Data storytelling

…all you need to know about it!

Contrary to popular assertion, data rarely “speaks for itself.” In our information-saturated world, the proliferation of data sources, data analysis, and business intelligence (BI) tools have boosted business analytics, increasing the complexity of analysis and data-driven decision making. However, these advancements have made data communication skills critical for most professionals. Specifically, data storytelling has become an essential competency for making data more understandable, visual and actionable. In this article we will explore data storytelling and introduce skills training, courses and workshops that can help you improve your data communication and data storytelling skills.

Storytelling in data analysis

Every day, businesses need to make decisions like “hire more people,” “spend less on this,” or “spend more on that” “increase productivity,” “cut this feature,” or even “merge with a competitor.” Done right, data can fuel this decision-making, identify impending danger before things blow up, and help leaders leverage the opportunities analysis reveals to grow and expand.

However, data rarely speaks for itself. Instead, data needs the help of a storyteller that can extract and explain data-driven insights and use them to build understanding, craft recommendations and inspire people to take action. Developing the ability to bring data to life using data storytelling can take any professional to an entirely new level as a valuable contributor and leader.

67% of all job roles are now data-enabled

Our information-driven societies are becoming increasingly data-oriented. Price Waterhouse Cooper recently reported that 67% of all job roles are now data-enabled. More than ever, modern employees are working with data. Even workers who don’t manage data on a day-to-day basis are increasingly working cloud-based solutions and SaaS tools with their own built-in reporting, analytics, dashboards and APIs that produce data, and potentially integrate with other data analysis and business intelligence (BI) tools.

Technology companies like Tableau and Microsoft’s Power BI have raised the data analysis game for businesses, using artificial intelligence to analyze data the same way humans do, at astounding speeds. Technologies are getting smarter about how they clean, merge, analyze, visualize, and highlight data. Machine learning (ML) and Artificial Intelligence (AI) is helping these tools extrapolate insights, generate visuals and produce predictive data that can be useful and enlightening.

The proliferation of data, data analysis tools like Tableau and Microsoft Power BI have also raised the stakes for humans. The basic ability to interpret data is no longer a strong enough skill to retain one’s job. For success, modern professionals need to understand how to leverage these tools and apply their own critical and. creative thinking make actionable recommendations based on data. People have a much bigger job to do, related to synthesizing data, extracting and communicating data insights in a way that helps solve bigger and bigger problems. This is where data storytelling skills become essential.

Data storytelling skills are critical in data-driven decision-making

Collectively we can break down data storytelling skills into the following capabilities:

  • Extracting and synthesizing insights from various data sources, analytics and business intelligence tools
  • Solving small and big problems by applying critical thinking and creative problem-solving skills to data analysis
  • Creating and presenting data visualizations, documents and slide presentations in ways that are clear, insightful and persuasive
  • Explaining data-driven insights to others in a way that informs decision-making
  • Recommending actions that should be taken in response to data
  • Inspiring people to act based on a compelling, impactful presentation of data

These skills benefit organizations by building stakeholder alignment, and speeding decision making. However, as the diagram below shows, these skills, also help individuals advance into greater levels of strategic influence as leaders that inspire action.

Storytelling using data

Leaders inspire action

Almost all data collected is generated by someone – or something – taking action. Data is collected while driving your car, submitting a receipt, clicking on an email, and swiping right (or left) in a dating app. It follows therefore, that if you change the action, the data will also change.

For example, say your organization wants to grow. Following the illustration above, to figure out how to grow, the steps may look something like this:

  1. Explore – We examine the data and find an opportunity for growth
  2. Explain – We present the insight from the data, with a recommendation on how to grow
  3. Inspire – We motivate people to act with the goal of moving the data in the desired direction

When an individual explores data, s/he consumes articles, reports and pour through charts and graphs. These activities would be futile, however, if they didn’t lead to decisions – and actions – that make the or the person’s organization stronger. In like manner, good leaders don’t just explore data – they have the ability to explain what the data is saying and inspire people to take action with clear, justifiable recommendations based on the data.

Data storytelling workshops (a shameless plug)

To support data-driven decision making, companies are investing in data communication skills, presentation skills workshops, data visualization and data storytelling workshops to strengthen internal capabilities. Duarte DataStory® workshops help professionals communicate data in more effective, meaningful and impactful ways, unlocking understanding, fueling rapid decision-making and inspiring people to take action. We teach individuals and teams how to present present data in a more effective, impactful and persuasive ways. Attendees grow from being fluent in data as individual contributors, to being inspiring leaders who turn data into action.

We will highlight more information about available training and courses that can help you build your data storytelling skills toward the end of this article. However, if you are curious now, you can find more information by clicking below.

Decisions made from data

High-stakes decisions are made from data all the time – whether it’s based on a single chart, a dashboard, or commissioned research to drive a larger strategy. There are levels of complexity related to the data, how it is presented and the decisions that are made, moving from the tactical to the highly strategic.

As the chart below reflects, the more tactical the decision is, the more simple the information presentation is likely to be. The more strategic the decision is, the more complex the data analysis may be. The latter may require more charts, data and more artful data storytelling.

Data storytelling framework

Stages of data-driven decision-making

There are two sides of data analysis, exploring and explaining data, and distinct modalities and thinking that inform the tasks involved.

Exploring vs. explaining data

Data driven decision making

Exploring data is analytical in nature

This is where we begin to review data, clean it and analyze it. This stage pulls on critical thinking skills to help you identify patterns, spot anomalies, test hypotheses, check assumptions and fix problems with data. This requires not only an understanding of data processing, storage and retrieval, but the use of business analytics tools, the ability to clean up data, and portray data in a manner that allows it to be reviewed.

Explaining data is interpretive in nature

This is where we draw inferences and conclusions about the underlying causes of observable patterns, and where we craft and sell a recommendation on what to do about the data. Creative thinking is essential in the explanatory phase – both for determining what behavior that is creating the data – and in describing this behavior. This also requires data interpretation skills, and the ability to apply reason, critical and creative thinking to interpret the data, and explain it in a manner that allows other people to understand it, make informed decisions and take action.

A distinct modality shift occurs between the two phases of data-driven decision-making.

The chart below takes a deeper dive on the steps involved in each phase, and making the shift in modality, skills and mindset more clear:

The Explore Phase of data decision-making involves diagnosing a problem or identifying an opportunity. Usually, the business has an objective, and they want to use data to make a decision based on data.
Identify decision: Leaders go to data for insights to determine both large and small decisions. In business, data either uncovers a problem or an opportunity. Leaders charged with inventing the future need to change human behavior based on the data so the trajectory of data in the future will move in a positive direction.
Process data: A Data Scientist and/or Data Engineer manages the data processing, data storage and retrieval. They might also clean the data, do feature engineering, model selection, and build data integration from multiple sources.
Analyze data: Tools like Microsoft Power BI, Tableau, and others are used in the process of data analysis. A data analyst applies statistical techniques and analysis to extract insights from data. They use descriptive statistics, data visualization, and even advanced techniques such as hypothesis testing and predictive modeling.
The Explain Phase uses explanatory analysis to highlight the insights gathered and the decisions to be made. Building and communicating recommendations requires the ability to influence others to make the decisions a reality.
Interpret insights: After analyzing data, insights emerge. An insight can be as simple as one key finding from a chart written as a slide title, or it might be a sequence of insights from several pieces of data feeding into a high-stakes decision. It’s important to visually highlight key insights and annotate them to make insights from each chart as clear as possible to build understanding.
Recommend decision: Some decisions can be made from a single chart quickly. Others require an executive summary. High-stakes decisions require a presentation or Slidedoc™ so that stakeholders understand the implications. No matter the format, clear recommendations should follow a three-act structure.
Inspire action: A recommendation requires that people to act on it, to drive desired outcomes. To inspire action, wrap data in emotion by marveling at its magnitude, humanize data points, and deliver it in the shape of a story.

Humans are essential in data-driven decisions

It’s pretty clear that human involvement is required across the spectrum of work that is done between the Explore and Explain Phases of data-driven decision-making. While the Explore Phase may use a human to tee up insights using data – the Explain phase requires a human to make a judgment call about what to do about the insight.

While some people can perform all the steps above, these steps are sometimes performed by people in different roles. From a career vantage point, however, the more an individual can do, the more valuable that individual is likely to be to an organization.

“This represents a slightly different take on the classic data-driven decision making (DDDM) model…”

The need for human involvement represents a slightly different take on the classic data-driven decision making (DDDM) model, which suggests that that people can make informed and objective decisions based solely on data – rather than relying on intuition. While sometimes this is possible, very often, it is not the case.

“Why?” you may ask. Simply put, because most business problems are highly complex. Very often, of the time, the data can only take a business leader so far. It is very hard to get “perfect data” to fuel a “perfect decision.” Sometimes datasets aren’t available or are incomplete. Sometimes datasets are available, but they may not be clean enough to inform the right response. For these reasons, business leaders must often fill in the gaps in the data the way a detective does, sifting through clues to make the most viable decision possible. In some cases (and this is not uncommon), leaders may find themselves making business decisions that prove to be “counter-moves” to what the data may suggest.

“…Business leaders must often fill in the gaps in the data the way a detective does, sifting through clues to make the most viable decision possible.”

Whether data-driven decisions are driven by quick, intuitive data found in a single chart, a result of complex analysis and discussion, or an off-the-hip counter move – in the Explain Phase, the need for data storytelling skills are essential to build understanding and consensus, explain choices and rationale, present recommendations, fuel decision making and inspire action. The moment you are ready to start explaining the insight you’ve found in the data, you’ve entered into the world of communicating data.

Once you know the decision that’s to be made, that’s when data storytelling begins. The best way to communicate almost anything is to pull on the attributes of story, and this is where humans play a critical role.

What is data storytelling?

There are many terms used related to data and storytelling, with no single framework to align the nomenclature – or the many steps that go from identifying a dataset to using data to influence decision-making. At Duarte, however, we define data storytelling very simply, as follows:

Data storytelling applies the elements of story to explain what data means and inspire people to take action.

Data-driven storytelling

We can break down the steps within the Explore and Explain phases of data-driven decision making as follows:

What is data storytelling

  1. Identify the problem: It is important to understand not only the problem, but what data is available to provide you with insight. Focus broadly to get the best data possible. Be cautious about developing an “availability bias” caused by focusing on data that’s easily available – rather than the data you should be seeking out.
  2. Process the data: Performing tasks, such as deduplication, data cleaning, or removing outliers helps improve the quality of the data as well as your data analysis results, in a manner that will simplify your data storytelling.
  3. Analyze the data: Depending on the type of data under review, there are many approaches to data analysis. Most analytics or BI tools (especially those with AI may help you “slice and dice” the views of data, and automatically choose from different chart formats (graphs, charts, bubble diagrams, etc.) that help you process the data in a visual format, and identify patterns, trends, cause and effect.
  4. Interpret the insight(s): While the quote “correlation does not imply causation,” may be popular – very often, correlation IS associated with causation. Calculating P-values can give you a better feel for how strong any noted correlation may be. As you begin to interpret the data, walking your audience quickly your analysis can bolster your credibility as you present insights.
  5. Recommend the decision: This is where your data story becomes action-driven. Once you’ve gathered insights, it’s time to answer the question, “What should we do to move the data in the right/desired direction?” This is your data-driven decision. In some cases, it may be a choice between a few options. As you prepare to present the decision to be made, keep it simple and clear for your audience.
  6. Inspire action: All the analysis in the world amounts to nothing if you can’t motivate your audience to take action. Data storytellers know how to lay out a compelling data story that leads people through the insight, the decision in a persuasive manner. This step fulfills the ultimate objective of data storytelling, and comes naturally when data is presented in a 3-act data story format.

What is a data story?

Data stories are used to clearly communicate the action that needs to be taken, based on the insight derived from data. Data stories are communicated using a three-act structure to convey information, and each properly structured data story will have a clear beginning, a middle, and an end.

Here are two storytelling examples that are simple enough for anyone to understand. These frameworks are explained in Nancy Duarte’s book, DataStory as well as the Duarte DataStory® Course

Marketing data story example:

Data storytelling examples

Above you can see a recommendation from data that has been framed as a data story. This framework makes the context, problem, and solution very clear. This is data-driven decision making at its finest. If you start communicating data clearly, in this manner, it will help you advance professionally.

HR data story example:

Data storytelling examples

The first act, or “Beginning” states the problem. The second act, or “Middle” describes the data. This can often be considered “messy” middle — because you find yourself dealing with the data you want to see move in a different direction here. The third act, or “End,” contains the resolution, and answers the question about the action the audience should take to turn the numbers around.

Storytelling data visualization

There’s a saying: “to be clear is to be kind.” This statement directly applies to storytelling data visualization. One of the best methods of making data clear is through effective data visualization, which is a primary tool supporting data-driven decision-making.

Making data visual

It is important to remember the different learning styles that will be present in your audience. Some people learn in a more auditory manner, where others are highly visual-spatial learners. Data presenters that neglect to utilize storytelling data visualization techniques run the risk losing the percentage of an audience whose brains really yearn to see the data story – not just hear it. By remaining sensitive to the needs of your audience, it is possible to become a more effective presenter and storyteller, not just in what you say – but in how you convey your message with media.

Choosing a data presentation format

As we look at ways to visually present our data, we must consider our data presentation format. If you think of a presentation merely as the oral or written delivery of information from a real-world or virtual stage, you may not be seeing the whole picture. The use of presentation software like Keynote, Google Slides, or Microsoft PowerPoint to create slides and charts isn’t limited to creating traditional presentations. We can use these tools in other ways to visualize and explain data, based on the audiences we address and our business goals.

At Duarte, we view presentations within a spectrum, as highlighted in the graphic below. The two presentation types on the right of the graphic use presentation software.

Explaining data using Slidedocs®

We’ve referenced Slidedocs® in a few sections of this article, so now, we’ll explain what a Slidedoc™ is.

Let’s say you have a great idea, and you need your co-workers to get on board. However, your idea is too complicated to explain in an email, and long-form report might not read by busy colleagues. You need something that will effectively get your point across. Something that can grab attention, explain your idea well, and even “sell it” – even if you’re not in the room. This is where using a Slidedoc becomes your best friend.

Simply defined, Slidedocs are skimmable, visual documents that effectively deliver your most important ideas. These powerful, magazine-like documents are ideal for use as a pre-reads, leave-behinds, and pass-alongs. They contain your data story as well as impactful data visualizations that can help your ideas catch on like wildfire. Slidedocs® are used by executives, managers, speakers, data analysts, technologists, project leaders, salespersons, marketers – and well – just about anyone who needs to get a point across. Public speakers also use them to summarize a talk or presentation.

In addition to slide presentations, Slidedocs are a terrific tool for data storytellers. Duarte’s DataStory® data presentation course includes high-level training on how to structure and write a Slidedoc. However, we also offer a dedicated, on-demand Slidedocs® course that teaches attendees how to design and build a Slidedoc using presentation software (Microsoft PowerPoint).

Visualizing data in slides and charts

For all slides and charts, one rule of thumb is to ensure that each individual chart makes one point very clear. You can see the benefits of doing this in the data visualization example below:

Data visualization benefits

As you attempt to visualize your data, here are the three choices you will need to make:

1. Select what chart type to use

Choose the type of chart that makes your point most effective and clear. AI-enabled tools like Microsoft BI or Tableau can actually do most of the work for you, by recommending a chart type for you and even proposing an observation to make. However, if you are working within Keynote or Microsoft PowerPoint, you can also import your data to create an array of charts and graphs, and toggle between them to choose the best one.

2. Emphasize the most important part of the chart

Either using the observation recommended by the AI in your tool, or based on critical thinking of your own, you’ll next want to visually emphasize the most important part of the chart. You can do this by applying a contrasting color to emphasize a bar or data point, or you can also use a highlight or a callout. The point of the visual emphasis is to draw the reader’s focus to that data point.

3. Write a data observation as the title

Craft a short, to the point insight about the chart and use it as the title of your slide. Some consulting firms put the key insight at the bottom of the slide as well. The objective is to make your point very clearly and prominently.

If you’re using a tool like Tableau, the steps above become more simple, as the AI in the tools will choose a chart type and propose an observation for you. As you look to present analysis to your audience, you might only need one chart to create a compelling recommendation. However, most “hairy” business problems, usually involve a collection of charts from multiple data sources.

Other data visualization methods

If you’d like to add even more skills to your data visualization tool belt, Duarte also offers other training in the areas of data visualization and visual thinking and slide and presentation design. These courses will help any professional communicate and present data with greater clarity and impact.

Effective data storytelling

Effective data storytelling techniques, data visualizations and the use of data stories can elevate any data-driven culture by shifting focus to turning data into actionable insights. This will not only foster greater organizational alignment, it will speed decision-making and action.

Making data actionable

The chart below describes how executives use data and the tools and methods that can support data-driven decision-making.

(one chart)
(multiple sources)
How executives use data: This is like “sonar data” which signals the current state. Executives use this data to assess whether the data is moving in the right direction at a highly practical and tactical level. CEOs and executive teams review key metrics and critical insights that can make, or break, an organization. This is typically viewed within a central reporting dashboard, which synthesizes and summarizes reporting and/or dashboards used by other business units and departments. Data comes from a myriad of internal and external data sources, delivered in various formats from different systems and sources, including individuals, or consultants. The goal is to provide an inflow of fresh perspective and insight to fight stagnancy, because stasis equals death. This data analysis may be less crisp and straightforward.
Tools: Many common desktop productivity tools let you plot charts from simple (and complex) data sets like Microsoft Excel and PowerPoint and Numbers and Sheets for Mac Users. Enterprise-wide business analytics tools like PowerBI, Tableau and Google Data Studio visualize data from several sources with interactivity so you can drill into and manipulate charts. There isn’t one tool to rule them all here. Data typically comes from multiple sources, including internal data, acquired research from analysts, white papers, external data sources, research and consultants, and/or from slicing your own internal data in new ways.

Examining how executives in your organization use and access data, and improving how this is done through the use of better tools, analysis and dashboards can help build a data-driven culture within your organization.

However, when a data-driven culture is not complimented by strong data communication skills it is easy for data-driven organizations to become mired in data-driven detail. This can make building a uniform understanding and interpretation of data incredibly difficult, and complicate an organization’s ability to formulate clear recommendations and direction from data. Adding data storytelling skills can break up the data “logjam.”

Data storytelling techniques

Data storytelling techniquesIn Nancy Duarte’s book DataStory she details the best data storytelling techniques. Below are just five of the data storytelling techniques she recommends:

  • Search for the “hero” and the “adversary” in the data: Identify the people whose actions generate the data and who can influence the outcome. These are the characters in your story.
  • Speak with the people generating the data: Interview the data-generating people to understand their motivations, challenges, and goals. This will help you empathize with them and craft a relevant message.
  • Identify and address conflict: Find the gap between what is and what could be in the data. This is the tension that drives your story forward. Explain why this gap matters and how it can be resolved.
  • Share context: Provide background information that helps your audience understand the data. Use comparisons, analogies, metaphors, and visual aids to make the data more accessible and memorable.
  • Present data as a recommendation: Don’t just show the data. Tell your audience what to do with it. Use a clear and concise structure to present your recommendation. Support your recommendation with evidence from the data and stories from the people.

Data storytelling examples

Data storytelling examplesThere’s a distinct moment in your data storytelling career where you move from explaining data to leading others based on how you have presented data. As a data storyteller, you can spark a movement or sustain one.

When presenting data, it’s less effective to just pop a chart up with a fancy data visualization and expect people to be moved to any sort of action. How you frame, unpack, and deliver data to an audience determines whether they want to align with you and work to help make your dreams come true.

Data presentations that inspire action

Here are a few great examples of data storytelling that inspire action:

Al Gore – An Inconvenient Truth presentation

Al Gore’s movie, An Inconvenient Truth was, in reality, an impactful data story delivered as a data presentation. It ended by letting the audience choose if they would do the work to change the trajectory of the data.

Bono – The Good News on Poverty TED talk

Similarly, Bono delivered a TED talk, “The Good News on Poverty”, which was almost entirely based on data. He humanized each data point by connecting the data narrative about the human-suffering reflected in the data. Bono used data-drive storytelling to show the gap of how far we still must go to reach his life-long quest of ending world hunger.

Steve Jobs – making data relatable

Steve Jobs was also a brilliant presenter of data. In each of his presentations, he was masterful at making the data relatable by connecting it to a scale that humans understand. For example, we all can connect to how long a minute or second is but it’s harder to connect to numbers larger than a million. So, he’d make those numbers relatable by saying things like “1,350,000 is 1 Mac every 6 seconds of every minute of every hour of every day in a quarter.”

To help audiences understand the volume of people coming through their retail stores, Jobs said “To put that in perspective, this Macworld is the largest computer show on the East Coast. In our 31 stores today, we’re seeing two MacWorlds-worth of visitors every week.” In that example, he used the auditorium the audience was sitting in to help them understand the scale of people entering their stores.

Data storytelling course

Duarte’s data storytelling course, Duarte DataStory® is available in a number of convenient learning formats highlighted and linked below.

Duarte DataStory® attendees learn what charts to choose for data visualization, how to write data insights, and ultimately how to make data actionable by making a recommendation from data using data stories. While most data storytelling courses on the market stop there, Duarte DataStory® goes the extra mile, by teaching attendees how to annotate charts with Duarte’s own annotation taxonomy, which was developed based on our research and analysis of thousands (and thousands) of slides.

Using creative thinking, course attendees will structure a recommendation from data using a three-act story structure with our Recommendation Tree™ methodology that creates the structural framework for the student’s data story.

Data storytelling course

The course puts new data storytelling skills into practice, by having students apply what they’ve learned to a case study about a fictitious cacao production company called De La Vega (DLV). Attendees must synthesize data that’s been sourced from multiple data sets from areas like finance, product, marketing, external reports, capacity reports, and even an email from the CEO.

(Incidentally, this exercise was so well built, one of the top 5 accounting firms licensed it for use with all their employees: A testament to how much applied learning comes from going through this process.)

Examples of data stories

Students will use critical thinking to create a real-world critical business decision using the data. They will then write an executive summary for a fictitious CEO, which follows a three-act story structure. A Slidedoc™ (see below) will ultimately become each student’s persuasive data story. Through this practice, they will learn the art of communication in high-stakes, data-driven decision-making.

Data storytelling training

Data storytelling trainingOur Duarte DataStory® full-day, immersive course is the most effective way to learn data storytelling. However, we also offer a shorter-length virtual course and on-demand keynote. These courses are more high level and focus on a few key concepts. Beyond Duarte DataStory®, our Slidedocs® course provides users with a deeper dive on how to create skimmable documents containing their data stories using presentation software.

For your convenience, we’ve listed our courses out for you, below. If you’re interested in data storytelling training your team, all of our courseware below (and more) can be customized for teams of any size. For a custom learning plan and pricing for your team, please drop us a line. We always respond quickly to inquiries!

Data storytelling workshop

Duarte DataStory® 1-day course

This in-person immersive data storytelling workshop is full of facilitated discussion, group collaboration, and individual exercises. You’ll explore a case study to practice the methodology and then apply it to your own recommendation. It’s perfect for individuals, and for teams, who can participate together, share experiences and learning in a way that supports team and culture-building.

Availability: This course is available for teams and open to the public.
Length: One full day (in person), or two 3.5 hour sessions (live online)

Soft skills used
  • Data synthesis skills
  • Critical thinking skills
  • Empathy skills
  • Storytelling skills
  • Reasoning skills
  • Communicating to executives skills
  • Influencing without authority skills
  • Persuasion skills
  • Visual communication skills
What you’ll learn
  • Apply audience empathy to a data recommendation
  • Synthesize a data set and transform it into an executive summary
  • Structure a data recommendation using storytelling techniques
  • Analyze your recommendation to identify counterarguments and additional actions or solutions for your audience
  • Strengthen your recommendation using memorable, effective communication techniques
  • Visually convey meaning of key insights through slide layouts, charts, and annotations
  • Recognize how Slidedoc™ layouts can extend the reach of your data stories

Duarte DataStory®

Half-day course for teams

Through a combination of facilitated discussion, group collaboration, and individual work, you’ll learn the core Duarte DataStory® methodology and apply it to your own recommendation.

Availability: This course is available for teams only.
Length: Half-day (3 hours) (in person or live online)

Soft skills used
  • Data synthesis skills
  • Critical thinking skills
  • Empathy skills
  • Communicating to executives skills
  • Visual communication skills
What you’ll learn
  • Apply audience empathy to a data recommendation
  • Structure a data recommendation using storytelling techniques
  • Strengthen your recommendation using memorable, effective communication techniques
  • Visually convey meaning of key insights through annotations

Duarte DataStory®

90-minute interactive talk

You can take this course live online or have us bring it to your company. Available for teams only, you’ll learn the basic foundation of the Duarte DataStory® methodology through a combination of facilitator instruction, group discussion, and individual exercises.

Availability: This course is available for teams only
Length: 90-minutes (in person or live online)

Soft skills used
  • Critical thinking skills
  • Empathy skills
  • Visual communication skills
What you’ll learn
  • Apply audience empathy to a data recommendation
  • Structure a data recommendation using storytelling techniques
  • Visually convey meaning of key business insights through annotations

Duarte Slidedocs®

90-minute training course

Again, Duarte’s DataStory® data presentation course, provides high-level training on how to structure and write a Slidedoc™. We also offer a dedicated, on-demand Slidedocs® course that teaches attendees how to design and build a Slidedoc™ using presentation software (Microsoft Powerpoint). Here’s some information that explains what a slide doc is, and what our courses include:

Soft skills used
  • Empathy skills
  • Visual communication skills
  • Communicating to executives skills
  • Persuasion skills
What you’ll learn
  • Persuade your audience, even when you’re not in the room, by combining powerful visuals and prose
  • Adapt live presentation slides into stunning leave-behinds that fully explain your message
  • Create magazine-like reading experiences, even if you’re not a designer
  • Access templates you can customize to match your brand’s look and feel

Take the next data storytelling step!

In conclusion, data storytelling is a powerful method of communication that is impactful and action driven. Explaining data effectively is especially helpful when problems are complex, controversial, or where the parameters of problem-solving are unclear. Using data to drive decision-making process can help take emotion out of the process, align business stakeholders on data insights, and help your audience reach decisions with confidence. Adding data storytelling to your public speaking and presentation toolbox is a great way to become a more powerful and well-rounded communicator that will serve you well, not just in your current situation, but throughout your career. We hope to see you at a Duarte data storytelling course in the near future!