什么是数据可视化？

Data visualization is the practice of arranging data sets into static or dynamic graphics that highlight the complex relationships between the data presented. Because visual representation is highly intuitive, information presented this way is easier to understand, and reaching actionable insights is faster.

Data is a valuable asset within the business context which can benefit every team in a business, and not just those directly associated with data collecting, generation, and processing. Top level leadership uses data visualization to understand broad forces acting on the business, and to understand quickly which strategic issues need to be prioritized. Frontline teams may use different aspects of data generated from the same sources but data and visualizations are chosen to help them understand more efficient operations.

In the IT context, data visualizations are attached to some aspect of automated data gathering and representation, which adds real-time representations for immediate business use through dashboards.

Dashboards rely on common archetypes for data visualizations which are easily read and almost universally used. Some of these common chart types:

• Standard Graphs — The most common data visualization representations include: line graphs, pie charts, bar charts, scatter plots, area charts, and histograms. However, these are somewhat limited and other charts have been devised to help represent more complex data sets.
• Infographics — Infographics present information graphics, and differ from charts because information does not need to remain numerical. Information can include other diagrams, such as maps, or flow charts.
• Bubble Cloud Charts — Bubble cloud charts are scatter plots with a third variable included. The independent and dependent variables are plotted on an X/Y axis, while the third variable is represented as a circle (or bubble), with a size indicating its value and the plot point at its center.
• Bullet Graphs — Bullet graphs represent bar graphs as something like how a thermometer presents temperature gradients. Essentially, bullet graphics are a type of skeuomorphism, a way to retain familiar designs in new forms, like representing the physical radio dials as on screen dials to aid in quick understanding and adoption.
• Heat Maps — Heat maps represent magnitude on a geographic map, or in abstract, as a two dimensional color spectrum. Sometimes other variables can be introduced, such as a heat map divided into squares of different sizes that represent one variable, while another independent variable is represented as the ‘heat’ color.
• Fever Charts — Fever charts are exceptionally useful, and common, because they represent a single variable over time. These charts are also referred to as time series

Nearly any form of data can be used in data visualization. The selection rest on the importance of data to its intended use, and the ease of accessing that data for analysis. Within the context of data storage, both unstructured and structured data can be used as a base for data visualizations. What the data represents also varies enormously, including very structured data say from sensors and devices, to data requiring greater qualitative analysis techniques such as social media and pictures. In general terms, data visualizations can be categorized based on their functional use. The table below highlights the various categories of data visualization, their intended use and the range of data charts, maps, plots and diagrams used to represent a range of data types.

Data visualization tools are commonly used today as business intelligence (BI) reporting tools. These tools use automation to collect and process data and then create dashboards that represent company key performance indicators (KPIs) as visuals.

Two qualifications are required to be considered a data visualization tool:

• Collect and consumer data from multiple sources, file uploads, database queries, and APIs
• Provide visual representation of key performance indicators

Tools can come as proprietary, with enhanced features, and a support team to help troubleshoot. Popular names in data visualization and big data tools include Microsoft, SAP, IBM, and SAS. For those teams with different needs, open source data visualizations tools exist too, like Redash, D3.js, and Google Charts.