Making Your Data Come to Life: 5 Best Practices and Tips for Data Visualization in 2024

Analytics Nov 2 22 minutes read

Table of contents

    Imagine looking at a bland spreadsheet filled with hundreds of columns containing nothing but some raw numbers…

    Be honest – how well would you understand the data presented in front of you?

    Even if you could, it’ll probably take some time until you connect the dots of how everything relates to one another. And on another note, not everyone in your business will be as data-savvy as you are.

    So, how can we fix this and make data understandable to all of the key members?

    The answer is through data visualization.

    Data visualization refers to using visual elements such as charts, graphs, and maps to help people understand complex information that’s being presented.

    But creating a data story isn’t always a straightforward process…

    That’s why we talked to 57 marketers to see how they leverage data visualization and share some of their tips and best practices on how you can do this successfully in 2024.

    We also talked about some of the most common challenges associated with data visualization, and emerging trends, and got a sneak peek into their data visualization processes.


    Let’s dive in.

    What Is Data Visualization?

    Data visualization is the process of representing data in a graphical or visual format to make complex information more accessible and understandable.

    It involves the use of visual elements such as charts, graphs, maps, and other graphical tools to present data in a way that is both informative and simple to comprehend.

    The primary goal of data visualization is to communicate data patterns, trends, and insights in a more intuitive and compelling manner, allowing individuals to grasp the information more readily than they would from raw data or textual descriptions.

    It’s important to note that data visualization is more than just creating pretty charts – it involves everything from data analysis to design and storytelling.

    Types of Data Visualization

    Data visualization comes in a variety of forms, each designed to serve specific purposes and display different types of data. Here are some of the most common types of data visualization:


    This is the simplest type of data visualization, typically just displaying a single numeric value. It can be used for quick reference or to highlight a specific data point.

    Number Graph

    Line Graph

    Line graphs are ideal for showing trends and patterns over time.

    They connect data points with lines, making it easy to visualize how a variable changes, making it ideal for tracking things like website traffic over a specific period.

    Line Graph

    Bar Graph

    Bar graphs use bars of varying lengths to compare categories or groups. They are effective in visualizing categorical data, like market share by companies or sales by product categories.

    Usually, they display data as horizontal or vertical bars, making it easy to visualize differences.

    bar graph

    Pie Chart

    Pie charts represent data as a circle divided into slices, with each slice showing a portion of the whole.

    They are great for displaying the distribution of a fixed quantity, like a budget or the composition of a population by age group.

    pie chart graph


    Tables present data in a structured format, making it easy to compare values across rows and columns. They are often used for displaying raw data.

    Table Graph


    Funnel charts are used to visualize a process, showing stages where data points are filtered or reduced as they progress.

    Their structure makes them valuable for showing conversion rates in sales, marketing, or other processes.



    Pipeline charts are similar to funnel charts, but they represent a linear process. They are often used to track progress through stages.

    Pipeline chart

    Progress Bar

    Progress bars show the completion or progression of a task. They are simple, effective visuals for indicating status.

    This data visualization type is common in project management and software interfaces to show the progress of downloads, file transfers, or task completion.

    progress bar chart


    Gauges represent data as a dial or needle, often used to display data on a scale or measure performance against targets.

    They are often used to convey performance metrics, such as the progress of achieving a target.

    Comparison Chart

    Comparison charts allow you to visually contrast different data points.

    They help viewers understand the relative differences between items, making it easier to identify trends and patterns.


    Interval charts are designed to show how data changes over a specific time frame.

    They are particularly useful for tracking trends and any changes that occur over a specific time frame.

    interval graph

    Combo Chart

    A combo chart is a versatile visualization that combines multiple chart types in a single view, allowing you to present and compare different aspects of the data.

    For instance, it can show the bounce rate as a line graph and the number of sessions as bars on the same chart.

    Combo chart

    Heat Map

    Heat maps use color gradients to represent data values across a two-dimensional grid.

    They are excellent for revealing patterns, correlations, and areas of concentration within a dataset. For example, website click patterns.


    Leaderboards provide a straightforward way to rank data points by their performance or value.

    This type of data visualization is often used in a competitive context, such as top-performing sales reps or employee performance rankings.

    Multi-tab Chart

    A multi-tab chart typically comprises multiple tabs or sections, each containing different chart views or data subsets.

    It’s a way to organize and present various data visualizations within a single interface for easy navigation.

    Different Ways Companies Use Data Visualization

    Okay, so we’ve covered what data visualization actually is and what the different types we can leverage are.

    Now, when you start visualizing data in your own business, you’re going to spend some time testing and refining your own processes for it.

    But why not pick up a few things from companies that have been doing it for years?

    We asked our respondents a lot of questions about what the process looks like in their business and one of the first things we wanted to know is their primary purpose for data visualization.

    According to our data, data visualization is most frequently used for internal reporting (to staff or departments), followed by marketing and sales initiatives.

    Different Ways Companies Use Data Visualization

    When talking about the exact process, the most attention and time is dedicated to understanding the audience, conducting data analysis, and identifying key insights.

    Incorporating interactivity is the phase our respondents spend the least amount of time on.

    Time invested in Data Visualization

    And as far as the visualization part of the process goes, 42.22% of the respondents use a mix of templates and custom designs.

    Use of templates for data visualization

    5 Data Visualization Best Practices

    There are a lot of moving parts that you need to pay attention to in order to maximize your success with data visualization.

    And sometimes, it can be hard to differentiate the practices you should primarily focus on from the ones that don’t really move the needle.

    Now, for our respondents, no single practice stands out as the one delivering the best results in data visualization.

    The top-ranked (by 24.44% of the respondents) is precisely defining the audience and understanding their needs.

    5 Data Visualization Best Practices

    That said, let’s get into more detail about the practices they shared:

    Precisely Defining the Audience and Understanding Their Needs

    Begin by clearly identifying your audience.

    Are you catering to executives, data analysts, or the general public? The persona of your audience should significantly influence your approach to data visualization.

    For example, executives may require high-level summaries, while analysts often demand granular data with multiple layers of detail to conduct in-depth analyses.

    The general public, on the other hand, may need simplified visuals that steer clear of jargon.

    Dan Fried of Specialty Metals says that this strategy helped them “streamline processes and improve decision-making by tailoring visualizations to the specific needs of jewelers, pawn shops, and industrial clients.”

    “This strategy increased operational efficiency by 20%, fostering a more data-driven culture within our 45-year-old company. As a result, we’ve not only maintained our commitment to excellence but also established ourselves as industry leaders in wholesale recyclable materials.”

    Dhawal Shah of 2Stallions also waged in and said that to “deliver information efficiently, we consider the client’s data literacy, campaign goals, and feedback.”

    “We tailor our data delivery to meet the specific needs of each client, just like a teacher tailors a lesson to meet the specific needs of their students. For example, if a client has limited data literacy, we might provide them with simpler visualizations or written explanations. If a client is focused on a specific campaign goal, we might provide them with data that is most relevant to that goal.

    If a client has specific feedback on how they want to see data presented, we will try to accommodate their requests. Our goal is to make sure that our clients can easily understand and use the data to make informed decisions.”

    Clearly Defining Your Objectives for Visualizing Particular Data

    You always want to start by considering what you want to achieve with your data visualization.

    Are you trying to showcase a trend, compare data points, or highlight variations over time? Your objectives will shape the type of visualization you choose and the story you tell.

    For instance, if your goal is to emphasize the growth of a particular product over time, you might opt for a line chart, which effectively showcases trends. If you need to make comparisons between different categories, a bar chart may be more suitable.

    Doug Van Soest of Socalhomebuyers says that his company’s data visualization process has been “much enhanced by specifying visualization goals precisely and using specific data practices.”

    “This helped us by giving us a clear road map for creating visualizations that exactly fulfilled our objectives. I was able to use the best visualization approaches and strategically organize the data elements to create an engaging narrative since I had clearly stated objectives.

    This targeted strategy improved the visualizations’ relevancy and clarity, making it simpler for stakeholders to quickly understand complex data. Our visualizations improved as a result, sharing insights with the target audience in a way that resonated with them and promoting better understanding and decision-making throughout the organization.”

    Keeping Visualizations Clean and Simple

    Data visualization, at its core, is meant to communicate information quickly and clearly.

    This simplicity begins with the choice of visual elements.

    You should always look for the visualization that conveys your message with the least complexity. Unnecessary design elements or a cluttered appearance can confuse the viewer and dilute the impact of the data.

    For example, if a bar chart is sufficient to represent your data, don’t opt for a more complex visualization type.

    David Krauter of Websites That Sell says that “prioritizing clear and straightforward visualizations has been crucial in my role.”

    “By embracing simplicity, we make it possible for our clients to quickly understand and take action on complex data. Users can easily gain insights thanks to a clear visual hierarchy, a simple design, and targeted messaging.

    A 15% increase in user engagement and positive feedback highlighting the user-friendliness of our designs were the results of this practice, which significantly decreased the time required for client onboarding. Clean visualizations emphasize our dedication to user-centricity and solidify Websites That Sell’s reputation as a reliable source for design-driven clarity.”

    “Prioritizing clear and straightforward visualizations has been crucial in my role.

    By embracing simplicity, we make it possible for our clients to quickly understand and take action on complex data. Users can easily gain insights thanks to a clear visual hierarchy, a simple design, and targeted messaging.

    A 15% increase in user engagement and positive feedback highlighting the user-friendliness of our designs were the results of this practice, which significantly decreased the time required for client onboarding. Clean visualizations emphasize our dedication to user-centricity and solidify Websites That Sell’s reputation as a reliable source for design-driven clarity.”

    David Krauter

    David Krauter

    Founder at Websites That Sell

    Want to get highlighted in our next report? Become a contributor now

    Using the Right Tools for Data Visualization

    Basic data visualization tasks may be accomplished with spreadsheet software like Microsoft Excel or Google Sheets, which offer charting capabilities.

    But for more sophisticated and customized visualizations, you should consider dedicated data visualization software such as Databox, Tableau, and Power BI.

    For example, visualizations that generally take hours in spreadsheets will only take a few minutes in Databox.

    Once you connect your data source and drag-and-drop your most important metrics, turning those numbers into sleek visuals is just a matter of a few clicks.

    Not only will you blow away your audience with a professional-looking dashboard, but they’ll be able to understand the numbers regardless of how data-savvy they are.

    Databox Dahboard Designer

    Using Attractive Colors and Design Elements

    Don’t forget to consider color psychology and the message you want to convey.

    Vibrant colors can draw attention to specific data points, while muted tones may create a more calming effect. A well-selected color palette should complement the data and help viewers connect with the information.

    Luke Van Der Veer of Luke Van Der Veer shares that his team made “data more approachable by carefully choosing a color scheme that captured the essence of our brand and was compatible with the tastes of our audience.”

    “For instance, a clear visual language was produced by utilizing warm, comforting colors for trends that were encouraging and cooler tones for data that were warning. Stakeholders were drawn in by the visual appeal and were persuaded to learn more about the insights. It was like using data to paint a picture, where each hue and component contributed to the creation of the story. This approach not only improved comprehension but also helped the information stick in the mind, which had a long-lasting effect on how decisions were made.”

    4 Common Challenges Associated with Data Visualization

    Data visualization is probably the best way to make sure your audience understands the context behind the numbers, but this practice does have its challenges.

    To be specific, 44.44% of the respondents stated that they faced challenges in balancing simplicity with information density when creating effective data visualizations in 2023.

    More than one-third of them (each) stated that they faced challenges with Ensuring data integrity and accuracy and Choosing the right charts and graphs.

    4 Common Challenges Associated with Data Visualization

    Let’s check out what the respondents had to say about these challenges.

    Communicating Complicated Insights to Stakeholders with Different Analytical Understanding

    This scenario is common in organizations where data is interpreted by individuals with different roles and expertise.

    To address this effectively, it’s important to find some sort of balance between depth and simplicity in your data visualizations.

    William Manning of Pole Barn Kits says that he created a “two-tiered strategy to deal with this challenge.”

    “In the beginning, I used a user-centric design approach, adjusting visualizations to the unique requirements and comprehension capacities of various stakeholders. This required deconstructing technical lingo and concentrating on the most important ideas pertinent to their roles.

    Second, I led interactive workshops to give participants practical instruction on correctly reading and utilizing visualizations. This method helped close the communication gap between data professionals and non-experts, improving understanding and encouraging better decision-making throughout the organization.”

    Conveying Your Ideas without Tiring the Audience

    You want to get your message across clearly, but you don’t want to overwhelm or bore your audience. It’s all about finding that sweet spot between informative and engaging.

    But when it comes to numbers, many people are programmed to view it as some sort of mundane task that they need to power through…

    Sally Johnson of Greenlightbooking says that she combats this by “prioritizing the most important messages and employing crystal-clear, succinct images to address this. It is easier to convey information when the right chart types, color schemes, and labels are used.

    This prevents the audience from feeling overloaded with information and allows them to easily understand the key patterns and trends. I also frequently ask for comments to improve the visualization, trying to strike a balance between providing a complete image and facilitating understanding.”

    Harmonizing Metrics from Multiple Different Sources

    It’s not uncommon to find yourself juggling data from multiple sources.

    Different platforms, departments, or systems can produce data in various formats, and aligning them for meaningful comparisons can be a formidable challenge.

    Andrew Becks of 301 Digital Media has some interesting advice for this issue:

    “The best advice I can offer to solve this is to follow the old adage ‘measure twice, cut once’. That is, spend more time on ensuring that the data is well-formatted and accurate before beginning the process of creating visualizations since a well-presented visual with inaccurate data is meaningless and will result in a propensity among stakeholders not to trust the underlying data and erode the value derived from the visualizations themselves.”

    “The best advice I can offer to solve this is to follow the old adage ‘measure twice, cut once’. That is, spend more time on ensuring that the data is well-formatted and accurate before beginning the process of creating visualizations since a well-presented visual with inaccurate data is meaningless and will result in a propensity among stakeholders not to trust the underlying data and erode the value derived from the visualizations themselves.”

    Andrew Becks

    Andrew Becks

    Co-Founder at 301 Digital Media

    Want to get highlighted in our next report? Become a contributor now

    Handling Complex Datasets

    Managing complex datasets presents a significant challenge due to several reasons.

    First, complex datasets tend to be vast and multifaceted. This complexity often comes from the sheer volume of data points, numerous variables, and the intricacies of the data structure.

    Another reason is potential data anomalies. These datasets may contain missing values, outliers, or inconsistencies that require careful preprocessing. Dealing with these anomalies is time-consuming and may impact the accuracy of the visualization.

    Jason Wyrwicz of POTS PLANTERS & MORE says that at his business, they “often deal with extensive product catalogs and customer data, which can overwhelm traditional visualization tools.”

    “To address this challenge, we adopted advanced data analytics software and cloud-based solutions. This allowed us to efficiently process and visualize large datasets, creating meaningful insights.”

    4 Promising Data Visualization Trends to Keep an Eye On

    The data visualization landscape is constantly changing.

    With new tools, trends, and best practices emerging every so often, it’s a good idea for businesses to stay on top of them to try and identify new ways to streamline their data visualization process.

    We asked our respondents what emerging trends they think show the most potential and here’s what they shared:

    Leveraging Videos

    Video allows for dynamic storytelling by combining visuals, audio, and text to convey complex data in a more compelling manner.

    Plus, video is an inherently engaging medium. It can hold the viewer’s attention for longer periods than static images or text, making it an excellent tool for presenting data in a way that is easier for the audience to understand.

    James McNally of Self Drive Vehicle Hire believes that “more and more people are going to utilize video in their data visualization.”

    He adds that it will be “one of the most persisting trends in this field. People always exhibit interest and enthusiasm in watching videos. There are hardly any people who don’t like watching them. Videos also have a swift and long-lasting effect on audiences. They can have a deeper penetration into their psyche. It will make them remember videos for a long time. Likewise, they can establish an emotional connection, which resonates with viewers more.”

    Animated and Interactive Visualization

    Unlike static charts and graphs, animated and interactive visualizations have an easier time bringing data to life.

    Animation allows data to unfold gradually, making it easier for viewers to interpret the information presented. This is particularly useful in scenarios where temporal changes or sequences of events are critical. 

    Moreover, interactivity allows users to explore data on their own terms. Instead of being limited to a predetermined view of the data, users can interact with the visualization, zoom in on specific details, filter out noise, or change parameters to investigate their own questions.

    Matthew Smith of Ticket Squeeze is keeping an eye on this trend and says that this strategy adds the “possibility to comprehend how data points have evolved through time to the viewer.”

    “Without using a lot of language, this kind of visualization enables producers to express ideas and tell tales. Given the audience’s tendency to have short attention spans, that is a crucial aspect. In addition to engaging viewers more effectively than static visualizations or text, animated visualizations stand out amid other digital material.”

    Marcus Philips of Mortgages also says that this trend shows the most promise and adds that “AI will boost it to an even higher level.”

    “Its high potential is due to its engagement rate. Audiences will feel more connected to content such that. Furthermore, creators will also find it easier to manage it. AI-driven animated content will allow more customization and automation.”

    Artificial Intelligence (AI)

    Artificial intelligence has become one of the most popular emerging trends in data visualization for several reasons.

    For starters, AI has the capacity to process vast amounts of data quickly and efficiently, enabling more sophisticated and insightful visual representations.

    It can identify patterns, outliers, and correlations that might be missed by traditional manual methods, making data visualizations more actionable.

    What’s more, machine learning algorithms can adapt to user preferences and behaviors, allowing for real-time adjustments and personalization of the displayed information.

    For Jean Christopher Gabler of Yogi Times, something that is especially interesting in this area is Explainable AI (XAI):

    “Explainable AI (XAI) is the most encouraging trend for the future of data visualization. Understanding complicated algorithms is essential as AI becomes more prevalent. AI decision-making is represented visually in XAI, allowing users to understand it. This openness promotes confidence and enables better decision-making. The capacity to demystify AI-driven insights is essential at a time when the technology affects many different industries. As a founder who is dedicated to advancement, embracing XAI is consistent with the company’s mission to foster collaboration and understanding in the dynamic world of data and technology.”

    Augmented Reality (AR)

    AR has the potential to bridge the gap between the digital and physical worlds, allowing users to interact with data in a more intuitive manner than ever before.

    One key advantage of AR in data visualization is its ability to enhance context and spatial awareness.

    Traditional data visualization methods often present information in 2D or 3D charts and graphs, which can be limiting when trying to convey the relationships between data points in a real-world context.

    Steve Parr of Parr Business Law says he is impressed with AR’s ability to “seamlessly blend digital information with the physical world, allowing users to interact with data in real-time and real-world contexts.”

    “This technology offers unparalleled opportunities for immersive and context-rich data experiences. Whether visualizing complex legal scenarios or presenting data-driven insights, AR can enhance understanding by providing dynamic, spatially anchored visualizations. It enables users to explore data from different angles, fostering deeper comprehension and informed decision-making, making it a standout trend in data visualization’s evolution.”


    Step Up Your Data Visualization Game with Databox

    The bottom line is that if you’re not doing data visualization properly (or at all), you’re taking a big risk that key team members and company executives won’t understand the numbers presented to them.

    And this can lead to an avalanche of issues… from data-less decision-making to missing potentially lucrative growth opportunities in the market.

    That’s why you need to find a data visualization process that’s both efficient and time-friendly.

    With some tools, you can create dashboards rather quickly, but the data still isn’t at that understandable for many of the team members…

    Or, you end up with a great dashboard, but it took you countless hours and headaches to build it.

    Databox combines the best of both worlds.

    With Databox, creating easy-to-understand dashboards is a few minutes long process.

    You simply connect your data source, drag-and-drop the metrics you want to track, and then transform them into professional visuals with just a few clicks of a button.

    There are 100+ integrations available and you can choose to either build your dashboard from scratch or simply download one of our 200+ pre-built templates and customize them according to your specific needs.

    For creating visuals, you can forget about spending hours in Excel’s chart editor or even hiring designers to do the work – our one-click visualizations allow you to do the work in minutes.

    Not only is the entire process convenient, but your team is going to love the sleek and understandable design of your reports. Making your audience understand the raw numbers has never been easier.

    But you don’t need to take our word for it.

    Sign up for a free trial and see for yourself.

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