An Introduction to Data Visualization: From What It Is to What It Does — And How It Works for You
Let’s start at the beginning: What is data visualization, and why does it matter?
Everyone has data. But not everyone knows how to read or use it. When data is presented in a solely textual manner (daunting Excel docs, unending CSVs), it can be difficult to interpret. Many of us could spend hours staring at information and never see the patterns that live within the data. This is where data visualization comes to the rescue. Using graphical, visual representations of information, data visualization presents complex data in easy-to-understand ways, allowing users to quickly discover essential patterns, trends, and anomalies.
Data visualizations tell meaningful stories, uncover hidden intelligence, kick-start conversations, and lead to deeper, more significant discoveries for businesses and customers — and there are dozens of data visualizations you can choose from to get the most out of your data. We’ll take you through the processes of data visualization, introducing the tools you’ll use for the biggest impact on your business. Let’s crack into it.
Data dashboards: The first wave of data visualization
Traditional dashboards are built from charts, graphs, line plots, or maps. They can include scatter plots, infographics, pie or bar charts, word clouds — anything that can help you visualize your data. Dashboards are also one of the most vital data visualization tools out there. Strong dashboards are crucial components to data comprehension and effective use. They offer a way to analyze, track, and manage data points, metrics, and KPIs (key performance indicators) so you can fully understand the health of your business. Without them, data is complex and complicated at best, overwhelming and impossible at worst.
Beyond being an essential business asset, they save you time and human power scouring thousands (maybe millions or billions) of data rows for the information you need to make informed decisions. To put it best, using data dashboards is the difference between choosing a direction based on facts or closing your eyes, crossing your fingers, and hoping for the best.
Choosing the best dashboard for your needs
With all this information floating around the digital atmosphere, you didn’t think there would be just one type of dashboard for everyone, did you? Just as your business is unique, so too are data dashboards (at least, they should be). With many different types of dashboards, you have unlimited options for how you view your data.
To get a deeper understanding of dashboards, here is a simple breakdown of what they are, how they’re made, and how they can be used:
A basic dashboard is composed of quality data presented in a visual manner that paints a clear picture of a specific goal or objective. It can include an array of charts, graphs, line plots, or other graphics to help users get a better understanding of how their business is performing. Dashboards are made of widgets. Basic dashboards are the starting point for what’s possible.
In essence, a group of widgets makes up a dashboard. Widgets make dashboards even more powerful. They provide additional visual representation of data extracted from the business assets with which they are embedded. Without widgets, dashboards are a singular source of information. With them, you can provide multiple levels of data within one common space, allowing you to see a holistic picture of your business. We advise using seven to 10 widgets for your dashboards to ensure you’re getting enough information without overloading users.
With Sisense BloX, you can go even further, customizing your widgets to make them match your brand’s look and feel. This is something we’ll touch on later.
Embedded dashboards display data and analytics from existing business applications. By embedding an analytics platform into tools, workflows, and applications your company already uses, you can pool large amounts of datasets and use them to create strategies based on the tools your business uses daily.
Think of it this way: When you embed a data analytics tool into your existing applications, your dashboards become ingrained into your existing workflows, allowing you to access your data without having to exit your applications. This saves time as you don’t need to develop an analytic tool, and it makes managing your data a snap.
You can even embed widgets into your existing applications to seamlessly show data and analytics inside these platforms. Embedded widgets are integrated into your existing applications with copied and pasted code, making them easy to set up. Embedded widgets can be found everywhere, from the search function on an airline website to your city’s weather on the app on your phone.
Native dashboards take data and insights from embedded analytics and create dashboards that match the platforms with which they are connected. For example, users become accustomed to the look and feel of the applications they use every day. Native dashboards can match the UX/UI of these applications to provide customers with a consistent, cohesive experience.
We also call this “white labeling,” which means that your data is presented to match your brand identity, from design to colors to layouts that resemble your website or most used apps. In addition to powerful data analysis, native/white-labeled dashboards deliver an enhanced user experience with custom app-like visual interfaces.
Let’s use Skullcandy as an example. Imagine how powerful it would be to show the top/bottom 10 headphones in terms of revenue with a picture of the headphone model with the model name and details, as opposed to simply a bar chart or table. It becomes even more powerful if clicking on the model opens up another view with the power to dig into the data further and take an action like increasing spend on an ad campaign or inputting a ticket to the support team.
It’s essential to build seamless experiences to customize and change the look and feel of data analytics to match your product. Take a look:
“Hidden” dashboards, dashboards that don’t look like dashboards, are a major part of data visualization today. So much of the information we use daily is actually a dashboard propelled by data — you likely view and use data without even knowing it.
The health app on your smartphone that displays steps and your heart rate is a dashboard run by data. If you’re an avid hiker looking for trails ranked difficult in a specific mountain range, that’s a dashboard. When you want to know if it’s likely to rain in Los Angeles in June, you’re using a dashboard. Hidden dashboards present information based on user need, so they are completely customizable based on audience.
Let’s take a look at a dashboard that doesn’t look like a typical dashboard. The sleep widget on your iPhone is actually a dashboard, displaying your sleep data in a visual manner that is simple for you to process. Your overall time spent in bed, days, and specific times are all widgets that give you data with which you can make better decisions for your health.
If we break it down simply, hidden dashboards act as applications. They are designed to eliminate the guesswork traditional data dashboards can pose and create a universal system for understanding data. Not everyone can look at charts or bar graphs and know how to make sense of the information. But when dashboards are customized to fit the needs of their audience, and data is presented in a highly visual, digestible manner, users can comprehend data points and make smart, informed decisions.
Want to see a variety of dashboards in action? Take a look at some industry examples from the Sisense platform.
Out with the old, in with the new: Modern data dashboards
Today, business intelligence and data analytics are not additions to your business. They are a part of your business, working seamlessly with your existing platforms, applications, and workflows. This makes dashboards and decision-making more accessible than ever. Instead of having to access your data elsewhere, embedded analytics allow dashboards to be directly placed within your workflows, meaning more time to use your data and less time searching for it. This is the power of modern dashboards. They aren’t just charts that focus on a singular angle of your business; they, in essence, act as applications that are powered by widgets to show you the entire scope of your organization or only the most important piece of data at a given time.
With embedded analytics, your dashboards are customized to match your existing systems, applications, and tools, blending in perfectly. They also can be customized to match your branding, including fonts, color palettes, and logos. You can see that in these examples:
Discover what lies beyond the dashboard with Sisense Fusion Analytics and Sisense Fusion Embed.Want to Grow Your Business — Fast?
Going beyond the dashboard: Adding AI to surface insights
Dashboards are the difference between “Here’s my data” and “Wow! I understand my data.” They present your information in a digestible manner and a pretty one — but without the ability to organize, manage, and prioritize the information they produce, dashboards are simply impressive pictures. This is why going beyond the dashboard becomes integral to your business.
Modern dashboards are all about making information as accessible, usable, and ubiquitous as possible. Dashboards are now powered by AI, which allows users to get specific information and take action on it. AI digs through the depths of data and pulls out the information users need based on their queries. Once this data is extracted, it is presented in the widgets that make up your dashboards.
Even further, when you determine the KPIs needed to meet your business goals, AI is used to deliver the data that provides you with the insights into how your KPIs are performing. Without it, the process of measuring KPIs and key business metrics can be arduous, long, and riddled with human error.
That’s not all AI can do. It also plays a huge role in the functionality of dashboards and widgets, which makes working within these spaces simpler for users. For example, you can add natural language query (NLQ) to your dashboard. NLQ mimics the natural human language, and with the help of AI and machine learning, it presents data in the most natural manner possible — plain, readable text.
Autocorrect is a form of NLQ. The more you type on your smartphone or tablet, the more that device learns mannerisms, spellings, sentence structure, syntax, and flow, eventually enabling your device to complete sentences for you organically. The same thing can be done in your dashboard.
Here is an example of an AI-powered Q+A generator that allows you to ask sophisticated questions and receive natural language-based answers that are easy to understand so anyone, no matter their role, can make sense of data. The more you interact with this system, the more it begins to learn your language and adapt for the future. Take a look:
Another example can be found in customer support tools, like the search bar in the AP Stylebook. Using predictive text, the search bar will attempt to lead you to answers faster based on how you format queries within the search.
Quick tips and tricks: How to set up your dashboards for success
- Find and set benchmarks
- Map out your goals
- Determine your KPIs
- Monitor traffic (Watch your bounce rates, time spent on pages, etc.)
- Dive into specifics (Who are you trying to reach? What topics are you focusing on?)
- Watch engagement metrics (How are users engaging?)
- Proof of ROI (Include sales metrics in your dashboards for full transparency)
The power and benefits of AI
As time goes on, AI-powered solutions will become stronger and more effective. Just as humans are capable of learning and growing, so too are machines. AI data solutions will continue to absorb audience information, track trends faster, and learn how to make more effective predictions. The speed with which AI-powered data solutions can become more intelligent surpasses human ability, and in the end, machines can see things we simply can’t. Using AI-powered insights is the cherry on top of your actionable insights sundae, positioning your business for future success with efficiency, accuracy, and unparalleled value.
So what does this mean for data visualization? It’s going to be a lot stronger. Consider this: To truly understand the position of your business, you need to look at it from a holistic perspective, not just a few components at a time. Before AI, dashboards were a more siloed way to view specific aspects of business. You could look at a few metrics at a time, which was and still is valuable, but with AI-powered dashboards, you can expand your view to hundreds of thousands of KPIs and metrics in real time — something we as humans can’t possibly achieve.
The benefits of AI are rooted in uncovering the hidden truths of your business to find inconsistencies, peculiarities, and caveats that are otherwise overlooked. These seemingly small details can be the difference between success and failure.
ArtFrames.com’s senior product managers wanted to gain a full understanding of how the brand has been performing the last two years, including 2020 to discover the impact COVID-19 had on its inventory, sales, and overall profitability. They need to view the last two years from a wide-angle lens — one visual won’t do. But with AI, take a look at how their data visualizations grew to meet those goals.
Take action with insights (and a little help from your widgets)
As if widgets aren’t amazing enough, they can do even more to support your data visualizations. Whether you’re operating on a main dashboard or using one widget customized to your role, you can add CTA (call-to-action) buttons to any widget, which makes them actionable resources for your business. Here’s how simple it is to add a new, actionable widget to your workflow:
Adding these widgets is simple:
By following these steps, beginning with “Add Widget,” you can define what type of widget you want, what action it needs to produce, and how it will be displayed. You can learn more about how to add actionable insights to your widgets here.
Here’s an example of how you can take action directly from your dashboard. ArtFrames.com has been having trouble with customers ordering sold-out frames. Instead of employees having to leave their dashboard and go into the website to manually cease orders on sold-out frames, or engage in a multistep, third-party solution, the company can simply create a “sold out” widget that lives on its main dashboard and add a “stop order” CTA button within that widget.
The button directly connects to the ArtFrames.com website, detects when a frame is sold out, and automatically launches the “stop order” CTA, prohibiting users from ordering any frame that is unavailable. This allows product managers to take direct action without disrupting their workflow, saving time.
Build your best data visualizations with these helpful tips and guidelines
Beautiful, impactful data visuals don’t just appear out of thin, digital air. They’re carefully constructed based on your goals, objectives, and interests. Embedded data analytics platforms take care of data extraction and collection; you create custom dashboards white-labeled to match your brand and highlight the stories you’re looking to tell.
But where do you start? Read on to discover how to design your final dashboards, avoid common pitfalls, and build the most powerful data visualizations for your business.
Start strong: Assembling your dashboard
Most dashboards follow a formulaic structure. While you can make adjustments as needed, it’s highly effective to use this common build:
Dashboard header: Make this descriptive and direct so users understand what they’re viewing.
Dashboard title: Use an explicit title that says what the dashboard addresses. Using the word “dashboard” in the title is sometimes redundant.
Icons: Have client logos and Help icons if necessary. They should be of a consistent size and aligned with each other, and consistent with other dashboards.
Displaying core metrics in header: If the business user can use a parameter or filter to show a core metric for the dashboard, the name of the selected metric should also be embedded in the header. The aim is to reduce the amount of effort needed by the business user to identify exactly what information is being presented.
Time period: Display the dates being selected prominently. If the business user can choose a date band, show Start Date to End Date.
KPIs: KPIs should provide a summary of the dashboard’s main points:
- They should generally be shown at the top of the visualization area.
- Standard fonts and colors and numbering conventions should be used, but they can generally be larger than on individual visualizations.
- Icons can be used to make results clearer.
- For embedded analytics, try inserting some general KPIs and leave the option for the user to change and insert new ones.
Formatting and display: The gold standard
- There should be standardized fonts and sizes that comply with a style guide.
- Avoid excessively small text.
- When embedding, ask the client for a copy of the brand and style guide in order to incorporate into the dashboards.
- The developer should use a palette that works for a range of users, including those who are colorblind.
- If there is only a single dimension, all members of that dimension should be the same color.
- Make sure the color legend is explicit, prominent, and easily identifiable.
- Are colors consistent for the same dimension on different visualizations?
- Never use white or light text colors over light backgrounds.
- Are all data points labeled?
- Is it clear what data point is referenced?
- Make sure labels are unambiguous. Avoid abbreviations where possible.
- Show total amounts in titles if relevant.
- Is it possible to misinterpret the values?
Axes, borders, and gridlines:
- Axes, borders, and gridlines should only be used where necessary to understand the visualizations.
- Dollar amounts should be shown with currency symbols.
- Rounding should be intuitive.
- For values of less than 100,000, consider showing absolute numbers rounded to the nearest whole number, i.e., $64,567.33 is shown as $64,567.
- For values of 100,000 to 999,999, use the K convention to one decimal place, i.e., $564,567.33 becomes $564.6K.
- For values over $1 million, show to a million, with two decimal places, i.e., $9,564,567.33 becomes $9.56m
- Avoid showing values in terms of ‘000s.
- Showing 5,550 with ‘000s in the title is far less clear than just showing 5.55m. By using the “m” notation, the business user doesn’t have to do mental arithmetic to convert the value to millions.
Highlighting the most relevant values:
- Highlight values that change with updated selections in bold and italics.
Clarity of language:
- Are there any abbreviations that could be misinterpreted by the nonspecialist business user?
- Are there any typos or spelling mistakes?
- Does the title make grammatical and logical sense?
- Tooltips should give additional details. They should not be substitutes for labeling.
- The other standards regarding font, numbering, color scheme, and clarity should also be applied to tooltips.
Common visualization downfalls and how to avoid them
Overflow of information
It’s tempting to try to build a single visualization for all your data. However, this leads to complicated dashboards that are difficult to comprehend. Additionally, combining magnitude and composition can also lead to an overload of information in one visual, making it difficult to get full value. Create dashboards and widgets with specific information, and remember you don’t have to fit all your data into one place. Here’s an example of how to break up your visualizations so they are easier to understand:
It is typically better to split this into two separate visualizations.
Visuals that take over
The visuals of your dashboard are essential to its purpose, but cramming too many into one dashboard creates a visual mess and forces users to scroll endlessly, potentially missing vital information.
Natural language should be used as much as possible. Abbreviations should only be used if there is no alternative. Abbreviations aren’t always universal, which can make them an obstacle. For example:
- Average rather than Avg.
- Amount rather than Amt.
Simplify naming conventions
When adding data fields from the source, the names don’t always transfer in a way that makes sense for visualizations. Be sure to rename them for ease of use and comprehension. For example:
- Using the name “Prod Desc” rather than the simpler “Product.”
Don’t overload a single visualization with too much information. This can overwhelm and confuse users. For example: Don’t create a graphic with sales, demographic, and purchase history information all in one. Create separate charts or visuals for the best results.
Use the appropriate visualization
Complex information calls for complex visualization. However, not all data deserves extensive charts, graphs, or graphics. When choosing which visuals to use for your dashboard, be sure the execution fits the information.
Be smart with labels
Users shouldn’t have to search tooltips or other interactions to know the values being displayed. Your visuals need to be clearly defined.
Using geographic maps wisely
When it comes to geography, save maps for specific information, such as potential customers, revenue, or demand by region. Steer clear of situations where:
- Large contributions from the geographically smaller state can be missed.
- Geographically larger states can be given undue weight by the business user due to their size.
Don’t go overboard on granular
Granular information is highly detailed, vast data. When you try to incorporate too much of this information, your dashboards become counterproductive, undifferentiated, and confusing for users.
Are you a data visualization novice?
If you’re super new to the world of data, here are a few additional resources to help you create the most effective visuals, fully customized with your brand’s logo, colors, fonts, and identity:
But wait! There’s more resources for your team
To help you make the most of your data, use these worksheets and checklists for extra guidance and support.
Dashboard Design Worksheets & Checklists
Common Dashboard Pitfalls
Initial planning checklist
Visualization design – to be checked for each proposed visualization
Individual visualization review
Assembling final dashboard
Individual visualization review
Final dashboard structure
See how Sisense visualizes data with our extensive list of example dashboards:Example Dashboards