Build data intelligence through a presentation layer and visualization capabilities
Data visualization is the process of creating visual representations of data in order to communicate information clearly and effectively. It is an important tool for understanding and interpreting data, and it is widely used in fields such as business, science, and engineering.
There are several key steps involved in implementing data visualization:
Identify the purpose and audience: The first step in implementing data visualization is to identify the purpose and audience of the visualization. This will help determine the most appropriate visual representation of the data and the level of detail needed.
Collect and prepare the data: The next step is to collect and prepare the data for visualization. This may involve cleaning and formatting the data, as well as selecting the relevant data points.
Choose an appropriate visualization type: There are many different types of visualizations, including bar charts, line graphs, scatter plots, and maps. It is important to choose the most appropriate visualization type based on the purpose and audience of the visualization, as well as the characteristics of the data.
Design the visualization: After the visualization type has been chosen, the next step is to design the visualization. This may involve choosing the layout, color scheme, and other design elements to effectively communicate the data.
Create the visualization: After the visualization has been designed, the next step is to create it using a tool such as a spreadsheet program or visualization software.
Evaluate and revise the visualization: It is important to evaluate the visualization to ensure that it effectively communicates the data and meets the needs of the audience. If necessary, the visualization can be revised to improve its effectiveness.
Implementing data visualization requires a careful and structured approach that involves identifying the purpose and audience, collecting and preparing the data, choosing an appropriate visualization type, designing the visualization, creating it, and evaluating and revising it as needed.
Several data visualization vendors offered robust platforms with unique features and capabilities. Keep in mind that the landscape of data visualization tools is constantly evolving, with new vendors and features emerging regularly. Here are some of the top data visualization vendors and their key features:
Tableau:
Key Features:
Intuitive drag-and-drop interface.
Support for various data sources and connectors.
Real-time data analysis and integration.
Interactive dashboards and storytelling.
Strong community and user support.
Microsoft Power BI:
Key Features:
Integration with Microsoft's ecosystem, including Excel and Azure.
Rich visualizations and customization options.
Natural language query (Q&A) capabilities.
Advanced analytics and machine learning integration.
Mobile and cloud deployment options.
QlikView/Qlik Sense:
Key Features:
Associative data model for exploring data relationships.
Drag-and-drop data loading and visualization.
Self-service data exploration and discovery.
Scalability for enterprise-level applications.
Collaboration and sharing features.
Domo:
Key Features:
Cloud-based platform for business intelligence and data visualization.
Pre-built connectors to various data sources.
Interactive, real-time dashboards.
Collaboration and alerts for data-driven decision-making.
Mobile access and app marketplace.
Looker (Google Cloud):
Key Features:
Semantic modeling for data exploration.
Data source connectivity and data transformations.
Embedded analytics for applications and websites.
Collaboration and data sharing.
Integration with Google Cloud services.
Sisense:
Key Features:
In-chip analytics for fast data processing.
Single-stack architecture for data preparation and visualization.
AI-driven insights and recommendations.
Integration with a wide range of data sources.
Scalable and adaptable for large datasets.
TIBCO Spotfire:
Key Features:
Interactive and advanced analytics.
Predictive and geospatial analytics.
Support for R and Python for data science integration.
Real-time data streaming and analysis.
Integration with TIBCO's broader analytics and integration ecosystem.
MicroStrategy:
Key Features:
Enterprise-level analytics and mobile capabilities.
Data discovery, data blending, and data wrangling.
Embedded analytics for applications.
Scalability for large user bases.
AI-driven insights and recommendations.
Highcharts:
Key Features:
JavaScript-based charting library for web and mobile applications.
Interactive and responsive charts.
Compatibility with various data formats.
Easy-to-use API for developers.
Extensive customization and extensibility.
Dundas BI:
Key Features:
Embedded BI and data visualization.
Customizable dashboards and reports.
Data connectors and transformations.
Self-service analytics for end-users.
Integration with other applications and data sources.
It's important to evaluate these tools based on your specific needs, such as data sources, visualization requirements, user expertise, and budget. Additionally, the data visualization landscape continues to evolve, with new vendors and innovations emerging, so staying informed about the latest offerings is valuable for making informed decisions.
Sash Barige
Jun/23/2019
Photo Credit: Unsplash.com
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