Viewing data as a wall of rows and columns can be a formidable barrier to understanding. As we work with more business information daily, in sky-rocketing volumes, it’s essential that everyone – from experienced developers to data visualization novices – are confident working with data and extracting value from data, fast.
The truth is that data visualization – at its heart – is meant to be a tool for empowerment.
“Data visualization helps us understand information faster,” Forbes shared. We couldn’t agree more. The ability to visualize data – to move past walls of rows and columns and actually tell a story – introduced a great leap forward in Business Intelligence, greatly changing the way we ingest and understand business insight, and how we act on it.
What is data visualization and why is it important?
Data visualization is a visual representation of data that simplifies complex information. Data is translated into smaller, easy-to-understand parts and displayed using visual objects like charts and graphs. These visual objects – like Pie charts or Line graphs – help users easily recognize patterns and trends that may have gone undetected had they been required to navigate a wall of figures.
According to Gartner, another term for data visualization is interactive visual exploration. We love that description.
Because the process of uncovering insights isn’t linear, or discrete, it’s a journey. So, it’s important that your analysis is not limited to a static visual. Data visualization that enables users to interact with and deep-dive into data to find answers is the best, most reliable, and most dynamic way forward, in any number of industries, businesses, and teams.
How is data visualization used?
Data visualization is used to turn data into a story.
According to TechTarget, data visualization “provides a quick and effective way to communicate information in a universal manner using visual information.” Along with helping businesses “identify which factors affect customer behavior, pinpoint areas that need attention, and make data more memorable for stakeholders,” visualizing data creates value across the enterprise.
By visualizing data, we’re able to simplify analysis. Why? Because many of us are visual learners. So we learn things easier by looking at information that’s presented in an approachable, digestible way. Instead of overwhelming less technically skilled users with row after row of data, and expecting them to somehow uncover insights, a better solution is to simplify data by creating visual data stories.
When the data is in a more consumable format, like a Line or Bar chart, everyone can ask and answer their own business questions and drill down further into the data independently.
What are the main types of data visualization?
There’s a range of data visualizations available for everyone to use. From individuals to enterprises, there are a number of solutions that can empower anyone to more easily draw insight from their data.
Most people have seen and used the common data visualization, or dataviz types, such as a Line, Bar, Column, or Pie chart. Even though these are considered basic dataviz, they help present data in a compelling, interesting way. Importantly, these types of visualizations are easy to create, and often require little or no advanced skills or training to produce.
More complex analysis might require charts such as the Sankey chart, Heatmap, or Mekko chart. These visualizations are slightly more advanced, but with intuitive analytics software, most users should be able to create visualizations with advanced dataviz objects, too.
How to choose the best data visualization
With many different options of charts, tables, and other dataviz elements, how do you choose the best one for your data story? Let’s explore a few common visualization components.
Tables can display large quantities of data – from information such as age group or education level (called categorical data) or items like revenue in dollars or age in years (called qualitative data). Tables are great tools for when you have vast amounts of data, but if you need to see a high-level view or gain insights at-a-glance, a table may not be the best option. A Pivot table, on the other hand, offers users the chance to change perspective and answer questions fast by pivoting the data (changing rows to columns and vice versa) to suit specific queries.
Bar charts measure quantitative or categorical data and are a good way to compare the quantities of different categories. You could analyze different product types by visualizing the data in a Bar chart, for instance. Bar charts can be displayed horizontally, vertically, or even stacked on top of each other (a Stacked Bar chart), which is great for determining the contribution of different sub-groups to a total amount and comparing their performance.
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Line charts use ordinal data (such as a customer satisfaction scale from 1 to 5) or quantitative data. These charts are excellent for tracking change over time or recognizing the key performance or essential data points that reflect as spikes or dips on the graph. These are also useful for comparing the performance of multiple categories over the same period. An example is healthcare providers tracking patient satisfaction levels over a specific time frame.
Progress charts track the amount of work completed on a project or the progress made towards a goal. This chart is usually in the form of a gauge or arc shape and displays data referenced against a defined value, such as the progress of monthly sales targets. You’ll find these charts provide a clear, simple way to view progress at-a-glance.
Pie and donut charts
Pie and donut charts help you visualize categorical data, like a visual breakdown of your marketing spend for the year. They show the comparison between the parts of a whole, the sizes of different portions of a metric, and help you visualize how many elements make up a total. The difference between Pie and Donut charts is that Donut charts have a whole in the center. These charts are easy to understand, but are typically not the best choice for analyzing large volumes of data.
Scatter charts reflect quantitative, ordinal, or categorical data and show the values of two variables against two axes in a ‘scatter’ formation. For example, the relationship between patient satisfaction levels and consultation length. The pattern of the results shows the relationship between the variables. So, you might be able to determine that patients’ satisfaction is higher when their consultation is longer, or vice versa. You can also demonstrate this relationship by the size of the data bubbles. This type of chart is only valuable if your data is related.
Heatmaps measure categorical and quantitative data and display data in a matrix or grid composed of shapes. These are great for analyzing large or complex data sets, such as the performance of production bays in real-time, as colors are easier to distinguish than a multitude of numbers. Plus, they’re simpler to interpret — the darker the color shade, for instance, the higher the value it represents.
How to create powerful data visualizations
Here are seven steps to help you create data visualizations that keep insights flowing in your organization.
- Ask questions to establish what you’re trying to measure and why. For example, ‘Are we meeting our support targets for the month?’. And remember, measuring for the sake of measuring – with no connection to a business goal – is a waste of your time.
- Get the data that answers your questions. This might include implementing new processes to collect the right data or retrieving data from siloes across your business.
- Develop your dataviz plan and choose the visualization type that best suits your data story and audience.
- Review and analyze the data findings.
- Extract the insights and analyze the trends and patterns the visualizations reveal.
- Share the data findings with your team and other colleagues so you can collaborate and use collective intelligence to maximize the value of your data.
- Refine your processes and follow these same steps the next time you have a new question or situation that needs improving!
Tips to avoid misleading visualizations
It’s important to create visualizations that help steer users, but it’s just as important to make sure that your visuals don’t become misleading. Why? Because misleading or unclear visuals can trigger false insights. And subjective considerations like bias – how people interpret the same thing differently – also play a significant role. To avoid these pitfalls, there are a few tips to keep in mind.
Define the visualization’s purpose clearly. Make sure you understand what you’re being asked to visualize, and align your approach with that goal.
- Only include what you need to. Exclude additives that will muddy the insight journey. Think about the data story, the message you want to get across, and what action you want end-users to take. Don’t clutter the dashboard with visuals that don’t need to be there.
- Make the visual easily consumable. For the most part, anyone should be able to understand what’s going on at a glance and answer questions quickly.
- Keep the message in context by adding things like labels and headings. Without context, data can become ambiguous or confusing.
- Show data truthfully. For example, use color carefully. Harvard Business School suggests that we avoid poor color choices to create the most powerful story with our data. Using too many colors overwhelms the viewer, and using familiar colors in unfamiliar ways (like using the color green to mean “stop”) can also result in unnecessary cognitive tax. Lastly, use plain, clear language on your dataviz: the word ‘increase’ is a better choice than ‘surge’ to get your message across straightforwardly and objectively.
Choosing a data visualization tool for modern data teams
Modern data teams need modern solutions like Astrato, that are intuitive, easy to integrate with other business tools, do the heavy lifting for you, and speed up time to value. Data analytics software that is not cloud-based, or that moves or replicates data, does not provide the security, agility, and performance that modern data teams need.
Cloud-native analytics solutions integrate visualizations directly on top of your cloud data platform. This means you have access to real-time dashboards that support the pace of your business decisions. Astrato is a next-generation cloud-native BI and analytics platform. With Astrato, there is no data movement, so everyone has direct, real-time access to their data. That means that data is fresh and reliable to support everyone in making smarter, more accurate decisions.
Plus, you don’t need any coding skills to create powerful visual data stories with Astrato. That doesn’t mean that a skilled developer can’t access sophisticated functionality within Astrato – those capabilities are available for people who want to use them – but it does mean that the basic analytics functions are simple enough for everyone to use: familiar functionalities like drag-and-drop and right-click options make creating visualizations comfortable, and fun. Whether you use the basic functions or take a more advanced approach, the seamless creation experience means that any user will save precious hours every day by working with Astrato, which means they have more time freed up to focus on core job tasks.
Once your visualization is complete, you can embed your live insights into your day-to-day workflow applications with Astrato Embeds. This enables you to infuse live data intelligence into your entire workflow for the whole department. such as your CRM, Salesforce, or HubSpot.
When data teams – or business users – are empowered to create powerful visualizations that help drive meaningful action, everyone wins. Productivity and efficiency are increased, cognitive tax is reduced, and important insights are accessible when and where you need them.
Creating data visualizations is about more than making data easier to understand. It’s about telling a story, and making sure that everyone in your team or organization has a role to play.
Empower your team with Astrato.