数据可视化：数据可视化是以图形或图形格式(例如：图表、图形和地图)的信息和数据的图形表示。数据可视化工具提供了一种查看和理解趋势、数据模式和异常值的可访问方式。数据可视化工具和技术对于分析大量信息和做出数据驱动的决策至关重要。使用图片的概念是了解数据已经使用了几个世纪。数据可视化的一般类型是图表、表格、图形、地图、仪表板。
数据分析：数据分析是分析数据集以便对它们拥有的信息做出决策的过程，越来越多地使用专门的软件和系统。数据分析技术用于商业行业，使组织能够做出业务决策。数据可以帮助企业更好地了解他们的客户、改进他们的广告活动、个性化他们的内容并提高他们的底线。数据分析的技术和过程已经自动化成机械过程和算法，可以处理原始数据供人类消费。数据分析可帮助企业优化其绩效。
下表列出了数据可视化和数据分析之间的差异：
| Based on | Data Visualization | Data Analytics | |||
|---|---|---|---|---|---|
| Defination | Data visualization is the graphical representation of information and data in a pictorial or graphical format. | Data analytics is the process of analyzing data sets in order to make decision about the information they have, increasingly with specialized software and system. | |||
| Benefits | Identify areas that need attention or improvementClarity which factors influence customer behaviorHelps understand which products to places wherePredict sales volumes | Identify the underlying models and patternsActs as an input source for the Data Visualization,Helps in improving the business by predicting the needs Conclusion | |||
| Used for | The goal of the data visualization is to communicate information clearly and efficiently to users by presenting them visually. | Every business collects data; data analytics will help the business to make more-informed business decisions by analyzing the data. | |||
| Relation | Data visualization helps to get better perception. | Together Data visualization and analytics will draw the conclusions about the datasets. In few scenarios, it might act as a source for visualization. | |||
| Industries | Data Visualization technologies and techniques are widely used in Finance, Banking, Healthcare, Retailing etc | Data Analytics technologies and techniques are widely used in Commercial, Finance, Healthcare, Crime detection, Travel agencies etc | |||
| Tools | Plotly, DataHero, Tableau, Dygraphs, QlikView, ZingCHhart etc. | Trifecta, Excel /Spreadsheet, Hive, Polybase, Presto, Trifecta, Excel /Spreadsheet, Clear Analytics, SAP Business Intelligence, etc. | Platforms | Big data processing, Service management dashboards, Analysis and design. | Big data processing, Data mining, Analysis and design |
| Techniques | Data visualization can be static or interactive. | Data Analytics can be Prescriptive analytics, Predictive analytics. | |||
| Performed by | Data Engineers | Data Analysts |