How do you combine stories and data?

When you support your story with data, you appeal to both the heart and mind of your reader. - Stories connect. - Data builds credibility. You need both when it comes to build a good presence on social media. So, combine both and create a data driven story that your audience will never likely forget.

Data is the backbone of a story which not only resonates with audiences but gives them a proof to believe in that. - With good stories you capture their heart. - With real data you capture their mind. Try to mix both and bring a better story to let people follow you for your writing style and trust you because of the proof you have presented.

Imagine a realm where storytelling and data converge, forging an enchanting bridge between imagination and cold, hard facts. While their melodies seem worlds apart, it's fascinating how storytelling employs data in ways both overt and veiled. Reflecting on my journey, the allure of storytelling as it weaves engaging tales, while data sometimes languishes in the shadows of monotony. Yet, the true magic lies in the art of presentation. When data is sculpted into a narrative, it metamorphoses into a captivating entity, capturing the attention of even the most indifferent souls. In essence: embrace your inner storyteller while entwining the threads of data, crafting an experience that dances on the precipice of creativity and reality.

As the saying goes " data rolled into context makes a great story". Hence there is essentially only one purpose - to communicate the message in an interesting way, supported by data.

Combining stories and data creates a holistic message. The story grabs attention and taps into emotions, while the data adds credibility and logic. Your audience not only understands your message but also feels connected to it on a personal level. It's this harmony of heart and mind that makes your communication truly compelling. Example: Think about a charity campaign. Sharing a personal story of a beneficiary creates empathy, encouraging people to donate. But when you supplement it with data showing how many lives have been positively impacted, potential donors gain confidence in the campaign's effectiveness.

You can weave a story on the basis of data. But that requires good research to understand the centre of the data which will actually bring relatability to people. To understand how to weave stories out of data; - read the data a few times to find out the crux - try to curate short notes out of it to bring a long format - write a summary of the whole data and try to mix the notes with data - Check where you can relate it with real life stories. With a good analysis of summary and notes, you'll be able to write short stories which will resonate with people.

In my experience there is no "data storytelling". There is either a story with supporting data, or a story without data. If the data contradicts the story, then it becomes part of the "conflict" which should be then addressed by an appropriate "resolution".

As we read through compiled data, it's crucial to have input from stakeholders that the data affects and from those the data serves. Hearing the stories of outliers, irregularities, and positive impact stories can help solve problems as we begin to weave our campaigns, copy, and thank you letters.

Look at what journalists do. For example, a new report comes out on companies mandating a return to office and the reluctance of employees to do the same. Let's say 75% of companies require return to office, and 85% of employees want/need flexibility in their jobs. Interesting, right? It shows a real disconnect between employers and employees. But to make it really engaging, a thoughtful journalist will interview a single mother who is forced to quit her job because she can't work on location and take care of her young son at the same time. Then they'll interview a frustrated executive who wants their teams together in person because there's a clear link to productivity and engagement. Right there – those are the stories in the data.

Let's delve into the process of finding the story in your data, using a real-world scenario: Imagine you're a marketing analyst tasked with presenting the results of a recent campaign. Identify the Core Message:your core message might be that the campaign successfully increased website traffic. Define the Problem/Opportunity: you could mention that the company sought to boost online engagement. Set the Goal/Outcome:your goal might have been to attract 'n' no. of new visitors to the website. Highlight the Challenge/Conflict: you could mention that reaching the target audience proved more challenging than anticipated. Present the Solution or Action: Describe strategies you employed, such as social media promotions and personalized content.

While the story structure is commonly known (though not commonly practiced in most cases), what people often overlook is the need to build a much-needed 'segue' between the parts. The absence of an appropriate segue disrupts the flow and prevents a smooth narration.

Storytelling is ingrained in tradition and culture. In my experience, I have found that when I use anecdotes from my learnings & failures, and simplify communication using culturally relevent analogies - Its made it easier for my listeners to relate and ask questions. I often start with a legend (formatting) for new participants because I use a lot of sketchnotes to keep everyone curious and engaged with lots of wavy hand gestures. Keeps my energy high, loud and keeps everyone in my facilitation sessions highly engaged.

Imagine you're presenting the results of a market analysis to your team. Beginning: Context: Introduce market landscape and the importance of the analysis. Characters: Introduce your team and their role in the analysis. Stakes: Highlight the significance of the insights in making informed decisions. Middle: Plot Development: Elaborate on the approach taken to conduct the analysis. Present the Data: Share the key data points, using charts or graphs Build Tension: Discuss unexpected findings or challenges End: Conflict Resolution: Explain how insights address the initial questions or challenges. Outcome: Reveal the actionable conclusions drawn from the data. Call for Action: Summarize key takeaways & propose actions based on the insights.

While selecting the data visualisations it’s important to ensure that they compliment your story. Also if the data is too extensive, choosing data visualisation that covers all relevant data without overwhelming the audience is important.

Appropriateness is key here; creating overly fanciful charts is not. In my experience, there is often an inclination among analysts to believe that simple charts do not impress people, which, unfortunately, is not true.

Scenario: Imagine you're presenting the quarterly sales performance to your company's stakeholders. Type of Data Visualization: Given that you're showcasing sales performance trends, a line chart might be a great choice. Design Elements:Use the same color palette, fonts, and styles across all your charts and graphs. Colors/Fonts:Choose colors that aid comprehension.Opt for fonts that are readable even from a distance. Labels :Labels are essential to help your audience understand what they're looking at. Titles and Captions: Each visualization should have a clear, concise title that summarizes its main message. Annotations: Annotations, such as arrows or text boxes, can draw attention to specific data points or trends you want to emphasize.

The more simple it is, the better. That means that you have to use common charts (if any) and declutter them to the point only the most important data point will jump to the eye. In that, you have a whole subset of weapons: colours, forms, callouts,...

I would like to quote Matt Abrahams from Stanford GSB, who says that when it comes to narration, the mantra to follow is 'Audience, Intent, Message.' Simply put, take the right AIM before attempting to communicate.

Narrating data can have severe pitfalls in storytelling. The temptation to manipulate data around the story we want to tell is strong. I suggest posing possibilities on why the data reads the way it does. When narrating the data for users or donors, the best option is to assume that our audience is bright and will reciprocate the trust given.

One thing I've found helpful is to ask if we're cooking the books with our data. Are we receiving only good news because that's what we seek? What aren't we seeing? How would we know if we were receiving insufficient data? What are our worst-case scenarios?

Testing - Gathering Feedback: Clear Point: Ensure your main message is evident and understood. Coherent Structure: Verify your narrative flows logically and is easy to follow. Compelling Visualization: Confirm that your visuals effectively convey data meaning. Persuasive Narration: Seek opinions on engaging and resonating delivery. Strong Call to Action: Evaluate whether your call to action motivates action. Refining - Enhancing Impact: Feedback-Based Adjustments: Incorporate suggestions to clarify, tweak visuals, and strengthen delivery. Practice Narration: Practice pacing, emphasis, and confident tone. Measure Impact: Gauge audience engagement and reactions post-refinements.

Data always tells stories for the simple reason that data is usually born out of real happenings. So, if you've some data on something, you can always take a different angle, and weave a fresh perspective. Two things are important in my view - A) knowing the topic well, and B) being able to connect different points in a 'storytelling way'. For example, right now, I'm looking at 2 data-based stories that I intend to read. 1) Why the Internet Didn't Shred the Paper Business (Slate.com), and 2) ‘He Drives Me Mad!’ Why Don’t We Dump Toxic Friends? (The Guardian). Hope this helps.