What is Data Visualization?
Data Visualization is basically a graphical representation of information and data. It is a visual content through which people understand the significance of data. There are various data visualizations and its data visualization methods or techniques which helps people to understand the importance of data. In general, patterns, trends, and correlations might go unnoticed in text-based form data but through visualizations, with various techniques, it can be exposed and recognized easier with different software.
Data Visualization tools and techniques are important to make data-driven decisions and analyze a massive amount of information and data. Using visual elements like graphs, charts, and maps, data visualization tools provide access to understand patterns and different trends. However, in today’s world, the various standard charts and graphs are lagged by data visualization tools. Now for displaying data in a more sophisticated way, infographics, dashboards, geographic maps, sparklines, heatmaps and detailed bar, pie, fever charts go beyond that traditional way for displaying of data. More on now interactive images comes into power and user can manipulate data for analysis and querying.
The scientific visualization to emerge an idea to present data in such a way so that it could be easily determined by anyone. Click to explore about, Visual Analytics and Data Visualization with CanvasJS
Why Data Visualization is important?
In Business Intelligence (BI), data visualization is almost a part of it to display information and interaction to data. There is two vendor space in business intelligence, Tableau, and Qlik.
- They heavily emphasize visualization and almost all Business Intelligence Software has strong data visualization functionality. It also has a strong functionality in Advance Intelligence. These tools have been important in democratizing data and analytics and making data-driven insights for workers in an organization.
- Data Visualization software also has an important role in advanced analytics and big data. These are generally easier to operate than traditional sophisticated Business Intelligence (BI) software. You can also read about Geospatial Visualization in this insight. You can also explore more about D3.js in this insight.
In advanced analytics, a data scientist writes advanced predictive analysis and various machine learning algorithms, then it is also important to visualize the output and monitor the results and ensure that these models are performing as intended and this is because visualization of various complex algorithms is easier to interpret than numerical output. Data Visualization can also:
- Identify areas that need attention or improvement.
- Help to understand the right place of products.
- Clarify factors influence customer behavior.
- Predict sales volumes.
Model Visualization provides reason and logic behind to enable the accountability and transparency on the model. Click to explore about, Machine Learning Model Visualization
Examples of Data VisualizationData Visualization tools in today’s world can be used in various ways. Business Intelligence (BI) reporting tool is one the use. In this, users can set visualization tools to generate automatic dashboards which are used to track company performance and interpret results. However, it is not only limited to track a particular thing like in marketing it is used to monitor the performance of electronic mail campaign, tracking metrics like open rate and conversion rate. It also now increasingly used as a front end in big-data environments. In this, data visualization software helps data engineers and scientists to keep track of data sources and do an analysis of data and detailed advance analytics.
How Data Visualization Works?Data Visualization tools come with connectors to different data sources, including most common relational databases like Hadoop and most of the cloud storage platforms. Then visualization software collects data from these sources and applies graphic type to the data. It allows the user to select the way of presenting the data, but software automates this step. Some tools automatically interpret the shape of data and detect correlations between them and place these relations into chart type that software determines the optimal. Generally, it has a dashboard component which allows tracking performance of companies or it may be an electronic mail campaign related to marketing after pulling multiple visualizations of analyzes likely which a web portal.
- Comprehend information quickly.
- Identify patterns and relationships.
- Pinpoint emerging trends.
- Communicate the story to others.
A method of translating raw data into visual forms like charts, graphs, and maps. Click to explore about, 10 Best Practices for Data Visualization
What are the types of Data Visualization?General data visualization types are pie charts and bar graphs, but typically these are according to some extent as now these are different types of visualization which is paired with the format of data, i.e, data visualization must be paired with a right set of information. As using various different visualizations we can effectively present data in a more interesting way.
What are the various methods to Visualize Data?Here are some methods that can help you to visualize your data in better for business outcomes.
- Heat Map
- Area Chart
- Bar Chart
- Fever Chart
- Bullet Graph
- Gantt Chart
- Scatter Plot
- Circle View
What are the Interactive Data Visualization?Interactive data visualization supports exploratory thinking so that decision-makers can actively investigate intriguing findings. Interactive visualization supports faster decision making, greater data access and stronger user engagement along with desirable results in several other metrics. Some of the key findings include:
- 70% of the interactive visualization adopters improve collaboration and knowledge sharing.
- 64% of the interactive visualization adopters improve user trust in underlying data.
- Interactive Visualization users engage data more frequently.
- Interactive Visualizes are more likely than static visualizers to be satisfied easily with the use of analytical tools.
Examples of Interactive Data Visualization
- MailChimp (Interactive Annual Report)
- The New Yorker (Interactive Visual Content for Media)
- SAP Intouch Wall (Interactive Executive Presentations)
- Bloomberg (Interactive Financial Data)
- The Lowy Institute Poll (Interactive Polling Data)
- Pulizter Centre (Interactive Data-Driven Campaigns)
- Sales Presentations
- Training Modules
- Product Collateral
- Shareholder Presentations
- Educational Content
- Press Releases and PR Content
Visuals are especially helpful when you’re trying to find relationships among hundreds or thousands of variables to determine their relative importance. Click to explore about, Data Visualization with React and GraphQL
What are the benefits of Interactive Data Visualization?
The benefits of Interactive Data Visualization are listed below:
Identifying Causes and Trends QuicklyToday’s 93% of human communication is visual, and it tells that human eyes are processing images 60,000 times more than the text-based data.
Relationships Between Tasks and Business OperationsBy interacting with data to put the focus on specific metrics, decision-makers are able to compare specific throughout definable timeframes.
Telling Story Through DataBy allowing users to interact with data present in a clear visual manner, a data-intensive story becomes visible.
Use Cases of Data Visualization
- History of Bruce Springsteen
- Marvel Cinematic Universe
- The Many Moons of Jupiter
- The Big Mac Index
- CF Weather Charts
- Galaxy of Covers
- Red Bull Party Visualization
- Figures in the Sky
- The Women of Data Viz
Data visualization is the control of pursuing to appreciate information by setting it in a visual setting with the goal. Click to explore about, Real-Time Streaming Data Visualizations
What are the best Data Visualization Techniques?
The best Data Visualization Techniques are described below:
Know Your AudienceSome of the most accomplished entrepreneurs and executives find it difficult to digest more than a bar chart, pie chart, or neatly presented visual, also have no time to deep into data. Therefore, ensuring that your content is inspiring to your audience is one the essential data visualization technique.
Set Your GoalsFrom storytelling right through to digital selling and beyond with the visualization of your data, your efforts are as effective as the strategy behind them. To structure your visualization efforts, create logic and drill down into insights that matter. It is important to set clear aims, objectives, and goals prior to reports, graphs, charts, and visuals. Right Chart Types
- Number Charts
- Pie Charts
- Gauge Charts
Take Advantage of Color TheorySelecting the right color scheme for your professional assets will help enhance your efforts significantly. The principle of color theory will have an impact on the success of your visualization model. You should always try to keep the color scheme more consistent throughout data visualizations.
Handle Your Big DataDiscover which data is available to you and your organization and decide which is the most valuable. Keep your data protected and data handling systems simple and updated to make the visualization process straightforward and ensure that you use business dashboards which present your most insights in easy access.
Utilize Word Clouds and Network DiagramA network diagram is utilized to draw a graphical chart of a network. This style of layout is useful for network engineers, designers, and data analysts while compiling network documentation. Word clouds give a digestible means of presenting complex sets of unstructured information.
Use Ordering, Layout, and Hierarchy to PrioritizeOnce you have categorized your data and broken it down to the branches of information that you seem to be most valuable to your organization, you should dig deeper, creating a clearly labeled hierarchy of your data and prioritizing it. Hierarchy, Ordering, and Layout will be in a state of constant evolution but will make your visualization efforts speedier, simpler, and successful.
Apply Visualization Tools For the Digital AgeInteractive online dashboard or tool offers a digestible, comprehensive, and interactive mean of collecting, arranging, and presenting data with ease. These data visualization concepts served to help your efforts to new successful heights. To enhance activities, exploring business intelligence and online data visualization tool will be useful.
What are the modules of Data Visualization?
The modules of Data Visualization are listed below:
Visual LiteracyIn Visual Literacy, there are some basic types which are used to interpret information and through visual literacy, we will build visualizations.
Visual AnalysisIn Visual Analysis, we can compare, relate and analyze the data through different kinds of visualizations and also we will use some basic analytical visualizations.
Visualizations for BusinessIn visualizations for business, the context for your visualizations is established in business communications.
- Analytic Reports
Visualizations of TomorrowIn visualizations of tomorrow, some complex examples of visualization are there where visualization will use tomorrow as the advancement of technology.
Data Visualization ToolsTools for Non-Developers
- High Charts
- Fusion Charts
Holistic Approach to Data VisualizationData visualizations are being used to discover hidden facts and trends. You can easily see and explore visualizations in the form of line charts to represent change over time. Bar and column charts are helpful when inspecting relationships and making comparisons. Good data visualizations are created when communication, data science, and good design strike. To adopt data visualization, you are recommended to have a look at below steps:
- Learn more about Data Visualization Techniques
- Explore what is 3D Data Visualization in this blog
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