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Writer's pictureDimitris Adamidis

Overcoming challenges in your data visuals and tune the data into your audience by asking key questions.

Updated: Mar 17


data visuals

In today's data-driven world, embracing data is essential for informed decision-making, risk reduction, and sustainable business success. Discover how the Graphiti solution can empower your Go-to-Market (GTM) organization, helping you harness the full potential of your data assets for long-term growth. Learn about the opportunities and challenges of this transformative shift and make data your most valuable asset.


Alta - Analytics Platform
 

A study from 2018 by the University of California, Berkeley, suggested that 60% of data analysts say that interactive visualizations are essential for their work. In the same study, analysts confirmed that 40% of them need more time and resources to create compelling interactive visualizations. This can be interpreted in a lot of ways. However, I'll focus here on the productivity angle. Since analysts need more time, they should be better prepared for effective data visualization and avoid wasting time for multi-iteration cycles. Knowing a few tricks can help you become a better analyst and trusted partner.


There are two critical components to successful interactive visualization:

  • Intuitive User Interface: It should have a user-friendly interface that allows users to interact with the visualization effortlessly. Clear instructions, intuitive controls, and familiar interaction patterns create a positive user experience.

  • Interactivity: The visual should offer interactive elements that enable users to explore the data, change perspectives, and discover insights. Features like filters, drill-down options, tooltips, and zooming capabilities enhance user engagement and facilitate deeper exploration.

In the last three articles, we focused on the seven (7) principles of Mastering the Art of Data Visualization, followed by the first four principles with a closer look at what it takes to get the correct data, remove noise, select the right visuals and it's visual type optionality fitting your findings/ insight. In this article, I want to focus on the second part of the principles that help understand how to focus the audience's attention, eliminate complexity and build trust with your audience.


I'll review each, stating the most common challenges, best practices, and examples before we conclude.


Challenges with bringing the focused attention:

  • Determining the most critical insights or key messages to convey through the visualization and effectively highlighting them to direct the audience's attention.

  • Managing the visual hierarchy, such as choosing appropriate visual cues (color, size, position) to guide the audience's focus on the relevant information.

  • Avoiding visual clutter and overcrowding, ensuring the visualization is clean and uncluttered to prevent overwhelming the audience and diluting the main message.


The approach includes three areas that could significantly improve your output:


  • Color contrast - color can be both signal or noise. Yet, this is still one of the most powerful tools available. Used strategically can help your audience might not otherwise to see. Refrain from over-indexing on the color itself. It's all about color contrast. Let's say you have a long chain of numbers where you need to count a number of fives. It would be easier to do with the middle set due to the color contract. Highlight all fives in blue while everything else is gray instead of having multiple colors used here. Is it easier to count them?

  • Text in explanatory titles can help tell the story. A straightforward way to make a point with few words. However, you should use the text strategically to drive attention through titles and annotations. You can use a descriptive approach writing "Sales reps by monthly revenue" or "Commission spend by location." These are alright, but it could be better if you shift towards explanatory. You could capitalize on the titles to underline the importance of that fact across visuals. You could bundle the information into something that reflects additional insight for your audience, changing the title to the outcome of your visual like "US-based team has 10 out of 20 best-performing reps globally". With that, when someone sees the commissions on the next slide/ chart, they can easily connect the dots.

  • Topography helps focus attention using typographic elements like weight, size, and color (again, it's about color contrast). Modifying one or more typographical elements aims to create a noticeable contract between the highlighted words or numbers and the rest of the text. The strategic use of typography can make your visuals easier to scan so your audience can quickly get the essence of your story point. Let's say you have a different size of GDP growth between several countries, and you want to highlight the importance of the Canadian growth that stands as an outlier. Instead of waiting for your audience to ask you questions or try to figure out which dot is which and why it's where it is, you could leverage text in the chart title, adding a title that says Canadian economy growth is 5% y/y, highlighting in red while on the topography chart you depict all countries in light gray while Canada is highlighted in red. With that, you guide your audience to the conclusion instead of letting them spend time for a couple of minutes doing so on their own.

  • Layering can help break up a complex chart into manageable parts. It's like guiding your audience through your disciplined thinking process. That can help you to keep them on track with the discovery process you went through while analyzing your data. You also give the audience to digest the information in a controllable way. Suppose you have a hypothetical situation where you must analyze the cost per opportunity creation between multiple Go-To-Market teams. Each of them will have a slightly different cost type, so explaining that to your audience while you layer one trend line after another from marketing, sales, SDR/ BDR, and partnership will settle better. Your execs will understand that there are different underlying costs, your team's output, and other parameters before they see all trendlines with slightly different colors. The same chart could highlight common events affecting all these functions, e.g., investments made by the organization.


Challenges making visuals approachable:

  • Simplifying complex data and concepts into easily understandable visual representations, considering the target audience's level of familiarity and expertise.

  • Choosing suitable chart types, configurations, and design elements align with the audience's preferences and cognitive abilities.

  • Ensuring consistency and coherence in the visual design, including clear labeling, appropriate use of color, and intuitive layout, to facilitate comprehension and engagement.

The approach includes three areas that could significantly improve your output:

  • Labeling is equally essential to good chart design. While too much labeling can add noise to a chart, a minimum level will always be required. However, this can sometimes be done in different ways. A typical chart, by default, creates labels kept at the bottom of the x-axis. However, this might be relevant for some data I often find more insightful to have labels within the chart. Let's say we have three regional organizations in a larger company, and we try to show how their revenue is performing over the last eight (8) quarters. It's easier to put regional labels into the area with values. It helps your audience keep their eyes in one place rather than jumping between the legend and graphs. The more these lines appear on this chart, the more likely the audience will start asking more questions.

  • Formatting considerations help you to make your information more approachable and digest easier based on the core theme of the analysis. Let's say you have a list of MQL generated by your marketing team by the last touch channel. Your visual tool settings will likely put the y-axis-based bar chart alphabetically. However, if you sort it based on the values of the chart bar, you are already making the data more approachable. Yes, this is not magic but a light touch that can make a difference if you try to make a point.

  • Convention Adherence is about gaining attention through a straightforward design to consume. My favorite example comes from the direction. We know how the magic quadrant works. Typically we associate the left and downward directions with negative values and the right and upward directions with positive ones. The top right quadrant is frequently seen as the most desirable location in a quadrant analysis. Positioning your positive results of completed projects in the wrong quadrant might misconstrue the message or need to be clarified for your audience. Make sure that you play to the practice of your message receiver side.

  • Imagery helps us to use photos and icons selectively to engage our audience. This works better with specific types of audiences than with others. I'd recommend using these only when presenting financial results to your board of directors. However, when you work with sales teams, that might be more appropriate as this audience often makes everything about the relationship with their prospects or customers. So you must present the volume of freemium signup across your enterprise products. Since you have several products offered through multiple segments and product versions, it's hard to remember all of them. Consider using icons next to the sorting form, the highest to the lowest volume per product. People might need to remember which exactly this product is, but once you put the icon, they can associate it with the right part of the portfolio and remember it. Another supportive element would be to reflect a few individuals working or using this product in a picture-like slide with the key text highlight and percentage of the total freemium signup associated with the product. With that, everyone will remember who is the winner of this game.

  • Real-World Examples is my favorite approach, as you can relate your insight to the real world. Many individuals need help understanding the magnitude of volumes or values. Let's say you have an exceptionally good pipeline generation quarter reflected in a simple conversion from the MQL to a first-stage opportunity/ deal. The conversion is around 42%. Well, this may be obvious to those who deal with this every day, but plenty of people do not understand the impact of that number. However, once you refer to Steph Curry's current 3-point his comparing percentage to our MQL conversion, we pass a very different message. First, we say we play at the top level in this space, but we also have a good reason to be happy about the outcome. It's simple and works in many other situations as well.

Challenges making your audience trust harder:

  • Ensuring data accuracy and integrity by validating and verifying the data sources, cleaning and transforming data as needed, and documenting the data processing steps.

  • Providing clear context and explanations for the visualization, including data definitions, assumptions, limitations, and potential biases, to establish transparency and avoid misinterpretation.

  • Considering ethical considerations and avoiding visual manipulation or distortion that could mislead or deceive the audience, maintaining trust in the visual representation of the data.

The approach includes three areas that could significantly improve your output:


Truncated axis. This is a classic oversight that mislead the audience. Let's say you try to compare the three sales team bookings. Each of them contributes with a different number, but your chart shows the starting point of the values on the y-axis that is not a zero. That means visually, your team with the lowest number might show a minimal contribution while this is still a lot, but less than two others. Instead, you could present the same data starting your y-axis with zero, adding an average line across the bars. That way, you don't debase one team's contribution and add reference points for all teams with a little average line across all bars.

Overstated axis scale or aspect ratio. This is a similar problem to the truncated axis. Suppose you have an SDR phone call that must be reflected in a trend line between different teams across your segments. Since the teams have a similar trend line, your chart looks like having several overlapping lines without the ability to call much difference. You could zoom in and adjust your range of values to reflect the difference. With that, you point out the factors in your narratives that make a difference without anyone questioning your recommendations.

Missing sources is probably the most prevalent scenario I've seen in meetings. Everybody gathers and reviews the data for 40 min. Finally, one number triggers a question, "Where is this data coming from?". Depending on the answer, this meeting might end in the next five minutes or continue and end with conclusions and a call to action. If you plan to surprise your audience with the new set of data or insight, please make sure that you clarify where the data is coming from. Your system combines data sets, big query-like systems, or research. Be ready to put it out beforehand instead of struggling to explain this verbally during the meeting. Avoiding these conversations through transparency will make you credible.


Conclusion: The biggest enemy is time in an analyst's life. Your job is critical and expects overcommit and outperforming. This is a tough racket that many who have yet to do in the past might need help understanding. Your toolbox must have many tricks and shortcuts to fit the expectations. This might be more unilateral to every job these days, but considering that people make strategic decisions based on your data, your job is unlike any other. The analyst is like a cook or chef in a restaurant. If you are in a small or medium company, you want to ensure that people can see basic, fundamental metrics and a few more sophisticated ones. If you are an analyst in a big company, you know that most analysts run basic metrics in your global organization's entire army. That's why your focus shifts towards a sophisticated approach to metrics, measurements, and visuals. Both analyst cook and analyst chef have one thing in common. They feed the company with the data so executives can make the best decision. To do so, you need to be more than just a data junkie, often putting yourself in the shoes of those who make these decisions. There is no better form of reflection than creating compelling data visuals that help them make these decisions fast. In the end, time matters to both of you.

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