outliers in data, and to make better decisions. However, it is important to note that data visualization is not without its limitations. For example, data visualizations can be misleading if they are not properly designed or interpreted. It is important to always consider the context of data visualization and to consult with experts before making any decisions based on it. Overall, data visualization is a powerful tool that can be used to gain insights from data. However, it is important to use it responsibly and to be aware of its limitations.
References
Unwin, A. (2020). Why Is Data Visualization Important? What Is Important in Data Visualization? Harvard Data Science Review, 2(1).
https://doi.org/10.1162/99608f92.8ae4d525
https://www2.deloitte.com/nl/nl/pages/tax/articles/bps-the-five-benefits-of-data-visualization.html https://uxmisfit.com/2020/11/12/learn-from-apples-art-of-tech-explanation/
