In the world of data, information is power. The ability to take data and transform it into a visual representation that is easy to understand can give you a powerful edge over your competition. In this guide, we will teach you everything you need to know about data visualization. We will discuss what data visualization is, the different types of visuals you can use, how to choose the right type of data visualization for your needs, and how to create effective visuals that communicate your data-driven insights clearly and effectively. And with the recent change by Google from Universal Analytics to Google Analytics 4 (GA4), the rise of understanding how to use a good data visualization tool has increased in importance.
What is data visualization
Data visualization is the process of transforming data into charts and graphs that help to make complex information more easily understood and acted upon. While data visualization can take many different forms, such as charts, graphs, maps, infographics, and diagrams, data visualizations are typically designed to convey a specific message or story clearly and compellingly. Visualization permits true data exploration as well. A good data analyst or data scientist will be able to review data and find connections, correlations, and potential insights.
Through data visualization tools and techniques, data can be presented in a more intuitive way that makes it easier for people to analyze, interpret, and act on information. Expert users can craft stories about events. For instance, Charles Minard mapped Napoleon's invasion of Moscow with an amazingly accurate graph. The map represented the army and the route of the Napoleonic retreat from Moscow and ties that information into the temperature and timescale for a more comprehensive picture of the events.
Whether you are a data scientist looking for new ways to present data insights to your team, or an entrepreneur looking to communicate the key elements of your business model to potential investors, data visualization is an invaluable tool that can help you promote a greater understanding of your data.
Why data visualization is important
Data visualization is important because it helps us to make sense of data. In a world where data is increasingly becoming ubiquitous, the ability to take data and transform it into something easy to understand and act upon is more important than ever before. Data visualization allows us to see relationships, patterns, and trends in key performance indicators that would otherwise be hidden in quantitative data that is presented in a more traditional format, such as a spreadsheet.
Data visualization is also important because it can help us to communicate data-driven insights to others in a way that is easy for them to understand. When data is presented in a visual format, it can be easier for people to see the story that the data is telling, and to understand the implications of that story.
In many cases, data visualization can help us to communicate data-driven insights more effectively than if we were to simply present the data in a tabular format. Oftentimes it is said that a picture tells a thousand words, data visualization creates that picture. When done well it can create the "Aha moment" for business teams, investors, and analysts. It can shape the operational direction of a business.
Data Visualization is a Key Tool for a Data Driven Organization
A data-driven organization makes decisions based on data. While all organizations rely on data to some extent, many are unable to comprehend the full scope of their business because there isn't enough information. A data-driven organization can better comprehend company drivers through a robust data discovery and visual discovery process aided by strong data engineering practices.
Data visualization helps data driven organizations by providing a way to see patterns and trends in data. This can help businesses identify opportunities and make better decisions. Data visualization also helps businesses communicate their data more effectively.
By using visuals, businesses can tell a story with their data and make complex data more understandable. In data driven organizations, data visualization is an essential tool for making better decisions and communicating data more effectively.
The History of Data Visualization
Data visualization has a long and varied history, dating back to ancient mathematical diagrams like those found in the ancient Sanskrit treatises. Over time, new tools and technologies allowed scientists and researchers to visualize their data in new and innovative ways, helping them gain valuable new insights into the patterns and trends hidden within their data sets. Today, new advances in digital visualization allow us to create stunning visual representations of our data that can help us make sense of what might otherwise seem like an overwhelming amount of information. Whether analyzing weather patterns or anticipating customer behavior, data visualization provides opportunities for discovery and new insights.
Microsoft Excel Is Not Enough?
Depending on your age, you may remember Lotus 1-2-3 or Quattro Pro, two early spreadsheet applications that were popular in the 1980s and 1990s. These programs allowed users to enter data into cells in a grid, and then to create basic charts and graphs to visualize that data. While these early visualization tools were helpful, they were limited in their ability to create anything other than the most basic visuals.
Microsoft Excel, the most popular spreadsheet application today, has taken data visualization to a new level with its wide array of built-in charting and graphing capabilities. However, even Excel has its limitations when it comes to visual analytics.
Without question, Excel is a great tool for data analysis, but it has its limitations when it comes to data visualization. Excel, like its predecessors, was designed as a spreadsheet application, and while it has some data visualization capabilities, it is not an ideal tool for creating complex data visualizations. Additionally, Excel is not well suited for creating interactive data visualizations that can be explored and interacted with by the user.
Complex Data
The emergence of the internet e-commerce, social graphs, and broader adoption of non-relational databases created an opportunity for a new breed of focused data visualization services. In many cases, data sets were too large or too complex to be effectively represented in a spreadsheet. For these reasons, data visualization experts often use specialized data visualization tools that are designed specifically for creating visual representations of data.
With the introduction of new data visualization platforms, such as Tableau and Qlikview, data analysts and data scientists could more deeply explore their data. These tools allowed users to create more sophisticated visuals, and to interact with data in ways that simply was not possible with a spreadsheet.
Along with Tableau and Qlik, application-specific appliances for data warehousing became wildly popular even in the face of the rising adoption of cloud computing. For instance, Teradata became a nearly $1 billion revenue business selling a data warehouse that helped businesses collect, store, process and analyze data. Teradata was used to create data visualizations that helped businesses understand their data and make better decisions. In time, technologies built to manage massive amounts of internet data became popular.
Technologies such as Hadoop an open-source software framework allowed for the distributed processing of large data sets across clusters of commodity servers. Hadoop was designed to handle data from web applications, and it has become a popular tool for data scientists and data analysts who need to work with large data sets.
Data Visualization is more than just a tool for Big Data
Data visualization is much more than just big data; it is an essential tool for collecting, exploring, analyzing, and interpreting complex data sets. Whether working with millions of records or just a few thousand, being able to visualize data clearly and concisely can be the key to finding insightful trends or identifying potential points of failure. For this reason, it is important to have a strong tool in your arsenal, regardless of the size or complexity of your dataset.
With its intuitive interface and flexible customization options, a good data visualization tool can help you explore your data in new ways and extract deeper meaning from your findings. Some tools even include machine learning capabilities that can automatically uncover patterns and generate predictive models based on big data sets. And by enabling real-time collaboration between team members, these tools also make it easier to work together on complex projects or big data challenges.
Overall, whether you are working with big data or small, having an effective way to visualize your data can be essential for gaining valuable insights and improving decision-making across all aspects of your organization.
What are some common data visualization techniques?
There are many different data visualization methods that you can use to represent data. Some of the most common methods include charts, graphs, maps, infographics, and diagrams. The best data visualization method for your needs will depend on the type of data you have, the story you want to tell with your data, and your audience. In general, data visualization methods can be divided into two main categories: static and interactive.
Static visualizations are those that are not designed to be interacted with, such as a bar chart or line graph. Interactive data visualizations, on the other hand, are those that allow users to manipulate the data in some way, such as by filtering data points or changing the data visualization type.
Many different techniques can be used to represent data. Some common techniques include:
- Bar charts
- Line graphs
- Scatter plots
- Pie charts
- Histograms
- Heat Maps
Each of these techniques has its strengths and is better suited for certain types of data than others. For instance, bar charts are typically used to compare categorical data points, while line graphs are better suited for data that is temporal data values. Scatter plots are often used to visualize the relationship between two numerical data sets, while pie charts are typically used to represent data that is proportions. Histograms can be used to show visual representations for the distribution of data, while heat maps can be used to show the relationship between three data sets.
Visualization Methods and Storytelling
While there are many different data visualization methods available, not all of them are equally effective at communicating data-driven insights. When choosing a method, it is important to consider the following:
- The type of data you have: Some visualization methods are better suited for certain types of data than others. For example, line graphs are typically used to visualize data that changes over time, while bar charts are better suited for data that can be divided into categories.
- The story you want to tell: The method you choose should be based on the story you want to tell with your data. For example, if you want to show the relationship between two variables, a scatter plot might be a good data visualization method to use.
- Your audience: The data visualization method you choose should be based on your audience. For example, if you are presenting data to a non-technical audience, you might want to use an infographic or diagram instead of a more complex data visualization method like a heat map.
While there are many different methods available, the best way to learn which data visualization method is right for you is to experiment with different methods and see what works best for your data and your audience. The most important thing is to communicate your data-driven insights in a way that is easy for people to understand.
Most good tools will provide these techniques out of the box. However, some tools may also offer more advanced data visualization techniques that can be used to represent data in more creative ways.
Some common advanced data visualization techniques include:
- Sankey diagrams
- Choropleth maps
- Word clouds
- Tree maps
- Spiral graphs
Sankey diagrams are often used to visualize flows of energy or data, while choropleth maps are used to color-code data by geographic region. Word clouds can be used to show the most common words in a data set, while tree maps can be used to show hierarchical data structure. Spiral graphs can be used to visualize data that is cyclical.
While these techniques are not necessarily appropriate for all data sets, they can be very effective when used appropriately. When choosing a data visualization technique, it is important to consider the type of data you are working with and the message you want to communicate with your data visualization.
What are some common data visualization tools?
There are many different data visualization tools available on the market. In fact, there are so many we have lost count. Some common tools include:
- Tableau (acquired by Salesforce.com)
- SiSense
- QlikView
- Domo
- Microsoft Power BI
- IBM Watson Analytics
- Looker (acquired by Google)
- D3.js
- Google Data Studio
Comparing the Most Popular Tools
Each of these tools has its own strengths.
- Tableau is a very popular platform that has many features and capabilities that make it an excellent choice for businesses of all sizes. One of the key strengths of Tableau is its ability to connect to a wide range of data sources, including databases, spreadsheets, and cloud-based data warehouses. This flexibility makes it easy to integrate Tableau into existing business intelligence infrastructure. Another key strength is Tableau's visual interface, which makes it easy to create interactive dashboards and reports. The drag-and-drop interface is simple to use and requires no programming knowledge, making it ideal for users who are not technical experts. In addition, Tableau's advanced features allow users to perform complex analysis and create sophisticated visualizations. As a result, Tableau is an extremely powerful tool that can help businesses gain insights into their data.
- QlikView is another popular data visualization tool that is used by many organizations. There are many different strengths to using QlikView as a data analysis tool. Perhaps the most significant of these is its ability to handle large and complex datasets with ease. With QlikView, users can easily navigate through huge volumes of data, filtering and visualizing aspects that are relevant to their particular research or project. Additionally, because QlikView operates in real-time on the cloud, it is well suited for monitoring systems that need to provide up-to-date information about changing trends or metrics. Finally, because of its intuitive interface and flexible functionality, Qlikview is easy for users of all levels to learn and use effectively. Similar to Tableau it can be a great tool for a broad array of users from data scientists looking for an advanced tool or a business professionals looking for a simple way to gain insights from their data.
- There are several key strengths to using Microsoft Power BI for data analytics and reporting. First, this tool is extremely versatile, allowing users to create a wide range of charts and graphs to quickly and intuitively visualize different types of data. Power BI integrates seamlessly with a variety of other Microsoft programs like Excel and SharePoint, making it easy to access and combine existing datasets. Finally, its powerful customization features allow users to easily tailor the tool to their specific needs and workflows. Overall, these strengths make Power BI an invaluable tool for organizations looking to gain deeper insights from their data.
- At first glance, Sisense may not seem to be the ideal solution for data analysis and visualization. Compared to many other data analytics platforms, it is less intuitive and offers a more complex interface. However, in reality, these factors become Sisense's greatest strengths when it comes to tackling large datasets. Unlike simpler tools that are limited by their scalability, Sisense can easily handle large volumes of information and process them quickly. In addition, its extensive array of powerful features allows users to customize the platform according to their specific needs and preferences, giving them even more control over large datasets. Overall, for businesses looking for an effective solution for big data analysis and visualization, Sisense is an excellent choice. With its sophisticated capabilities and flexible design, it helps organizations unlock insights from even their largest datasets.
- Looked Data Studio or Google Data Studio is a good visualization tool that has a free from Google. One of the main strengths of Data Studio is its flexibility. businesses can connect to a wide range of data sources, including Google Analytics, AdWords, BigQuery, and PostgreSQL. This allows businesses to create customized dashboards that provide the specific information they need. Additionally, Data Studio provides a variety of templates and tools that businesses can use to create stunning visualizations. GDS provides a range of visual communication assets so that the business can create visuals that effectively communicate their data and possible insights. For a free tool focused on a set of visualization issues, it can be a good solution.
- D3.js is a powerful JavaScript library for manipulating and visualizing data. It is particularly well suited for large data sets, because it can scale to meet the needs of even the most demanding applications. Additionally, D3.js is highly flexible, allowing developers to create custom views and interactions. Finally, D3.js is open source, meaning that it is always improving and evolving as new features are added by the community. In summary, D3.js is an incredibly powerful tool that can be used to create truly stunning visualizations. Since it is open-source, it will require more developer time and should be considered before adoption. As a visualization tool, we at Azumo love it!
Programming Languages
In general, data visualization tools can be divided into two categories: those that require programming and those that do not. The majority of these tools do not require a data analyst to know how to code and can be used by anyone, so long as they can access to the underlying dataset. These include: Tableau, Excel, Google Sheets, Power BI, and SiSense among many many others.
On the other hand, the tools that require programming tend to be more powerful and customizable. These data visualization tools include: D3js, R, and Python (which opens a huge world of possibilities for the data scientist or analyst).
What are some common data visualization challenges?
There are many data visualization challenges that data analysts and data scientists face daily. Some common data visualization challenges include:
- Ensuring that data visualizations are accurate
- Finding the right data visualization tool for the job
- Migrating data between data visualization tools
- Ensuring data visualizations are accessible to all users
- Creating data visualizations that tell a story
Data analysts and data scientists must overcome these challenges to effectively communicate data-driven insights.
Is it easy to migrate between different data visualization tools?
Migrating data between different products and technologies can be difficult. Depending on the data visualization tool you are using, you may need to export your data into a format that can be imported by another data visualization tool. For instance, if you are using Tableau, you may need to export your data into a CSV file for it to be imported by another data visualization tool. Additionally, some data visualization tools may not be able to import data from other data visualization tools. For instance, SiSense is a data visualization tool that can only import data from CSV files. Therefore, if you are using Tableau and want to migrate your data to SiSense, you would need to export your data into a CSV file first.
When between tools we have developed some practical tips based on our understanding and use across all of the tools. When migrating data between data visualization tools, it is important to consider the following:
- The format of the data. Some data visualization tools only accept certain formats (e.g., CSV files). Make sure that the data is in a compatible format before attempting to migrate it.
- The structure of the data. Some data visualization tools have specific requirements for the structure of the data. Make sure that the data is structured correctly before attempting to migrate it.
- The size of the data. Some data visualization tools have limits on the amount of data they can handle. Make sure that the data is within the size limits before attempting to migrate it.
What is a data pipeline and how does it support data visualization
A data pipeline is a series of data processing steps. The data is first ingested into the data platform, and then a series of steps are run to transform the data. Each step in the pipeline delivers an output that is used as the input for the next step. This continues until the pipeline is complete. In some cases, independent steps may be run in parallel. Data pipelines are an essential part of data processing, and they can be used to perform a variety of tasks such as data cleaning, data transformation, and data analysis.
A data pipeline is a set of processes that extract, transform, and load data from one system to another. Data pipelines are commonly used to move data between databases, file systems, and data warehouses. Data pipelines can also be used to process streaming data in real-time.
Data pipelines can help data analysts and data scientists overcome some of these challenges. Data pipelines can be used to ETL data, which stands for extract, transform, and load. ETL is a process in which data is extracted from one system, transformed into a format that can be used by another system, and then loaded into the second system. ETL can be used to migrate data between different data visualization tools. Additionally, ETL can be used to clean and transform data before it is visualized. This can help ensure that data visualizations are accurate.
Data pipelines can also be used to process streaming data in real-time. For instance, if you are tracking the stock market, you may want to create a data visualization that shows how the market is performing over time. Data pipelines can help you do this by ingesting data from the stock market and then processing it in real-time so that it can be visualized.
Data pipelines are an essential part of data processing, and they can be used to perform a variety of tasks such as data ETL, data cleaning, data transformation, and data analysis. Data pipelines can help make sure that data visualizations are accurate and accessible to all users.
The Importance of Data Warehousing
Yes, data warehouses can matter when creating data visualizations for a few reasons. First, data warehouses can provide a single source of truth for data that is used in data visualizations. This is important because it ensures that the data being used in data visualizations is accurate and consistent. Data warehouses can be used to store data from multiple data sources, which can be helpful when creating data visualizations that require data from multiple data sources.
Finally, data warehouses can provide a place to store data that is processed and prepared for use in data visualizations. This can be helpful because it reduces the amount of processing that needs to be done when creating data visualizations. In summary, data warehouses can be helpful when creating data visualizations, but they are not required.
Most tools can connect directly to data sources and do not require a data warehouse. However, data warehouses can provide benefits that make data visualizations more accurate and easier to create.
The Emergence Cloud-Based Data Warehouses like Snowflake
Many enterprises over the last decade have moved new data processing from appliance-based applications like Teradata into the cloud, relying on Amazon's Redshift or Snowflake as two leading examples. While there are some unique differences between Snowflake and Redshift, we sometimes view them as interchangeable as they are both outstanding data warehousing choices for the cloud.
Snowflake is a data warehouse that runs in the cloud. It is designed to handle data from a variety of sources, including data warehouses, data lakes, and streaming data. Snowflake offers many features that make it a good choice for data warehousing, including its ability to scale elastically, its support for semi-structured data, and its data-sharing capabilities.
Snowflake is a good choice for data warehousing for many reasons. However, one of the most important reasons is its ability to scale elastically. This means that Snowflake can scale up or down as needed, without affecting performance. This is a major advantage over data warehouses, like Teradata which ran on-premise, and was expensive to scale.
Another reason why Snowflake is a good choice for data warehousing is its support for semi-structured data. This type of data includes data that is not structured in a traditional way, such as JSON data. JSON data is becoming more and more common, as it is often used to store data from web applications. Snowflake is designed to handle this type of data, which makes it a good choice for data warehousing.
Lastly, Snowflake offers data sharing capabilities that make it a good choice for data warehousing. Data sharing allows multiple users to access the same data at the same time. This is a major advantage over data warehouses that do not offer data sharing, as it can be difficult to coordinate data access when multiple users are involved.
What's Next
Data visualization has come a long way since the early days of spreadsheet applications. Today, data visualization experts have a wide array of tools at their disposal to create stunning visual representations of data. These visuals can help us make sense of what might otherwise seem like an overwhelming amount of information. Whether analyzing weather patterns or anticipating customer behavior, data visualization provides opportunities for discovery and new insights.
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