Have you ever gotten advice from someone to visualize your success to manifest it? That’s kind of how data visualization works too. But unlike the power of positive affirmation, data visualization has precise tools, methods, and quantifiable targets which help you grow your business. Since the rise of big data, collecting information on the productivity of internal processes, consumer demand and behavior, and sales and marketing performance has been vital.
This was the advent of data analytics for the purpose of continuous improvement. Analyzing productivity data is essential for manufacturing companies. Analytics serve as insights into how manufacturers can make production processes efficient, cost-effective, and improve the quality of products. The information pool collected to make this possible is enormous. It is columns and rows of numbers that can be overwhelming and challenging to decipher.
To make the high volume of data easy to digest, interpret, and analyze for decision-making, business intelligence developers help visualize it.
This blog will define data visualization in detail, cover the different types of data visualization, and lay out its business benefits.
What is Data Visualization?
Data visualization means giving visual context to extensive information through shapes, colors, and text. It helps define what the numbers portray, so decision-makers can quickly understand and derive practical context from their collected information.
By visually representing data, organizations can identify any patterns, trends, and outliers within the datasets. We can describe data visualization as the most efficient way of presenting the information.
For modern businesses, Industry 4.0 tools like Artificial intelligence and Machine Learning (AI/ML) have useful applications in producing visuals that aid in interpretation. AI/ML solutions enables organizations to build massive datasets quickly by recording several vital aspects of an organization’s functions. AI tools can further process the data into various information graphics such as histograms, pie charts, heat maps, etc.
Another characteristic of data visuals is that they are dynamic and interactive. Thanks to AI/ML applications, these visuals can often automatically update as new data is entered and show changes in real-time, thanks to AI/ML applications.
Interactive visuals are able to display in-depth analysis by clicking on or zooming in on a particular section of the image. By adding these capabilities, business insight developers can make the visual data easy to navigate and make decision-making more convenient for stakeholders.
The simplest examples of data visualization are charts and graphs that MS Excel generates from a particular dataset. These are common and simple to make. However, they do not offer a lot of depth, and large amounts of data are tough to visualize. Advanced data visualization methods for data scientists include R programming that can provide statistical insights and also present massive data pools.
For the purpose of business intelligence, specialized tools like Power BI platform from Microsoft and Tableau come in handy. Both these software are specially designed for business individuals and offer built-in capabilities for manufacturing productivity analysis, mobile app development viewing options, and data cleaning tools.
Power BI developers and Tableau developers provide ready-to-function dashboards that are dynamic and interactive. These dashboards can be customized to suit an organization’s goals and create visuals in the preferred format for stakeholders. Organizations can use these visualization tools to share information with other parties while also keeping security protocols in place.
Types of Data Visualization
Each type of data needs to be presented differently depending on its characteristics. Time-related information is high in volume, data on performance has several variables to consider, and sales data needs to be divided into multiple categories for analyzers to decipher and derive context from them.
We will go over 5 common types of data visualizations that businesses worldwide employ.
This type of graph is best while analyzing information divided into time periods or categories. The bars on a bar graph can be sorted chronologically or in order of importance. The horizontal axis shows the types you choose to include, and the vertical axis indicates the quantity.
Bar graphs are a popular choice for visual representation because they can be broken down into groups of bars to indicate sub-categories within a larger group. This level of insight is often crucial to sales analysts who want to see how a range of products is performing.
Line charts are often seen in stocks and market performance analyses. The reason for that is they clearly display trends (upwards, downwards, or stagnant) and work best with volatile data. Each dot on a line chart represents a particular data point, and therefore a large amount of data can be shown on a simple line chart.
Line charts can be a host to more than one market’s information. Multiple lines can be differentiated by color or pattern, enabling users to summarize the performance on a larger scale. Consolidated data also tells a more detailed story than individually represented data.
Scatter plots are the best option for in-depth research on individually acting units such as countries or people. These charts represent how each individual is performing on a set of two chosen variables. They are also helpful for spotting underlying trends or any sort of error in the data.
However, scatter plots are not a highly-preferred option in the field of data visualization. It is possible for many points of the scatter plot to be clustered nearby, making it harder to identify each individual point, and it is difficult to make them visually appealing.
Heat maps are relatively newer and advanced area maps that actually evolved from line charts. These Heat maps can show the intensity of performance of an individual or a machine over a given metric of time or area.
They are used to measure how well a particular element performs over a day or to study the productivity and work rate of an employee. Heat maps use visual aids like color and natural patterns that are easy to decipher for analysts. Their visually appealing quality makes them an increasingly popular data visualization tool.
Typography is an interesting data visualization option. It represents data in the form of specific keywords by altering their size and color to indicate their performance.
Typography charts are most helpful in gauging consumer perception, SEO performance, and popularity searches. They are a vital tool for marketing research and presentation.
Data analytics is incomplete without visualization for business intelligence. Power BI developers and Tableau developers are regularly ensuring that data visualization can meet the requirements of manufacturers and business owners in accurately depicting information and processing large data sets quickly.
Xavor Corporation empowers businesses to deploy the visualization tool of their choice in their existing infrastructure to analyze better and act on business decisions faster. We have worked with multiple customers helping them shape their raw data in an easily understandable format so that they can turn valuable insights into actions.
Learn more about Xavor’s data analytics and visualization services.