Why Story Telling?

Raw data is useless on its own. Only when we add meaning to data, it becomes relevant and useful.

Designing effective data-driven story presentations involves balancing aesthetics, clarity, and the narrative to engage the audience and communicate insights effectively. Here are some key considerations for designing data-driven story presentations:

Know your audience: Understand your audience’s background, expertise, and expectations. This will help you tailor your presentation to their needs and ensure the insights are relevant and actionable.

  • Keep it Simple: Avoid overcrowding your presentation with too many visuals or excessive information. Keep it simple and focused on the most critical insights.

  • Focus on the most important insights: Highlight the most critical insights, trends, or patterns to help your audience understand the key takeaways. Use emphasis, contrast, or other visual cues to draw attention to important points.

  • People are much more more likely (~x20) to recall a tale than factual information that lacks narrative structure. When people hear a narrative, their brain waves synchronize with the teller’s, allowing them to assimilate elements of the story into their own experiences.

  • And few more Tips 👇

    • Consider your audience, your content and your purpose for telling a story. Likewise, define the point of your story and craft your message to illuminate it.

    • Define the narrative: Identify the key insights, trends, or patterns you want to convey and structure your presentation around a clear narrative. Make sure the story flows logically and guides the audience through the data.

    • Choose the right visualizations: Select visualizations that best represent the data and support your narrative. Use charts, graphs, and other visual elements that are easy to understand and effectively communicate the insights.

    • Use clear and concise language: Ensure that your text, labels, and annotations are clear and concise. Avoid jargon and explain any technical terms to make the information accessible to a broader audience.

    • Maintain visual consistency: Use consistent visual elements, such as colors, fonts, and chart styles, throughout your presentation to create a cohesive look and feel.

    • Provide context: Provide context and background information to help your audience understand the data, such as the source, timeframe, and any underlying assumptions.

    • Make it interactive: If possible, add interactive elements, such as filters, sliders, or drill-downs, to allow the audience to explore the data further and customize their experience.

    • Test and refine: Test your presentation with a diverse group of users, gather feedback, and iterate on the design to ensure it effectively communicates the insights and engages the audience.

    By considering these factors, you can create a data-driven story presentation that effectively communicates your insights, engages your audience, and supports better decision-making.

All those TIPS basically resonates with the best practices to create a Data Product: * Audience: Understand the people WHO need your data * Data: Define and enhance the data for your solution * Design: Craft an application that solves problems (HOW) * Delivery: Transition from application to profitable product (WHAT)

Building your Story with Data

  • Stories use pattern recognition to give meaning to data

  • Establish relevance with data-driven stories that appeal to your target audience

  • Strong data visualization can play a major role when creating compelling stories

  • Beware of deliberate manipulation, mind traps and false certainties when interpreting the data. A good point to review for this matter are both:

Use stories to assist decision makers see patterns in data to help them make sense of the world around them. Today’s storytelling is more machine-powered, with robots finding correlations in data that people may overlook.

Consider story telling to be “data packets” that compress and filter information for humans, providing meaning to raw data.

Know your Audience

The data-driven stories that you will create, must appeal to your target audience.

  • WHY are we telling the story?

Pay attention to the narratives of your target groups to have a better understanding of why people could connect with your tale.

  • WHAT Audience?

Use digital platforms to gain vital data insights into your customers’ expectations, opinions, and wishes.

  • WHICH Message?

Data helps you to uncover new tales and novel methods to relate these narratives to themes of interest to your target audience.

Communicating Insights

During your data Analytics career, you will find the following ways of communicating insights:

  • Report: a structured and organized presentation of data, often in tabular format or including basic visualizations like charts and graphs.

Reports typically provide detailed information and are used to monitor specific metrics, track progress, or analyze historical data. They can be generated periodically (daily, weekly, monthly) or on-demand and often include filtering, sorting, and drill-down capabilities for users to explore the data.

  • Dashboard: a visual representation of data that consolidates key performance indicators (KPIs), metrics, and other relevant information into a single view. Dashboards are designed to provide a high-level overview of an organization’s performance or a specific business process.

They often include interactive elements, such as filters and drill-down features, that allow users to explore data in real-time. Dashboards are typically more focused on the current state or recent trends and are used to monitor and track performance in near-real-time.

  • Data Story: a narrative that uses data and visualizations to convey insights, trends, or patterns in a compelling and engaging manner. Data stories often include a mix of text, visuals, and interactive elements to create a more immersive experience for the audience.

They are designed to make complex information more accessible, relatable, and actionable, helping stakeholders understand the implications of the data and make better-informed decisions. Data stories are particularly useful for presenting insights from data exploration, analysis, or when communicating the results of a specific project or initiative.

Data Visualizations

Your story will become more powerful if you assist it with proper Data Visualization.

Make certain that all data visuals convey the narrative properly and provide the information you want. People may utilize visuals out of context, thus your graphics should be able to stand on their own, without the need for words.

Remember that less is more and only include data comparisons that you feel will aid in conveying your point. The most intriguing tales, which can include both graphic and text-based information, relate the statistics to individual lives, giving numbers people faces.

Reality is Non-Binary

When analyzing data, avoid either purposefully distorting it or inadvertently misinterpreting it owing to false certainty.

We tend to think in polarities, separating the universe into binary categories. This can result in prejudices and an inability to perceive complexity.

Polarization allows the brain to make quick judgements, but thinking in basic, binary terms does not allow you to adequately comprehend the complexity of reality.

Avoiding Mind Traps

Beware of forming narratives by embracing faulty heuristics.

And since you might be more aware, try to spot when others are trying to apply such narratives to you or your close ones.

For example, many assume “everything used to be better,” and frame stories to present narratives of decline.

  • People may also assume that observed developments will progress uniformly – because they have in past years – and thus fail to predict reversals or exponential growth.
  • People also tend to overestimate the likelihood of an event occurring when they can think of examples of the event – the availability trap.”**

When telling stories, aspire to connect to people’s slow thinking brain systems, which – according to psychologist Daniel Kahneman – switch on for more complex thought processes.

Machine Biases

Computers may (and will) be biased as well: “garbage in, garbage out.”

Data inaccuracies can arise, resulting in “machine bias,” which occurs when data collecting reproduces human prejudices and patterns associated with discrimination.

The data you choose may be skewed, especially if it originates from social media platforms where people express prejudiced opinions.

We need to be extra careful when trusting blindly Algorithms, as there is always a trade-off.