Data analysis is a valuable skill, but it is not enough to just crunch numbers and create charts. You also need to communicate your findings and recommendations effectively to your audience, whether they are managers, clients, or stakeholders. How do you present data to drive action? Here are some tips to help you craft a compelling data story that inspires your audience to take action.
Before you start designing your presentation, you need to understand who you are talking to and what they care about. What are their goals, challenges, and expectations? What level of detail and technicality do they need? How do they prefer to consume data, visually or verbally? By knowing your audience, you can tailor your message and tone to their needs and interests, and avoid confusing or boring them with irrelevant or complex information.
One key aspect to keep in mind is the target audience's background and thought process. I focused on telling the patient journey and impact with data to my clinician leaders given their patient-oriented nature. Meanwhile, I led with clear overall P&L impact first before drilling down the operational drivers with my CFO. Knowing how your audience's preferred way to digest data is the key to structure your data story effectively.
Next, you need to clarify what you want your audience to do after they see your presentation. What is the main point or takeaway you want to convey? What is the problem or opportunity you are addressing? What is the action or decision you are recommending? By defining your purpose, you can focus your presentation on the most important and relevant data, and structure your argument logically and persuasively.
Always focus on the facts and don’t overreact to the presentation. Quality of data matters, we shouldn’t chase the quantity only.
The purpose should be part of our data analysis project, without purpose, there isn't a project, I'd refine the purpose in such a way that every audience can understand, for ex: leaders would look at efficiency/ROI, whereas call center folks would look at numbers of hours saved etc, translating the purpose based on audience is the key.
Depending on your purpose and audience, you may need to choose different formats and tools to present your data. For example, you may use a slide deck, a report, a dashboard, or a video to deliver your presentation. You may also use different software or platforms to create and share your presentation, such as PowerPoint, Tableau, Google Slides, or YouTube. By choosing the right format, you can optimize the visual appeal, interactivity, and accessibility of your presentation.
I think about presenting data as an Iceberg on the ocean. From my experience, the higher level your audience operates at, the smaller piece of the iceberg you need to show initially. But those pieces of data better be highly curated and intentionally summarized. On the other hand, presenting data to audience closer to work requires you surfacing larger part of the iceberg which are more relevant to their work.
One of the most effective ways to present data is to use clear and simple visuals, such as charts, graphs, maps, or icons. Visuals can help you highlight key trends, patterns, or insights, and make your data easier to understand and remember. However, you need to be careful not to overload your visuals with too much data, colors, or text, as this can distract or confuse your audience. Instead, use simple and consistent designs, labels, and legends, and choose the appropriate type of visual for your data.
Finally, you need to tell a story with your data, not just show numbers and facts. A story can help you connect with your audience emotionally, and motivate them to act on your data. To tell a story, you need to have a clear beginning, middle, and end, and use narrative techniques, such as setting the context, introducing the characters, building the tension, and resolving the conflict. You also need to use language that is engaging, concise, and confident, and avoid jargon or acronyms.
It's crucial to have a tight knit story-telling when presenting your data through dashboards or reports. There should be a clear connection between your data points and fluidity in your visuals. The labels must have a clear definition and use it smartly to setup the context and navigation. The end goal should be clearly addressed so that the end users aren't left in doubts or confusion. This will increase their confidence in your data and encourage them to utilize it for further analysis.
Storytelling with data involves creating a relatable and empathetic experience for the audience. By understanding their needs, concerns, and aspirations, the narrative can be tailored to resonate with them on a deeper level. Including real-life examples, testimonials, or personal anecdotes is a great way to humanize the data and make it more relatable, eliciting an emotional response that fuels action. The power of storytelling lies in its ability to engage and persuade, transforming raw data into a meaningful and memorable experience for the audience.
One approach that works really well for me involves using qualitative insights to 1. build a complete picture, and 2. to provoke empathy. In one way or another, there are people behind the data - and they should always be included in the story. Telling directors that the employee turnover rate has increased is one thing. Pairing that data with the reasons why staff are leaving adds a layer that not only helps the audience to empathise, but also to understand the situation and find solutions. You could include quotes from exit interviews, or common themes across the employee experience. At the very least, this gets people thinking and talking. Using qualitative insights to compliment your quantitative data can be a game changer.
Do pay attention to from whom, by whom, where, how, and why data were collected and the associated unintended biases. In addition, analyzing data that were collected for different purposes may present another layer of issues. I believe there is no story without data and there is no data without story.
We like to follow a systematic Situation · Causation · Action model: 1. Situation: Where are we now? 2. Causation: Why did we land here? 3. Action: What should we do about it? This model is especially effective for Performance Management, whereby the Situation is defined in relation to a past forecast (Exceeded, On Target, Missed), and the Action compares two future forecasts, one with action and one without action.
I agree with the importance of providing context for the data being presented and would argue this should be addressed first and foremost otherwise the entire data presentation loses its weight or worse, decisions are made based on faulty assumptions you failed to communicate. A sub-header titled “Add Context” or “Address Assumptions” or something to this effect could come at the top of this article on this topic.
(1) Data quality steps that result in loss of data should be documented and presented as "Notes" to check for bias. (2) Data analysis may be accompanied with statistical analysis, which may be presented only when asked for.
Some novel thoughts on this topic - remove data & shiny tech clutter - focus on what the stakeholder needs to make a decision or take action to avoid confusion. Eg. If an HR Leader needs to understand the attrition rate by department and predict future rates - there is less sense in showing how that data was transformed or how the predictive model was made compared to insights that help reduce employee attrition.