In the past decade, data has transformed how we look at business, politics, and the environment. Storytelling has become a go-to form of communication in recent years, with data now playing an essential role in these stories. Thanks to the surge in Big Data, we can now tell richer stories than ever—stories tailored to our audience’s specific data visualization needs.
As we move forward, these trends will continue to grow stronger and cause changes in even more areas of our lives as we learn to utilize data better to accomplish our goals. Learn more about the megatrends that are fueling this shift.
Here Are 5 Data Storytelling Megatrends and Examples:
1. Information Overload
In a world constantly bombarded with information, it can be tough to stand out from the noise. It’s not just the sheer volume of information that’s overwhelming. It’s also the speed at which it arrives.
We get constantly bombarded with news and updates, and keeping up with the expanding history base can be challenging. In today’s media environment, data and algorithms are how many businesses get their stories out.
Journalists and content creators are under increasing pressure to come up with new angles and stories, making them want to rely on existing data sets. Some media companies employ programmers whose only job is to generate original reports based on incoming data.
Many other outlets use a mix of in-house staffers, freelancers, and paid contributors who specialize in using data to create fresh narratives for their audience. As such, there’s been an explosion of datasets released by governments and private organizations.
In addition, there are lots of essential developments happening every day. These developments include geopolitical events, globalization, scientific discoveries, and economic changes, and we want to stay on top of all those things too.
That’s why data visualization is so important. It helps people make sense of the vast amounts of data available and allows us to see the bigger picture. With the right data visualization strategy, you can cut through the clutter and connect with your audience on a deeper level.
To simplify your life, you need tools for managing information overload. Filtering technology is essential in cleaning up your inbox and other digital data sources so you can focus on what matters most.
Scheduling your day allows you to plan how you will spend your time. For example, if you want to be more productive at work, prepare things like breaks and meetings so you can have time for focused work. These quick breaks help prevent information overload and allow you to take some time away from intense tasks.
Similarly, allowing yourself a certain amount of free time each day ensures you can disconnect and recharge before returning to your busy schedule.
Also Read: How to Create a Dashboard in Power BI
2. Dwindling Trust in the Media
The trust in the media is at an all-time low. In an era of fake news and alternative facts, there is mistrust due to the proliferation of news sources, including social media, and the lack of transparency around how stories get reported.
Many people believe the media to be subject to immoderate political influence. On the other hand, only diminishing a minority believes that media prioritize society’s interests ahead of their commercial interests.
As a result, people are increasingly turning to data-driven storytelling to get accurate information. But what does this mean for the future of data storytelling?
Finding new ways to gain a person’s trust and ensure your content is credible will be critical. One way would be to be transparent about where you get your information from, who funded your research or survey, and whether you have any financial ties to the companies.
Data storytelling provides a way to present information clearly, and unbiasedly. In all likelihood, companies and government agencies will ramp up efforts to use data-driven storytelling as a tool for problem-solving.
For example, many financial institutions already use data analytics to manage risk profiles and prevent fraud. Automated decision-making systems will support many business processes, with businesses and governments turning more frequently to data-driven storytelling.
Routine tasks will become increasingly automated over time. Decision makers should prepare for increased reliance on machine intelligence and predictive model. Optimized data storytelling will enhance the use of their organizational resources more effectively to increase agility and reduce costs.
3. The Winner-Take-All Dynamics
We rely on algorithms and sensors to be an extension of ourselves, connecting everything we do online. The winner-take-all dynamics created through machine learning already impact all aspects of our lives, including media consumption.
More and more of our everyday interactions get driven by predictive analytics and AI. From the food we eat to the products we buy to the people we meet: technology learns from every experience and optimizes for future decisions.
However, there’s a disconnect between what we expect as consumers and what companies provide when it comes to storytelling. The over-reliance on algorithms has created a need for more data storytelling techniques.
As a result, the profession of data storytelling is evolving as more organizations recognize the importance of telling their story through analytics. Data storytelling has emerged as a new way to present information to bridge that gap between what we expect as consumers and what companies deliver.
As more organizations use predictive analytics and machine learning to inform their operations, data storytelling has become essential for delivering meaningful insights. In a winner-take-all digital world, the company with the best data storytelling capabilities will be the one that captures the most value.
So what does this mean for your business? It means you’ll want to start thinking about how to develop your internal data storyteller resources.
Those companies who can tell the story behind their products and services with precision will have the edge over competitors who cannot do so. The demand for talented data storytellers is only going to increase as more industries need them to connect with consumers on an emotional level.
4. Democratization of Data Storytelling
In the past, data storytelling was the domain of a select few. But as data becomes more accessible and tools for analysis become more user-friendly, we’re seeing a democratization of data storytelling.
Data storytelling democratization means anyone with access to data who can use essential analytical tools like Qlik Sense extensions can now tell stories with data. At the same time, the need for high-level data science skills is decreasing.
As a result, more organizations are creating internal data storytellers. These are individuals who understand how to find and analyze data but lack advanced quantitative skills. With easy-to-use software, these storytellers can create meaningful visualizations without programming knowledge.
The downside is that this democratization of data storytelling may lead to lower quality content from non-specialists. However, those skilled at telling good stories will be even more valuable than ever.
Organizations will still need people who know what data to acquire and why it’s essential. They’ll also require someone with a sense of what questions to ask about the data. That’s where traditional business intelligence teams come in.
They’ll provide guidance on which dataset to work with and identify possible insights before handing off the project to the storyteller, who then must transform these ideas into something consumable by a wider audience.
5. The Changing Roles of Text and Multimedia
In the past, data storytelling was primarily text-based. However, with the rise of multimedia platforms like Snapchat and Instagram, we are shifting towards more visual and interactive content. People are now looking for ways to consume data that are more engaging and visually appealing.
The changing role of multimedia means that data storytellers need to be able to adapt their skills to include multimedia elements and data visualization in their stories. This trend will continue as people increasingly consume content on their mobile devices.
These trends include animations, videos, augmented reality, podcasting, presentation, and virtual reality. These new trends require a whole new set of skills for data storytellers, such as coding and designing for digital formats.
As this becomes an increasingly popular medium for telling data stories, it will create a demand for skilled professionals who can bring visuals to life through animations and video.
Brands have also begun leveraging this trend by adopting native video marketing strategies, which allow them to create engaging video content that feels less intrusive than banner ads. With so many brands taking advantage of these new opportunities, companies must invest in data storytelling if they want to keep up with this rapid change.
Video content continues to grow exponentially, with one billion hours daily consumed on YouTube alone. Unsurprisingly, most marketers plan to increase their spending on digital video advertising over the next few years.
However, marketers need to consider the potential pitfalls of video content, such as how quickly audiences can get bored or distracted while watching videos. Keep your videos short and provide call-to-actions throughout to ensure high engagement rates.
See Also: Does Data Visualization Have an Impact On SEO?
Final Thoughts
The future of data storytelling is looking bright. With the rise of new technologies and the ever-changing business landscape, there is a need for more sophisticated and innovative ways to tell stories. As we become more reliant on data to make personal and professional decisions, those who can tell a story with data will have a leg up.
