Businesses in the digital age are inundated with data as it floods in from multiple channels. This data is both a challenge to wade through and an absolute goldmine. Its tremendous potential can be harnessed to communicate meaningfully with audiences and advance an organization’s brand awareness in the public eye.
The problem is, however, that raw data itself can’t tell a compelling story to most people. It needs to be woven together artfully to create a narrative that connects with a specific audience. This is where data-driven storytelling comes in.
Because data is so crucial to many business decisions and day-to-day operations, your internal teams, clients, and customers need to have a relationship with it. When blended appropriately with human communication, data can create engaging stories that are, importantly, anchored in factual reality.
Data-driven storytelling is an effective communication tool, as your messaging can be tailored to the specific audience you are trying to reach. By making use of engaging and relevant data visualization techniques, such as charts, graphs, and other images, you can weave together a narrative that reaches and convinces your target audience.
Data stories are crafted by sifting through and analyzing large datasets to zero in on meaningful insights that can reveal a new or unique perspective on the information.
By effectively combining the strengths of these valuable insights with the fluidity and relatability of the narrative medium, data-driven stories can communicate both details and big pictures clearly while ensuring the audience is not cognitively overwhelmed.
The Benefits of Data-Driven Storytelling
The constant onslaught of data can be a struggle to make sense of. The storytelling aspect can elevate it into a valuable tool that presents the data itself, while also contextualizing it in any number of ways. At the same time, numbers and raw facts will anchor your claims in reality, which will lend you greater credibility.
Consider especially leveraging internal or proprietary data while crafting your data stories, as it will help you stand out amongst the crowd and captivate your audience. The right graphical elements and data visualization techniques will also catch the attention of the media, which will increase the likelihood of your content being circulated by influential individuals and publications.
Cognitively speaking, the narrative and graphical elements simultaneously tap into both hemispheres of the brain, making the experience both analytical and sentimental.
This ensures that your audience internalizes and retains the message you’re trying to get across. What’s more is that data-driven stories are versatile, and can be delivered through a myriad of media.
When portrayed correctly, your organization’s data can tell an enlightening and entertaining story. This comes from comprehending the patterns created by the information and unearthing the larger trends. These significant points, when presented objectively and unbiasedly to the viewer, form the core of the story’s communication value.
The primary object is to define the objectives of your story by narrowing down who your audience is, and then understanding the extent of that audience’s knowledge on the topic. This will help you accurately tailor your visuals to their level of comprehension and to any questions they may have. You may leverage any number of relevant data visuals, such as line graphs to model long-term trends and projections, or pie charts to display demographical information.
To amplify the impact of your data visualization techniques, however, you will need to find the plotline. Leading the audience along a journey that keeps momentum will keep them engaged, and their investment will especially when the plotline builds to a satisfying resolution. Other narrative techniques that are important to make use of include setting (to contextualize the data) and characters (to establish the major players in the story’s plot.)
In practically every industry in the market, the art of collecting and interpreting data is a valuable skill, the demand for which has risen exponentially over the last decade. The potential that business intelligence(BI) and advanced analytics to enhance operations, decision-making, and brand communication cannot be understated. Visualizing and exploiting the narrative potential of your enterprise’s big data can transform the organization on many levels, and improve your brand’s impact both within your team and among your customer base.
Application Modernization at Speed and Scale Enterprises are pursuing greater application scalability, cost efficiency, and standardization with containerization and virtualization platforms. So, what’s the difference? Containers are a type of virtualization technology that allows users to run multiple operating systems inside a single instance of an OS. They are lightweight and portable, making them ideal for running applications across different platforms.Explore
Container Orchestration or Compute Service? Amazon Web Services (AWS) offers a range of cloud computing services to meet enterprise needs. Included in its service offering is the elastic compute service (ECS) and elastic compute cloud (EC2). Choosing between these two services can be difficult, as one focuses on virtualization while the other manages containerization. In the following article, we will explore the differences between Amazon ECS and EC2 to help you better understand which service is right for your use case.Explore
What is Application Modernization? Application modernization is the process of converting, rewriting, or porting legacy software packages to operate more efficiently with a modern infrastructure. This can involve migrating to the cloud, creating apps with a serverless architecture, containerizing services, or overhauling data pipelines using a modern DevOps model.Explore
What are the Differences? Though often used interchangeably, data pipelines and ETL are two different methodologies for managing and structuring data. ETL tools are used for data extraction, transformation, and loading. Whereas data pipelines encompass the entire set of processes applied to data as it moves from one system to another. Sometimes data pipelines involve transformation, and sometimes they do not.Explore
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore
The Rise in Big Data Analytics According to Internet World Stats, global internet usage increased by 1,339.6% between 2000-2021. With nearly thirteen times as many people using the internet, this has resulted in a massive increase in the amount of data being processed daily. Our increased sharing and consumption of digital media also compounds this increased usage to create an enormous pool of data for big data analytics firms to process.Explore