HomeAdvanced Analytics >  Graph Data Processing and Data Visualization

Graph Data Processing and Data Visualization

What is Data Visualization?

Data Visualization is basically a graphical representation of information and data. It is a visual content through which individuals understand the significance of data. There are various data visualizations and data visualization methods or techniques that help individuals to understand the significance of data.
In general, patterns, trends, and correlations may go unnoticed in in text-based form data but through visualizations, with various techniques, it very well may be exposed and perceived easier with different software.
Data Visualization tools and techniques are essential 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 various trends.
However, in today’s world, the various standard charts and graphs are slacked by data visualization tools. Now for displaying data in a more sophisticated manner, 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 questioning.

Talk to a Big Data Analytics Solutions Consultant
Importance of Data Visualization

In Business Intelligence (BI), data visualization is almost a part of it to display information and interaction to data. There is two merchant space in business intelligence, Tableau, and Qlik. They vigorously 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 significant 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 conventional sophisticated Business Intelligence (BI) software. You can also find out about Geospatial Visualization in this insight. You can also investigate more about D3.js in this insight.
In advanced analytics, a data scientist writes advanced predictive analysis and various machine learning algorithms, at that point it is also critical 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 consideration or improvement.
⦁ Help to understand the perfect spot of products.
⦁ Clarify factors impact customer conduct.
⦁ Predict sales volumes.

Examples of Data Visualization

Data 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 that 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 campaigns, 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 monitor data sources and do an analysis of data and detailed advance analytics.

How Data Visualization Works?

Data Visualization tools accompany connectors to various data sources, including most common relational databases like Hadoop and most of the distributed storage platforms.
At that point visualization software collects data from these sources and applies graphic sort to the data. It allows the user to select the way of presenting the data, but software automates this step. Some tools consequently interpret the shape of data and detect correlations among them and place these relations into chart type that software determines the ideal.
For the most part, it has a dashboard component which allows tracking performance of companies or it might be an electronic mail campaign related to marketing after pulling multiple visualizations of analyzes likely which a web portal.

  • Comprehend information rapidly.
  • Identify patterns and relationships.
  • Pinpoint emerging trends.
  • Communicate the story to others.
Types of Data Visualization

General data visualization types are pie charts and bar graphs, however generally these are as indicated by some extent as now these are various types of visualization which is combined with the format of data, i.e, data visualization must be paired with the correct set of data. Using various visualizations we can effectively present data in a more interesting manner.

  • Charts
  • Graphs
  • Tables
  • Maps
  • Dashboards
  • Infographics
Methods to Visualize Data

Here are some methods that can assist you with visualizing your data better for business outcomes.

  • Heat Map
  • Area Chart
  • Bar Chart
  • Fever Chart
  • Bullet Graph
  • Gantt Chart
  • Matrix
  • Histogram
  • Scatter Plot
  • Circle View
  • TreeMap
Interactive Data Visualization

Interactive data visualization supports exploratory reasoning so that decision-makers can effectively investigate intriguing findings. Interactive visualization supports faster decision making, greater data access and stronger user engagement along with desirable results in several different metrics.
Some of the key findings include:

  • 70% of the interactive visualization adopters improve joint effort and information sharing.
  • 64% of the interactive visualization adopters improve user trust in hidden 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 Center (Interactive Data-Driven Campaigns)
  • In extras some different examples of specialized device:
  • Sales Presentations
  • Training Modules
  • Product Collateral
  • Shareholder Presentations
  • Educational Content
  • Press Releases and PR Content
Benefits of Interactive Data Visualization
Identifying Causes and Trends Quickly

Today’s 93% of human correspondence is visual, and it tells that human eyes are processing images 60,000 times more than the text-based data.

Relationships among Tasks and Business Operations

By interacting with data to put the focus on specific metrics, decision-makers are able to compare specific all through determinable timeframes.

Telling Story through Data

By allowing users to interact with data present in an clear visual way, a data-intensive story becomes visible.

Use Cases of Data Visualization
  • History of Bruce Springsteen
  • Apollo
  • Keuzestress
  • Marvel Cinematic Universe
  • The Many Moons of Jupiter
  • Newsmap
  • 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 Techniques
Know Your Audience

Some of the most accomplished entrepreneurs and executives think that 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 Goals

From storytelling directly 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, make rationale and drill down into insights that issue. It is important to set clear aims, objectives, and goals before reports, graphs, charts, and visuals.

Right Chart Types
  • Number Charts
  • Maps
  • Pie Charts
  • Gauge Charts
Exploit Color Theory

Selecting the correct shading scheme for your professional assets will help enhance your efforts significantly. The guideline of color theory will affect the success of your visualization model. You should always try to keep the color scheme more consistent all through data visualizations.

Handle Your Big Data

Discover which data is available to you and your organization and choose which is the most important. Keep your data secured and data dealing with systems simple and updated to make the visualization process straightforward and ensure that you use business dashboards that present your most insights in easy access.

Utilize Word Clouds and Network Diagram

A network diagram is used 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 data.

Use Ordering, Layout, and Hierarchy to Prioritize

When you have categorized your data and separated it to the branches of data that you seem to be most valuable to your organization, you should burrow further, creating a clearly labeled hierarchy of your data and prioritizing it.

Hierarchy, Ordering, and Layout will be in a state of constant development but will make your visualization efforts speedier, simpler, and successful.

Apply Visualization Tools For the Digital Age

An interactive online dashboard or tool offers a digestible, comprehensive, and interactive mean of collecting, arranging, and presenting data effortlessly.
These data visualization ideas served to push your endeavors to new successful heights. To enhance activities, exploring business insight and online data visualization tool will be useful.