The earliest form of data visualization can be traced back the Egyptians in the pre-17th century, largely used to assist in navigation. There are a number of ways to represent different types of data, and it’s important to remember that it is a skillset that should extend beyond your core analytics team. Alternatively, they may utilize a graph structure to illustrate relationships between entities in a knowledge graph. For example, while text mining an analyst may use a word cloud to to capture key concepts, trends, and hidden relationships within this unstructured data. However, data visualization isn’t limited to performance dashboards. Teams that manage reporting systems typically leverage defined template views to monitor performance. Data visualizationĭata visualization is a critical step in the data science process, helping teams and individuals convey data more effectively to colleagues and decision makers. While visual discovery helps data analysts, data scientists, and other data professionals identify patterns and trends within a dataset, every day data viz supports the subsequent storytelling after a new insight has been found. Visual discovery and every day data viz are more closely aligned with data teams. Data modeling also uses abstraction to represent and better understand data flow within an enterprise’s information system, making it easier for developers, business analysts, data architects, and others to understand the relationships in a database or data warehouse. Project managers frequently use Gantt charts and waterfall charts to illustrate workflows.
It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence, but it can also be used to represent organization structures or processes, facilitating communication between the right individuals for specific tasks. Idea illustrationĭata visualization for idea illustration assists in conveying an idea, such as a tactic or process. While these visualizations are usually unpolished and unrefined, they help set the foundation within the project to ensure that the team is aligned on the problem that they’re looking to address for key stakeholders.
They are frequently leveraged during brainstorming or Design Thinking sessions at the start of a project by supporting the collection of different perspectives and highlighting the common concerns of the collective. We’ll delve deeper into these below: Idea generationĭata visualization is commonly used to spur idea generation across teams. Harvard Business Review (link resides outside IBM) categorizes data visualization into four key purposes: idea generation, idea illustration, visual discovery, and everyday dataviz. Management also leverages it to convey organizational structure and hierarchy while data analysts and data scientists use it to discover and explain patterns and trends. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.ĭata visualization can be utilized for a variety of purposes, and it’s important to note that is not only reserved for use by data teams. Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations.
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