Dear friends!
Numbers have an important story to tell. They rely on you to give them a clear and convincing voice. Welcome to the world of storytelling (with data)! Today, Sindhu Iyengar and I will introduce you to the methodology of bringing data to life with the goal of improving your visualization skills and the effectiveness of your communication.
What is Data Storytelling?
Storytelling with data is a huge undertaking. As we shift into a world more driven by data and its finding, we need to ensure that we are ahead of the curve. Simple statistics and numbers don’t cut it anymore — we need a narrative that we can spell out with analysis, or we risk losing our audience entirely.
Data Storytelling can be divided into 4 crucial components, each of which plays a vital role in ensuring the success of your data storytelling project. These areas help to define clear objectives and ensure that your data storytelling efforts are focused and effective.
I. Context: Understanding your Audience, Clear Goal, and Actionable
Understanding your Audience. A common mistake among business analysts is to leave this step until the end, with the goal of tweaking the final presentation and outcome to match the audience they have. By categorizing your audience at the beginning, you save a lot of time and effort by tailoring your work during every single step of the process to match the people you will eventually present to. This means less stress when you’re presenting, and a cleaner, easier-to-understand slide deck for your audience. You can ask yourself who exactly you will present to (What do they do? Are they used to statistical measures? Do they want to see number-backed results?), how comfortable they may be with seeing data and its insights, how much of an introduction you will need to provide them with, and what kind of solution they are looking for. For example, engineers tend to prefer scientific models, numbers, and statistics, whereas managers tend to prefer specific examples, high-level visualizations, and only want to see the most important statistics.
Frame the problem / clarify the goal. This involves breaking down the data into smaller pieces, using tools such as Trello or Google Keep to visualize the process, and keeping track of your progress. The goal of this step is to have a clear picture of what you are trying to solve, what information you need to deliver, and what you need to solve it.
Once you have a clear picture of the problem, you need to prepare for your analysis. At this stage, you have the data (or need to collect it), but before you can do anything with it, you need to understand the domain. By gaining in-depth knowledge and understanding of the industry that the data pertains to, you can approach the data with a more informed perspective, identify relevant trends and patterns, and provide more meaningful insights. Furthermore, having domain expertise helps to build credibility with your audience, as it demonstrates a deep understanding of the subject matter, and allows you to develop a more engaging and impactful narrative.
Actionable. Data storytelling is all about delivering impactful insights that drive meaningful action. By honing in on the aspects of the context that matter most to the management and what they have the power to control and act upon, you can craft presentations that are both relevant and insightful. This is essential for ensuring that the data being analyzed is not only meaningful to the audience but also drives real results for the business.
II. Data: Quality of Source, Statistical Analysis, Accuracy, Focus
Once you have a clear understanding of the context and your audience, it’s time to analyze the data. First, it is essential to ensure that the data you used is accurate, reliable, and up-to-date. To do this, you need to assess the source of the data, whether it is a primary or secondary source, and the method used to collect the data. It is also important to consider the credibility of the source and the relevance of the data to your analysis.
Statistical analysis is the process of using mathematical and statistical methods to analyze and interpret data. This toolbox helps you to identify patterns, trends, and relationships in the data. It can also support you in identifying outliers and anomalies in the data, which can lead to new insights and opportunities for further analysis.
Lastly, take another look at your presentation and re-examine it. You know who your audience is — are you telling them exactly what they need to hear? Additional information can be great, and well-received by people who are interested in further insights, but if your audience is comprised of managers with less time to spend on your analysis or executives who are far away from the data, you may want to trim off all the excess information they didn’t ask for.
Of course, any unusual findings, additional recommendations or assumptions, and supportive data are all very important to see and understand, but consider that not everyone needs to see them. A great way to ensure you don’t lose essential information is to create two versions of your presentation — one with all the additional information, and one without (maybe with an appendix). This way, you have a new set of findings to present if your audience asks further questions or appears to be interested in your other information.
III. Design: Design Principles, Design Elements, Imagery
Selecting the right visual representation for your analysis is as important as the analysis itself — it will control how your work is received and what people can take away from your project. Design principles include using a consistent visual style, creating a visual hierarchy through the use of color, font, and spacing, and using clear and concise labeling. Design elements can be used to create visual interest and help guide the audience’s attention, such as using charts, graphs, and diagrams to represent data in a visually appealing way.
Imagery is also a crucial aspect of data storytelling. It can support your findings and bring an emotional element to your presentation, making it more memorable and impactful. Imagery can come in the form of photographs, illustrations, or even iconic symbols that are associated with your industry. For example, an analyst in the healthcare industry may use an image of a stethoscope to represent the medical field, or an analyst in the finance industry may use an image of a bar graph to represent financial data.
IV. Narrative: Language, Structure, Call to Action
Now that you know exactly what you will present, you need to decide how you will lay it out. The most important findings should be at the top, so it is the first thing people see. At the same time, you want to tell a story with your work, to help people see how to get from point A to point B. Create a narrative to keep your audience engaged throughout your presentation — numbers and data are great, but can be difficult to follow if people are not already familiar with the data source and analysis. Ask yourself exactly what parts are most important for your audience to know, and how you want to direct their focus, so you can point them in that direction.
Call to Action: This is arguably the most important part of your presentation. Pick out the insights you wish to leave your audience with. Point out what action steps you are recommending, the process you think it will take to get there, and any other insights that you know are most important. The final remarks you make at the conclusion of your presentation will leave a lasting impression on your audience, so it is important to end with a powerful summary, memorable visual, specific call to action, or any other clear and straightforward message.
Now that you’re armed with the tools to create a great story with your data, you’re all set! Create a narrative for your analysis, and you’ll see the positive impact your recommendations and findings can make.
