Master Data Storytelling: Avoid the 10 Common Mistakes

Data storytelling is a powerful technique that allows us to transform raw data into compelling narratives, driving understanding, insights, and decision-making. When done effectively, data storytelling has the potential to captivate audiences, convey complex information, and inspire action. Despite data storytelling being fairly straightforward to implement in consulting practice, it’s all too easy to fall prey to common mistakes that can hinder the impact and effectiveness of our narratives.

In this article, together we will explore 10 common data storytelling mistakes that are easily made by even the most experienced storytellers, and how to avoid them. From misinterpreting data to choosing the wrong visualizations, these pitfalls can undermine the credibility and clarity of our data stories. Alongside each mistake, I will provide practical guidance and strategies on how to avoid them, enabling you to craft data-driven narratives that resonate with your audience and drive meaningful outcomes.

Whether you’re a seasoned data storyteller looking to refine your skills or a beginner embarking on your data storytelling journey, this article will serve as a valuable resource to help you steer clear of these common pitfalls. Let’s dive in and unlock the secrets to impactful data storytelling by learning from these mistakes and mastering the art of data-driven narratives.

Not knowing the difference between exploratory vs explanatory analysis

Not knowing the difference between exploratory and explanatory analysis can have a negative impact on data storytelling. Before you learn why, let’s go over what the difference between the two is.

What’s the difference between exploratory and explanatory data analysis?

Exploratory analysis involves initially exploring and understanding the data without any preconceived notions or specific hypotheses in mind. It helps to uncover patterns, trends, and relationships in the data. Explanatory analysis, on the other hand, focuses on explaining a specific phenomenon or outcome based on the available data. Both of these are greatly explained by the author of Storytelling with Data: A Data Visualization Guide for Business Professionals, Cole Nussbaumer Knaflic, in this video at Google.

If I were to contextualize in terms of the reporting medium, exploratory analysis would be like sending your client a link to their Google Search Console, Google Analytics, Looker Studio Dashboard, or any third-party tool export. Of course, you can do this regularly – the point here is that this enables them to dig into the data as opposed to you providing them with insights, commentary or actions. On the contrary, when you do a monthly, or quarterly report, you take the time to go through all the data at your disposal, the work completed by you or your team, the results achieved, and so forth; you analyze this data, and ideally put forth a data story to explain, enlighten, and educate your audience, and inform next steps. Likely, you’d also be doing this when hit with an external event, such as in SEO – an algorithm update, new SERP feature launched, or an industry shift like Google’s new generative AI experience or the introduction of ChatGPT or Bard.

If you confuse exploratory findings with explanatory ones, or vice versa, it can lead to confusion and lack clarity in your data storytelling. Confusing the purpose of dashboards with that of a static point-in-time report can undermine your authority as a data consultant.

Lazarina Stoy

Presenting exploratory findings as explanations can lead to Causal Inference Issues

Exploratory analysis is primarily concerned with discovering patterns and relationships within the data. It often involves generating hypotheses that can be further tested in explanatory analysis. Explanatory analysis, however, aims to establish causal relationships and provide explanations for observed outcomes. If you mistakenly present exploratory findings as causal explanations, it can lead to incorrect or misleading conclusions.

Confusing exploratory with explanatory data analysis can lead to Misleading Interpretations

Exploratory analysis allows for flexibility and open-mindedness in exploring data. It involves testing multiple hypotheses and considering various factors. Explanatory analysis, on the other hand, requires a more focused and rigorous approach to establish causality and explain the observed outcomes. If you mix up the two, you may present preliminary findings or correlations from exploratory analysis as definitive explanations, which can mislead your audience.

Mistaking exploratory for explanatory data analysis can lead to Loss of Storyline in your data story.

Data storytelling involves creating a narrative that engages and informs the audience. It requires a logical flow and a clear storyline. Exploratory analysis is an important part of the data exploration process, but it may produce multiple hypotheses and fragmented findings. Explanatory analysis, on the other hand, helps to refine and narrow down the focus to provide a coherent and compelling storyline. If you fail to distinguish between the two, your data storytelling may lack a cohesive narrative, making it difficult for your audience to follow and understand the insights you are trying to convey.

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To avoid these pitfalls, it’s important to clearly differentiate between exploratory and explanatory analysis. Give proper context when presenting exploratory findings and clearly label them as initial observations or hypotheses that require further investigation. Reserve explanatory analysis for establishing causal relationships and providing well-supported explanations. By doing so, you can ensure that your data storytelling is accurate, informative, and engaging.

Not understanding your data

Every data story starts with the data – it’s your role as a data analyst, SEO analyst, or consultant to analyze the data before choosing your data narrative. Not understanding one’s data can significantly hinder data storytelling efforts in several ways, including providing a false interpretation or failing to recognize the context or appropriate next step

Lack of data understanding can lead to Inaccurate Interpretation

Without a clear understanding of the data, you may misinterpret or misrepresent the insights it holds. This can lead to incorrect conclusions or flawed narratives, undermining the credibility of your data storytelling.

Lack of data insights will undoubtedly lead to a lack of context

Understanding the context of your data is crucial for effective storytelling. Without context, you may fail to recognize the underlying factors influencing the data, such as timeframes, external events, seasonality, and others, leading to your storytelling lacking depth and failing to provide a comprehensive understanding of the situation.

Lack of data understanding will lead you to paint incomplete or incoherent storylines

Data storytelling aims to weave a narrative that connects different data points in a coherent story, with conflict, set-up, and resolution. When you don’t understand your data, you may struggle to identify the key insights, patterns, or relationships necessary to construct a compelling storyline. This can result in fragmented or confusing narratives that fail to engage and persuade your audience. The biggest negative outcome of this will be your inability to inspire action with your data story.

Failure to comprehend the data will lead to an inability to answer questions, eroding your perceived authority.

During the presentation of data stories, questions may arise from your audience. If you lack a deep understanding of the data, you may struggle to provide accurate and meaningful responses. This can undermine your credibility and leave your audience unsatisfied, diminishing the impact of your storytelling efforts.

Not knowing the full picture might translate into missed data storytelling opportunities

Data often hides valuable insights and opportunities. Failing to leverage the potential of your data can result in missed opportunities to inform, persuade, or drive actionable outcomes.

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To mitigate these challenges, it’s crucial to invest time in understanding your data thoroughly. Explore its sources, quality, limitations, and underlying context. Document all of these insights and provide supporting documentation to your report.

Apply appropriate data analysis techniques and consult with domain experts if necessary. By gaining a deep understanding of your data, you can enhance the accuracy, coherence, and effectiveness of your data storytelling efforts.

Choosing the wrong charts and visualizations

Not understanding data visualization principles and choosing the wrong chart can have negative consequences for data storytelling. You might confuse your audience at best and mislead them at worst. Each chart selection you make conveys a message on its own, so making appropriate choices here is key to building a cohesive report.

Ineffective chart choice can lead to the misrepresentation of data

Choosing the wrong chart or visualization technique can lead to a misrepresentation of the data. Different charts are suitable for different types of data and relationships yu may be trying to signify. Using an inappropriate chart may distort the information, making it difficult for the audience to understand the true message behind the data.

Choosing the wrong charts can lead to data confusion and misinterpretation

When the chosen chart does not effectively convey the intended message, it can lead to confusion and misinterpretation. The audience may struggle to grasp the key insights or draw accurate conclusions, undermining your storytelling efforts.

Wrong charts can lead to a lack of clarity regarding the next steps

Clear and concise communication is crucial for effective storytelling. If you fail to choose a visualization that conveys the information clearly, your story may lack clarity. This can result in a loss of engagement and failure to communicate the intended message effectively.

Choosing inappropriate charts for your data can signal inefficient communication

The purpose of data visualization is to simplify complex information and communicate it efficiently. Choosing the wrong chart can hinder this process, leading to inefficient communication of the data. The audience may become overwhelmed or struggle to extract meaningful insights.

Choosing the wrong visualization can lead to missed insights

Different charts excel at highlighting specific patterns or relationships in the data. Choosing an inappropriate chart may obscure or downplay important insights that could contribute to a more impactful story. Consequently, you may miss opportunities to inform, persuade, or uncover significant findings.

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To mitigate these issues, generate a good understanding of visualization principles and techniques. Consider factors such as the data type, relationship with other metrics and influence, and the message you want to convey.

Choose the most suitable chart that aligns with your storytelling goals and effectively represents the data.

Seek feedback from others or consult visualization experts to ensure that your chosen visualizations enhance rather than hinder your data storytelling efforts. Always iterate on your designs for certain data storytelling narratives and reports to ensure you have the best one, depending on your stakeholder’s preferences.

Choosing a confusing color scheme

Choosing a confusing color scheme can have a detrimental impact on your data storytelling efforts in the following ways:

Choosing the wrong color scheme can lead to misrepresentation of data

Colors play a crucial role in representing different data categories or values. If you choose a confusing color scheme, it can distort the perception of the data. This misrepresentation can lead to incorrect interpretations, or even various associations that people have (depending on their culture) with certain colors.

Check out the infographic demonstrating color-culture associations of different countries.

Ineffective use of colors can lead to lack of differentiation of important metrics

Effective data stories require a protagonist – a metric, or set of metrics, such as traffic, user experience, or otherwise, which are followed throughout the story. A confusing color scheme can make it challenging to differentiate between different elements, such as data series or categories. This lack of differentiation can create confusion and hinder the audience’s ability to understand the relationships or patterns in the data.

Choosing a color scheme for design purposes alone can lead to visual fatigue and overwhelm

When a color scheme lacks coherence or harmony, it can create visual fatigue and overwhelm the audience. Too many clashing or poorly contrasting colors can strain the viewer’s eyes and make it difficult to focus on the key insights or messages in your data storytelling.

Choosing inappropriate colors can lead to accessibility issues

An unclear color scheme can create accessibility challenges, particularly for individuals with color vision deficiencies. Colors that are too similar or lack sufficient contrast can make it difficult for these individuals to distinguish between different elements, excluding them from fully engaging with and understanding your data storytelling.

An improper color scheme can lead to loss of emotional impact

Colors have the power to evoke emotions and convey meaning. Choosing a confusing color scheme can diminish the emotional impact of your data storytelling. Colors that are incongruent or fail to align with the intended message or tone may dilute the emotional resonance of your data story.

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To avoid these pitfalls, consider the following guidelines when choosing a color scheme for your data storytelling:

• Use a limited and harmonious color palette that enhances clarity and differentiation.
• Ensure sufficient contrast between colors to facilitate easy comprehension of your protagonist metrics.
• Consider the cultural and contextual associations of colors to align them with the intended message.
• Test the color scheme for accessibility, ensuring it is inclusive for all viewers.
• Leverage color psychology to enhance the emotional impact of your storytelling, aligning colors with the intended tone or message.

By being mindful of these considerations and selecting an appropriate color scheme, you can enhance the clarity, comprehension, and emotional resonance of your data storytelling efforts.

Choosing an inappropriate report medium

I already discussed how the type of report you are presenting (be it exploratory or explanatory) can influence your choice of report medium. But also, this choice of how you present the report should be influenced by your understanding of your stakeholders. Choosing an inappropriate report medium can negatively impact your storytelling efforts.

The core of the analysis should influence the choice of how the data story is presented, otherwise, you risk having limited audience engagement

Different data stories require different mediums to effectively convey the message. If you choose an inappropriate report medium, such as a lengthy written report for a visually driven story, you risk limiting engagement. The audience may find it difficult to connect with the information, leading to disinterest and reduced impact.

This, of course, is highly dependent on your stakeholder or client, and their typical reporting or communication routine.

When I did organic marketing consulting with AWS teams, I knew the importance of written reports for that organization and that that was their typical reporting routine, so I took that into my reporting practice with those teams, despite it being contradictory to my typical practice with other clients at the time.

Lazarina Stoy

Different audiences have varying preferences for consuming information. Choosing an inappropriate report medium may fail to align with your audience’s preferences, hindering their ability to connect with and understand the story.

Choosing the wrong report medium can lead to inefficient communication

Ineffective report mediums can impede the efficient communication of your data story. For instance, if you choose a medium that lacks interactivity or dynamic elements for a complex dataset, it may make it challenging for the audience to grasp the insights effectively. This can result in a loss of comprehension and failed communication of the key message.

Choosing the wrong report medium can hinder your ability to make an impact with your data story

Different mediums have different strengths in conveying specific aspects of the story, such as interactivity, visualizations, or storytelling elements. If you don’t leverage the appropriate medium, you may fail to leverage its potential to engage, persuade, or inspire action, thereby diminishing the overall impact of your storytelling.

Some report mediums can lead to reduced memorability of your data story

The choice of report medium can impact how memorable your data story is to the audience. An inappropriate medium may fail to leave a lasting impression or make the information easily recallable. This can limit the potential for your storytelling efforts to have a long-term impact or influence decision-making.

This is affirmed by research conducted in the data visualization community, whereby the largest scale visualization study to date using 2,070 single-panel visualizations, categorized with visualization type the insights show:

• choosing the right color scheme can influence memorability and data perception
• choosing less common and more customized chart types to the data story can influence memorability positively

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To mitigate these challenges, consider the following steps when selecting a reporting medium:

• Understand your audience and gain insights into their preferences, expectations, and needs to choose a medium that resonates with them.
• Consider the nature of your data story and the most effective way to convey its essence. Match the medium with the content to enhance its clarity and impact.
• Explore available technologies or tools that can support your chosen report medium. Use interactive features, visualizations, or multimedia elements to enhance engagement and comprehension.
• Seek feedback from a diverse group of stakeholders or test your chosen report medium before finalizing it. Incorporate their insights and suggestions to refine your storytelling approach.

By carefully considering the appropriate report medium for your data story, you can maximize engagement, improve comprehension, and enhance the overall impact of your storytelling efforts.

Not providing enough context for your audience

Not providing enough context for your audience could relate to not educating your audience on how the report was built, how data was gathered, what metrics mean, or otherwise – the issue of lack of documentation.

I’m not saying that data documentation needs to be presented in person, or as part of the main event. I’m just saying someplace, somewhere – your stakeholders need a one-stop for all their questions on the data context to get everyone to speed.

Lazarina Stoy

Lack of context can lead to lack of understanding of your data story

Without sufficient context, the audience may struggle to grasp the significance of the data being presented. They may be unclear about the purpose, background, or relevance of the information, leaving the audience confused or disengaged.

Lack of context can lead to misinterpretation of your data story

Context plays a vital role in interpreting data accurately. When context is lacking, the audience may misinterpret the data or draw incorrect conclusions. Key factors such as timeframes, when and where from the data was extracted can significantly influence the meaning of the data. Without context, the audience may form inaccurate or incomplete interpretations, leading to flawed decision-making or misguided actions.

Not providing context to the metrics you’re discussing can lead to the inability of your stakeholders to relate

Context provides the audience with a frame of reference that enables them to relate the data story to their own experiences or situations. When context is absent, the audience may struggle to connect the data to their own lives, hindering their ability to find personal relevance or meaning in the story. This is particularly relevant for presenting a data story, based on metrics that not everyone in the audience is familiar with – how can you expect for someone from engineering or product to be engaged with your storytelling, related to bounce rate if they don’t even know what that is or why it’s important?

Context often provides insights into the underlying causes, trends, or patterns within the data. Without this information, the audience may miss out on valuable insights that could enrich their understanding and appreciation of the story. By withholding context, you limit the audience’s ability to gain a comprehensive view of the data and extract meaningful takeaways.

Not understanding how the data was extracted or data story built can lead to Increased Skepticism

When the data context is lacking, the audience may become skeptical of the data story’s credibility or reliability. They may question the validity of the information or view it as biased or misleading. Without the necessary context, the audience may be less inclined to trust the data and the narrative being presented.

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To address these challenges, it is crucial to provide sufficient context when presenting data stories:

1. Clearly communicate the purpose, background, and objectives of the data story upfront. Explain why the information is important and relevant to the audience.
2. Share any necessary contextual details such as timeframes, geographical factors, or historical trends that influence the data. Help the audience understand the broader context in which the data exists.
3. Transparently communicate the sources of the data and any limitations or biases associated with it. This helps the audience understand the data’s credibility and potential limitations.
4. Connect the data story to the audience’s experiences, challenges, or aspirations. Highlight how the insights are relevant to their lives or work, fostering a sense of personal connection.
5. Provide clear and actionable insights that the audience can apply in their decision-making or problem-solving. Explain how the data story can contribute to positive outcomes or drive meaningful change.

By providing sufficient context, you enable the audience to better comprehend, relate to, and act upon the information presented in your data story.

Not knowing your audience well enough

Beating the wrong drum, or advocating for the right outcome but to the wrong people. If you don’t know the stakeholders, you’re not going to be able to communicate urgency well enough to them, ending up boring them and hurting your credibility. Let’s see how not knowing your audience well enough can have detrimental effects on your data stories.

Not knowing your audience can lead to ineffective communication or misallignment

When you lack a deep understanding of your audience, you may fail to align your data story with their specific needs, interests, and preferences.

Different audiences have different levels of familiarity with data and varying levels of expertise in the subject matter. Not knowing your audience well enough can lead to ineffective communication of your data story. This can result in a mismatch between the message you’re trying to convey and what resonates with your audience or you may use jargon, technical terms, or complex visualizations that are not suitable for your audience’s level of understanding.

Not knowing who you’re presenting your data story to can lead to lack of relevance

Understanding your audience enables you to identify the specific relevance of your data story to their context or challenges. If you fail to grasp their unique circumstances, you may present data that is not directly applicable or meaningful to their situation. This lack of relevance can make it harder for the audience to see the value or significance of the information being presented.

Data storytelling is not just about presenting information—it’s about forging a connection with your audience. When you don’t know your audience well enough, you may miss opportunities to establish an emotional connection, address their concerns, or tap into their aspirations.

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To mitigate these challenges, invest time in getting to know your audience better:

1. Gather information on who will be at your presentation to understand their role, interests, and needs.
2. Develop personas that represent different segments of your target audience, e.g. c-suite, project leads, or team members. Use these personas as a reference to tailor your data stories to specific groups.
3. Encourage feedback from your audience and engage in meaningful conversations. Actively listen to their perspectives, challenges, and expectations to refine your data storytelling approach. Adapt your storytelling techniques, visualizations, or narrative approaches to better resonate with their preferences and understanding.
4. Recognize that your audience may evolve over time. Stay updated on their changing needs, preferences, and dynamics, and adjust your data storytelling approach accordingly.

By knowing your audience well and tailoring your data stories to their specific context, you can enhance engagement, relevance, and the overall impact of your storytelling efforts.

Not providing commentary or informed analysis of performance 

Not providing commentary or analysis of performance can undermine the effectiveness of your data storytelling.

Simply sending out a bland stats dump, regardless of whether it’s in a Looker Studio dashboard or fancy deck is not the way to do storytelling with data.

Lazarina stoy

Leaving it to your audience to derive the same insights from the data as you can lead to misalignment

Data alone does not tell a story; it requires interpretation to derive meaning and insights. By omitting commentary or analysis of performance, you deprive your audience of the context and understanding necessary to make sense of the data. They may be left with raw numbers or visuals without a clear understanding of what they signify, resulting in confusion or misinterpretation.

Presenting a data story without analysis and insights is like making an omelet without eggs

Commentary and analysis are essential for identifying patterns, trends, or outliers within the data. Without this analysis, your audience may miss out on valuable insights that could drive decision-making or highlight significant findings. The story may lack depth and fail to uncover the actionable takeaways or key messages that the data has to offer.

Lack of insights can lead to reduced Persuasion and actionability

Data storytelling often aims to persuade or influence the audience’s perceptions, beliefs, or actions, but most importantly to drive decision-making and action. Commentary and analysis play a crucial role in shaping these persuasive narratives. Without them, your data story may lack a compelling argument or fail to convince the audience of the significance or implications of the data. The story may lack persuasive power or a call to action, diminishing its impact and hindering its ability to inspire change.

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To avoid these pitfalls, ensure you provide commentary and analysis of performance in your data storytelling:

1. Explain the background, objectives, and significance of the data being presented. Help your audience understand why the performance metrics matter and how they relate to the broader context or goals.
2. Analyze the data to identify patterns, trends, or relationships that are relevant to the story. Highlight significant findings and explain their implications or potential impact.
3. Clarify any anomalies, outliers, or unexpected results in the data. Offer plausible explanations or hypotheses to help the audience make sense of these observations.
4. Translate the data into actionable insights or recommendations. Connect the performance metrics to specific actions or strategies that can drive improvement or change.
5. Weave the analysis and commentary into a coherent and persuasive narrative. Tell a story that captivates the audience, connects with their emotions, and drives them to action.

By incorporating commentary and analysis into your data storytelling, you can enhance comprehension, engagement, and the ability to inspire meaningful action based on the insights derived from the data.

Not providing a way out of a catastrophe or a way to capitalize on success (missing next steps)

Not providing a way out of a catastrophe or a way to capitalize on success, or otherwise – presenting. a data story with missing next steps can undermine the effectiveness of your consulting.

Don’t leave your client to come up with their action items or next steps – it’s your role as an analyst or consultant to present an analysis and inform them of the recommended next steps, especially when presenting a data story.

Lazarina Stoy

Without actions, you’ll never be able to execute proper data storytelling

Data stories should not just present historical or current data; they should provide a roadmap for the future. By neglecting to offer the next steps or actionable recommendations, your data story remains incomplete. Without this guidance, your audience may feel lost, which can lead to inaction, missed opportunities, or ineffective decision-making.

Missed next steps translate to missed opportunities for problem-solving or capitalizing on success

Data stories often aim to address problems or challenges. Not providing a way out of a catastrophe deprives your audience of potential solutions or strategies to overcome difficulties. This can leave them feeling helpless or frustrated, as they lack the necessary direction to address the issues at hand.

Similarly, success should be celebrated, but it should also be leveraged to drive further progress or growth. By failing to provide ways for capitalizing on success, you risk stagnation or missed opportunities for continued advancement.

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To avoid these challenges, consider the following when including next steps in your data storytelling:

1. Provide specific, actionable recommendations based on the insights derived from the data. Clearly outline the steps or strategies that should be taken to address challenges or capitalize on opportunities.
2. If there are multiple next steps to consider, prioritize and sequence them based on urgency, feasibility, or impact. Help your audience understand the logical progression of actions to be taken.
3. In the face of a catastrophe, present alternative scenarios or contingency plans. This provides a sense of resilience and options for the audience to consider when dealing with challenging situations.
4. Support your next steps with practical advice or guidance on how to implement them effectively. Address potential barriers or challenges that may arise and offer solutions or workarounds.
5. Encourage your audience to actively engage with the next steps and collaborate on finding solutions. Create opportunities for discussion, feedback, or brainstorming to drive collective problem-solving or decision-making.

By including action points in your data storytelling, you empower your audience to take action, address challenges, and seize opportunities. This helps to complete the narrative and enhance the relevance, impact, and effectiveness of your data story.

Being afraid of the conversation

Being afraid to start a conversation or anticipating a response and not giving your audience a chance to respond can be limiting to you as someone interested in data storytelling. This can mean anything from sugarcoating performance or shying away from reporting on projects when things did not go as planned, to skipping out on conversations as you think you know what the outcome of the call will be.

Being selective about the data stories you tell can hinder your authenticity

Data storytelling is most effective when it comes from an authentic and genuine place. If you’re afraid of the conversation, you may hold back or filter your message, resulting in a lack of authenticity.

Applying your pre-conceived notions of how the presentation would go leads to missed opportunities for impact

Engaging in conversations allows for a deeper understanding of your audience’s perspectives, concerns, and needs. By avoiding these conversations out of fear, you miss opportunities to gather valuable insights on the reasons why certain projects are a ‘NO’. Your fear may prevent you from fully understanding and addressing the issues that matter to your audience, leading to incomplete storytelling, where crucial aspects or uncomfortable truths are left unexplored.

By limiting your data stories to your subjective interpretations of importance you’re also limiting your growth and learning

Conversations provide an opportunity for growth and learning. By embracing the conversation, you open yourself up to feedback, constructive criticism, and alternative perspectives. These insights can help you refine your data storytelling skills, challenge your assumptions, and broaden your professional horizons. If you’re afraid of the conversation, you may miss out on valuable opportunities for personal and professional growth.

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To overcome the fear of telling challenging data stories and improve your data storytelling:

1. Acknowledge that it’s natural to feel apprehensive or uncertain when engaging in conversations, particularly around sensitive or complex topics. Embrace vulnerability and recognize it as an opportunity for growth and learning.
2. Create a safe and inclusive environment where open dialogue and diverse perspectives are encouraged. This can help alleviate fear and encourage more meaningful conversations.
3. Engage in active listening during conversations. Pay attention to not only the words spoken but also the underlying emotions and concerns. This will enable you to respond more effectively and address the specific needs of your audience.
4. Continuously educate yourself on the topics you discuss in your data storytelling. The more knowledge and understanding you have, the more confident you’ll become in engaging in conversations and addressing challenging subjects.

Remember, embracing conversations and overcoming the fear associated with them is an essential part of growth as a data storyteller. It allows you to connect with your audience more authentically, create a greater impact, and continuously improve your storytelling abilities.

Key takeaways on data storytelling challenges and mistakes to avoid

Data storytelling involves multiple skills, that span from more technical like data extraction and analysis, to creative like data visualization to soft skills, like active listening and narrative design. In this guide, we’ve discussed the most common mistakes people make in data storytelling and provided solutions for these challenges.

Mistake	How to Recognize It	What to Do Instead
Not knowing the difference between exploratory vs explanatory analysis	Confusion between presenting data for exploration vs. explaining specific outcomes	Clarify if the data presented is for exploring patterns or explaining phenomena. Use exploratory analysis to generate hypotheses and explanatory analysis to test these hypotheses and explain specific outcomes.
Not understanding your data	Inaccurate interpretations, lack of context, and incoherent storylines	Invest time in understanding your data thoroughly, including its sources, quality, and context. Document insights and consult with domain experts.
Choosing the wrong charts and visualizations	Misrepresentation of data, confusion, and lack of clarity in your storytelling	Learn visualization principles, choose charts that match your data storytelling goals, and seek feedback to ensure clarity and effectiveness.
Choosing a confusing color scheme	Misinterpretation and visual fatigue due to poor color choices	Use a harmonious and limited color palette, ensure sufficient contrast, and consider cultural associations of colors. Test for accessibility.
Choosing an inappropriate report medium	Limited audience engagement and ineffective communication	Understand your audience and the nature of your data story to select the most effective medium. Consider interactivity and preferences.
Not providing enough context	Lack of understanding and misinterpretation of the data story	Provide necessary background, explain data sources and limitations, and connect the story to the audience’s experiences.
Not knowing your audience well enough	Ineffective communication and lack of relevance	Invest time in understanding your audience’s needs, preferences, and challenges. Tailor your storytelling to their context.
Not providing commentary or informed analysis	Raw data without interpretation or insights	Include analysis and commentary to identify patterns, explain anomalies, and translate data into actionable insights.
Not providing a way out of a catastrophe or a way to capitalize on success	Incomplete data storytelling without actionable recommendations	Offer specific next steps and actionable recommendations based on the data insights. Prioritize and sequence actions for clarity.
Being afraid of the conversation	Lack of authenticity and missed opportunities for impact	Embrace conversations, engage in active listening, and create a safe environment for open dialogue.

For me, the key takeaways for you as a data storyteller are:

• adaptive and flexible in how and when you report
• precise and selective in which charts, visualizations, colors, and report mediums you choose – based on the data and your audience
• intentional with what you include or exclude in your data story
• proactive in educating your stakeholders on the context needed for them to understand your data story

Frequently asked questions on data storytelling mistakes