Credit to efforts towards data democratisation more and more individuals regardless of possessing a technical background or not, are now able to leverage all that data has to offer. However, Data alone doesn't speak for itself. Simply possessing vast amounts of data is not sufficient; it is equally important to convey the insights derived from that data in a manner that is both lucid and captivating. This is where the significance of effective storytelling with data comes into play.
The art of storytelling brings a human element to data, making it relatable and memorable. It enables organisations to uncover patterns, trends, and correlations that might otherwise go unnoticed. Through compelling storytelling, businesses can present their findings, discoveries, and recommendations in a manner that sparks interest, influences decision-making, and drives action.
As you might have noticed the term “data storytelling” has become a bit of a buzzword in recent times. But data storytelling is more than just a trend or buzzword; it has an exact science to it and if practised effectively it can be immensely powerful within organisations. Effective data storytelling involves understanding the context and objectives behind the data, selecting the most relevant information, and structuring it logically and coherently. It requires the skilful use of visualisations, such as charts, graphs, and infographics, to enhance comprehension and create a visual impact. Additionally, incorporating narratives, anecdotes, and real-life examples can further engage the audience and make the data more digestible and impactful.
Before we dive into key elements of data storytelling, let's first examine why these data narratives hold such significance and what they can help you accomplish.
The benefits of storytelling with data:
While numbers and statistics can provide valuable information, they often fail to resonate with people on an emotional level. By presenting data in a compelling narrative format, we can bring the numbers to life, engage our audience, and unlock a range of benefits.
- Influencing decision-making: One of the primary objectives of storytelling with data is to influence decision-making. By incorporating data-driven narratives into presentations, reports, and visualisations, you can motivate decision-makers to take action that drives positive change, improve processes, and achieve organisational goals.
- Simplifying complex concepts: Modelling and preparing your data, allows you to distil complex data into a cohesive and easily understandable narrative. This helps to bridge the gap between data experts and non-technical stakeholders, fostering a shared understanding and facilitating better decision-making.
- Enhancing engagement and memorability: When data is presented in the form of a story, it becomes more engaging and relatable. This enhances information retention, ensuring that the key insights and messages conveyed are impactful and are more likely to be remembered and acted upon.
- Building trust and credibility: When data is presented in a transparent and narrative-driven manner, it becomes more persuasive and trustworthy. When you share the context behind the data, the methodology used, and the real-world implications, you provide your audience with a holistic understanding that instils confidence in your analysis. This trust is essential for gaining buy-in from stakeholders, encouraging collaboration, and driving data-driven decision-making.
- Champions data democratisation: Storytelling promotes data literacy across the organisation. Not only does it demystify, but it also makes accessing, interpreting and communicating data, easy and accessible to employees of all professional backgrounds.
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3 essential elements to data storytelling:
Data: A dataset/ datasets of high quality and fit for purpose is the bedrock of data storytelling. This data needs to be prepped, modelled, and engineered to use.
Narrative: Narrative provides context and explains why the data is important. Like any good story, the narrative should be attention grabbing and easy to understand. The narrative should also demonstrate a good understanding of the business and its data.
Visualisation: The last key element of data storytelling is visualisation: the visual representation of information using charts, graphs, or tables. Visualisation is a powerful means of demonstrating how the data is connected to the narrative.
Now that you have the blueprint to create a data story, let’s explore how to ensure each of these elements are of high standards ensuring the creation of a compelling story.
Data:
A dataset that satisfies the following dimensions can be classified as a high-quality dataset that can contribute to good data storytelling:
- Completeness: This represents the amount of data that is usable or complete. If there is a high percentage of missing values, it may lead to a biased or misleading analysis if the data is not representative of a typical data sample.
- Uniqueness: This accounts for duplicate data in a dataset. For example, when reviewing customer data, you should expect that each customer has a unique customer ID.
- Validity: This dimension measures how much data matches the required format for any business rules. Formatting usually includes metadata, such as valid data types, ranges, patterns, and more.
- Timeliness: This dimension refers to the readiness of the data within an expected time frame. For example, customers expect to receive an order number immediately after they have made a purchase, and that data needs to be generated in real-time.
- Accuracy: This dimension refers to the correctness of the data values based on the agreed-upon “source of truth.” Since there can be multiple sources which report on the same metric, it is important to designate a primary data source; other data sources can be used to confirm the accuracy of the primary one. For example, tools can check to see that each data source is trending in the same direction to bolster confidence in data accuracy.
- Consistency: This dimension evaluates data records from two different datasets. As mentioned earlier, multiple sources can be identified to report on a single metric. Using different sources to check for consistent data trends and behaviour allows organisations to trust any actionable insights from their analyses. This logic can also be applied to relationships between data. For example, the number of employees in a department should not exceed the total number of employees in a company.
- Fit for purpose: Fit for purpose helps to ensure that the data asset meets a business need. This can be difficult to evaluate, particularly with new, emerging datasets. Different data modelling techniques can help create fit-for-purpose data sets include:
- Dimensional Modelling: Designing a data warehouse for business intelligence reporting
- Hierarchical Data Modelling: Representing organisational structure.
- Graph Data Modelling: Modelling social networks.
- NoSQL Data Modelling: Designing a database for a document-oriented system.
- Temporal Data Modelling: Tracking changes to data over time.
- Spatial Data Modelling: Storing and analysing geographic information.
- Multidimensional Data Modelling: Analysing data with multiple dimensions, such as sales data with dimensions like time, product, and region.
Narrative:
Crafting a compelling narrative in data storytelling involves several key steps, begin by considering the following:
- Define your objectives: Establish the purpose of your data story. Are you trying to inform, persuade, entertain, or educate your audience? Understanding your objective will shape the narrative direction.
- Know your audience: Tailor your narrative to the needs, interests, and level of understanding of your audience. Consider what they already know about the topic and what they need to learn from your data story.
- Structure your story: Organise your narrative into a coherent structure with a clear beginning, middle, and end. Introduce the topic, present the main findings, and conclude with key takeaways or recommendations.
- Create a compelling hook: Capture your audience's attention from the start with a compelling hook or opening that sets the tone for your data story. Use storytelling techniques such as anecdotes, questions, or thought-provoking statements to pique curiosity.
- Provide context: Contextualise the data by explaining its background, significance, and relevance to your audience. Help them understand why the data matters and how it relates to their interests or concerns.
- Engage your audience: Encourage active engagement and participation from your audience by asking questions, soliciting feedback, or prompting discussions. Keep them engaged throughout the data story to maintain their interest and attention.
- Conclude with a call to action: Summarise the key insights and conclusions of your data story and conclude with a clear call to action or next steps for your audience. Encourage them to act based on the insights gained from the data.
Visualisation:
Incorporating well-designed visualisations into data stories can significantly elevate their effectiveness and influence. Here are some best practises to follow:
Simplicity & Relevancy: The primary goal of storytelling with charts is to convey a clear and concise message. Complex data can often be overwhelming, so it is important to simplify it without sacrificing its integrity. By selecting the most relevant data points and using appropriate chart types, you can distil complex information into easily digestible visuals.
Context: Provide context for the data visualisations to help the audience understand the significance of the insights presented. Highlight key insights, include annotations, captions, or call outs to provide relevant background information or explain key findings.
Stay consistent: Using consistent colour schemes, typography, and chart styles creates a cohesive story and visual experience. Additionally, using colour and typography effectively can emphasise important data points and create a visual hierarchy. It is crucial to consider accessibility as well, ensuring that the charts are accessible to individuals with visual impairments by providing alternative text and considering colour contrast.
Impactful chart break-up: Any story will have a setup, conflict, and resolution. Begin by setting up your charts based on the story you want to convey. Then, find the point in the visuals where there's a problem or conflict. Break down the visual into smaller parts, focusing on the data before and after this conflict point. This breakdown will help you uncover insights and turn your data into a compelling narrative.
Leverage tools: Data visualisation tools like Tableau, QlikView and Power BI offer more than just basic charting capabilities. They provide dynamic, interactive, and real-time visualisation that can adapt to the ever-changing data landscape. These tools allow for more complex visual representations, enabling users to dive deeper and uncover insights that might have been overlooked.
Feedback and Iteration: Seek feedback from stakeholders or test audiences to identify areas for improvement in visualisations. Iterate on the design based on feedback received, refining visualisations to better align with the story's objectives and audience preferences.
Finally remember, visualisation required for telling a data story should be different from those that were created for operational reporting or analytics dashboards.
Do you often find your organisation is overwhelmed by the sheer volume of data they possess? It is crucial not to be intimidated by this abundance. Data, on its own, holds no intrinsic value; it's the way we interpret and communicate it that drives impact. Which is why data storytelling is more than just a trend- its practise that needs investing in and it’s a skill that is assured to grow in importance.
At Ei Square, we are skilled navigators on the sea of information. We specialise in sifting through the waves of data, uncovering hidden insights, patterns, and more importantly, guiding attention to what truly matters.