What is data visualization, and why it's important?
The practice of translating converting data into a visual context, such as a map or graph, helps the human brain understand and extract insights from data. The main objective of data visualization is to facilitate the identification of patterns, trends, and outliers in large data sets. The term is frequently used interchangeably with terms like information graphics, data visualization, and statistical graphics.
Data visualization is the data science process that states that data must be visualized after it has been collected, processed, and modeled for conclusions to be drawn.
For similar reasons, visualization is essential in advanced analytics. When a data scientist is developing advanced predictive analytics or machine learning (ML) algorithms, it is critical to visualize the outputs to monitor results and ensure that models are performing as expected. This is because visualizations of complex algorithms are generally easier to understand than numerical outputs.
Why is data visualization important?
Data visualization is a quick and effective way to communicate information to people all over the world by using visual data. The practice can also help businesses determine which factors influence customer behavior, identify areas that need to be improved or given more attention, make data more memorable for stakeholders, understand when and where specific products should be placed, and forecast sales volumes.
The following are some additional advantages of data visualization:
- the ability to absorb information quickly, improve insights, and make decisions more quickly;
- a better understanding of the next steps that need to be taken to improve the organization;
- an improved ability to keep the audience's attention with information that they can understand;
- an easy distribution of information that increases the opportunity for everyone involved to share insights;
- an increased ability to act quickly on findings and, as a result, achieve success with greater speed and fewer errors
Data visualization and big data
The growing popularity of big data and data analysis projects has increased the importance of visualization more than ever. Businesses increasingly use machine learning to collect massive amounts of data that can be difficult and time-consuming to sort through, comprehend, and explain. Visualization provides a way to accelerate this process and present information to business owners and stakeholders in a way that they can understand.
Big data visualization frequently goes beyond traditional visualization techniques such as pie charts, histograms, and corporate graphs. It instead employs more complex visuals, such as heat maps and fever charts. Big data visualization necessitates the use of powerful computer systems to collect raw data, process it, and convert it into graphical representations that humans can use to quickly conclude.
While big data visualization can be beneficial, it can also have a number of drawbacks for businesses. These are their names:
- A visualization specialist must be hired to get the most out of big data visualization tools. This specialist must identify the best data sets and visualization styles to optimize their data use.
- Because big data visualization necessitates powerful computer hardware, efficient storage systems, and even a move to the cloud, IT and management are frequently involved in big data visualization projects.
- Big data visualization can only provide as accurate insights as the data being visualized. As a result, having people and processes in place to govern and control the quality of corporate data, metadata, and data sources is critical.
Common data visualization use case
The following are examples of common data visualization use cases:
Marketing and sales
According to Magna's research, half of all global advertising dollars will be spent online by 2020. As a result, marketing teams must pay close attention to their web traffic sources and the revenue generated by their web properties. Data visualization makes it simple to see traffic trends resulting from marketing efforts over time.
A geographic map that shows which party each state or district voted for is an everyday use of data visualization in politics.
Healthcare professionals frequently use choropleth maps to visualize important health data. A choropleth map depicts geographical areas or regions that have been color-coded in relation to a numeric variable. Professionals can use choropleth maps to see how a variable, such as the mortality rate of heart disease, changes across specific territories.
Scientific visualization, abbreviated as SciVis, enables scientists and researchers to gain more insight from their experimental data than ever before.
Candlestick charts are used as trading tools, assisting finance professionals in analyzing price movements over time and displaying important information such as securities, derivatives, currencies, stocks, bonds, and commodities. Data analysts and finance professionals can detect trends by analyzing how the price has changed over time.
Researchers and data scientists
Data scientists typically create visualizations for their own use or to present information to a small group of people. The visual representations are created using visualization libraries from the programming languages and tools of choice.
Data scientists and researchers frequently use open source programming languages, such as Python or proprietary data analysis tools. These data scientists and researchers use data visualization to understand data sets better and identify patterns and trends that would otherwise go unnoticed.
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