As each organization’s data grows bigger and spreads across multiple data sources containing data sets from a wide range of toolsets, applications, user data.. and on top of all this, data coming from multiple geographic locations + cloud + on-premises – it becomes more difficult for business owners to interpret the data and take confident business decisions. if you would have a tool that would help you consolidate the data from multiple sources and provide a hierarchical views with a layer of abstraction for different roles in your organization .. you are way ahead of the curve!
Let us discuss a few important factors that data visualization tools are solving!
Data Visualization?
Firstly, Data visualization is the process of representing data in a visual format that is easy to understand and analyze. With the vast amount of data generated every day, it has become increasingly important to effectively communicate and present data to facilitate decision making. we will discuss the importance of data visualization and how it can benefit individuals and organizations.
Consider the below table with just 25 rows.. the table consists of following information from a sample organization:
- employee name
- position
- office location
- age
- start date
- salary
Lets say from the above data you want to analyze 2 very simple use cases the below:
- how the employees are spread across different cities so that you can plan your office lease
- the spread of salary across different roles in the organization
it becomes difficult to put a perspective if you would just filter out the salaries and sort them in a table to show the spread… and you would also lose the audience you are presenting the data to if you are bidding that you need to have office space leased in London, New York, San Fransisco if they are just city names displayed on a table of cities with their count.
However, if you represent the same data in the form of a visualization model.. this would add a lot of confidence to your story + the data you are presenting looks very credible.
Now all this with just a data table of 25 rows.. what if your data is spread across millions of rows and thousands of such data tables where you would need to cross reference the data from multiple tables to provide meaningful insights?
You are right.. there is no way you would be able to just represent such complex data in just a consolidated table … therefore difficult to take business decisions.
Key Benefits of Data Visualization
Let us take a look at some of the key benefits of data visualization when you are handling large sets of data.
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Communicate complex data
One of the primary benefits of data visualization is that it can simplify complex data sets. Data visualization can make it easier to identify patterns, trends, and outliers within data that might not be immediately apparent from a table or spreadsheet. By creating clear visualizations, people can better understand complex data, making it more accessible to everyone, regardless of their technical background.
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Identify insights and trends
Data visualization allows people to quickly and easily identify trends and patterns in large data sets. By visualizing data, it is possible to see relationships and correlations that might not have been obvious before. This can lead to insights and opportunities that may have otherwise been missed.
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Improve decision-making
Data visualization can greatly improve decision-making. By presenting data in a visual format, it is easier to identify patterns and make more informed decisions. Visualizations can help to quickly identify trends and relationships, making it easier to spot opportunities and risks. This can be particularly useful for businesses looking to optimize their operations or make strategic decisions based on data.
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Enhance communication
Data visualization can help to enhance communication between different stakeholders. Visualizations are often more effective at communicating complex information than written reports or presentations. They can help to ensure that everyone is on the same page and can lead to more productive discussions around data.
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Increase engagement
Data visualization can increase engagement with data. People are often more likely to engage with visualizations than with raw data. This can be particularly useful for businesses that want to engage with customers or employees around data-driven initiatives.
Choosing The Right Visualization Model
It is also extremely important that you chose the right data visualization model when representing complex data. some of the factors that you would need to consider when choosing the right visualization model are discussed below:
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Data Type
The first factor to consider when selecting a data visualization model is the type of data being presented. There are several types of data, including quantitative, categorical, and temporal data. Quantitative data is numerical and can be measured on a continuous scale, such as height or weight. Categorical data consists of discrete values, such as gender or job title. Temporal data is time-based and can be measured over a period, such as sales data over time. Different visualization models are suited for different types of data. For example, a line chart is suitable for visualizing temporal data, while a bar chart is suitable for visualizing categorical data.
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Purpose
The second factor to consider is the purpose of the visualization. The purpose can range from exploratory analysis to presenting findings to a broader audience. For exploratory analysis, interactive visualizations that allow for easy filtering and exploration of data are recommended. For presentations, static visualizations that highlight key insights are preferred.
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Audience
The third factor to consider when selecting a data visualization model is the intended audience. The visualization should be tailored to the audience’s level of understanding and technical expertise. For example, a highly technical audience may prefer detailed visualizations that contain a lot of information, while a non-technical audience may prefer simpler visualizations that are easier to understand.
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Context
The fourth factor to consider is the context in which the visualization will be used. The context can include the device or medium used for the visualization, such as a desktop computer or mobile device. The context can also include the environment in which the visualization will be presented, such as a boardroom or conference hall. The visualization should be tailored to the context to ensure it is easy to view and understand.
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Design
The final factor to consider is the design of the visualization. The design should be visually appealing and easy to read. It should also be consistent with the overall branding and style of the organization or project. The design should also be optimized for the medium in which it will be presented.