Data Storytelling vs Data Visualisation

Richard Jones our consultant managing the role
Posting date: 11/7/2019 9:00 AM
The demand for “unicorn” employees is growing. Those with humanities and communications skillsets are now in demand, alongside those who specialise in Computer Science, Data Science, and anything technology-related. So, what exactly is the world looking for today?

Well, with the plethora of online learning opportunities available, the ramping up of technology courses both online and offline, and a cadre of storytelling books on the shelves; answering the question can seem daunting. But there are two ways in which you tell your story. They’re not separate exactly, but they do have their own parts to play.

What is Data Storytelling?


In a nutshell, it’s the ability to tell a story using the Data you’ve collected and analysed. So, how does this work exactly, and why would someone use it? This way to explain what’s happening the Data to stakeholders and executives helps paint a picture of their company in a different way. And unlike traditional storytelling, this type has facts and figures to back it up.

But that’s only half the story.

By taking a wider view of Data storytelling, you can provide stakeholders with the big picture in a way that’s relevant and engaging. But you still need the Data to back it up. This is where Data Visualisation comes in. Think of it as the Graphic Novel of your business’s story. Content is the narrative and images are the visual behind the narrative cementing the story in your mind.

What is Data Visualisation?


This is how you define your story, and you can do this in a variety of ways. You can use Data Visualisation software to help guide your story and keep you on track in the details. Seeing is believing and can help persuade a call-to-action from decision makers.

In a nutshell, Data Visualisation enhances storytelling using traditional techniques such as a “hook”, and embodies the basic structure of beginning, middle, and end. And while those in the marketing world know how to draw emotion and get people to act on it, Data Storytelling provides a new, useful skill for Analysts.

What are the Elements of a Good Story?


First, understand the story you’re telling. While visualising the results happens at the end to cement the story you’re telling, the heavy lifting is done in Data preparation. It’s not unlike baking a cake; you spend more time buying (collecting/gathering) the ingredients, mixing them, and organising (which pan, how long, and at what temperature), than you do baking the cake. The end result is the smell of something freshly baked, that looks amazing (visualisation), and tastes phenomenal – where the story and the visuals come together.

Second, identify the main characters; your Data elements. You need ask yourself what is the relationship between your characters (Data elements) and was is their role in the story.

This can help you bring together two disparate Datasets. Ask yourself, what tools would you need to make things work together? This is the preparation side of things. Once this is sorted, you have the elements of your story. 

Keys to Good Data Storytelling


  • Choose the right subject
  • Source credible Data
  • Craft an interesting, engaging, or enlightening narrative
  • Ensure your story provides meaning and value
  • Ensure you’re using credible Data to back up your story.
  • Blend narrative and visuals which can cement the information and make your story stick.
  • Choose relevant, useful topics for a more engaging story. You want your listeners to resonate with what they’re hearing or seeing. When people are engaged, this is where the emotion comes in.

Stories come from a variety of sources, but are essentially either internal (you or your organisation) or external (trade publications or industry leaders). For content marketing, external sources offer a variety of ideas to tailor your story around. But what best will resonate with your audience is your internal story. Those tailored to pain points or interests are particularly valuable. 

Remember that Data storytelling is not a story about numbers; it’s about humans and how those numbers affect them. 

If you’re interested in Data Storytelling and Visualisation, we may have a role for you. Take a look at our current vacancies or get in touch with one of our expert consultants to find out more. 

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

Weekly News Digest - 11th-15th Jan 2021

This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of data and analytics. KDNuggets: 20 core Data Science concepts for beginners The field of Data Science is one that continuously evolves. For Data Scientists, this means constantly learning and perfecting new skills, keeping up to date with crucial trends and filling knowledge gaps.  However, there are a core set of concepts that all Data Scientists will need to understand throughout their career, especially at the start. From Data Wrangling to Data Imputation, Reinforcement Learning to Evaluation Metrics, KDNuggets outlines 20 of the key basics needed.  A great article if you’re just starting out and want to grasp the essentials or, if you’re a bit further up the ladder and would appreciate a quick refresh.  Read more here.  FinExtra: 15 DevOps trends to watch in 2021 As a direct response to the COVID-19 pandemic, there is no doubt that DevOps has come on leaps and bounds in the past year alone. FinExtra hears from a wide range of specialists within the sector, all of whom give their opinion on what 2021 holds for DevOps.  A few examples include: Nirav Chotai, Senior DevOps Engineer at Rakuten: “DataOps will definitely boom in 2021, and COVID might play a role in it. Due to COVID and WFH situation, consumption of digital content is skyrocket high which demands a new level of automation for self-scaling and self-healing systems to meet the growth and demand.” DevOps Architect at JFrog: “The "Sec'' part of DevSecOps will become more and more an integral part of the Software Development Lifecycle. A real security "shift left" approach will be the new norm.” CTO at International Technology Ventures: “Chaos Engineering will become an increasingly more important (and common) consideration in the DevOps planning discussions in more organizations.” Read the full article here.  Towards Data Science: 3 Simple Questions to Hone Python Skills for Beginners in 2021 Python is one of the most frequently used data languages within Data Science but for a new starter in the industry, it can be incredibly daunting. Leihua Yea, a PHD researcher at the University of California in Machine Learning and Data Science knows all too well how stressful can be to learn. He says: “Once, I struggled to figure out an easy level question on Leetcode and made no progress for hours!” In this piece for Towards Data Science, Yea gives junior Data Scientists three top pieces of advice to help master the basics of Python and level-up their skills. Find out what that advice is here.  ITWire: Enhancing customer experiences through better data management From the start of last year, businesses around the globe were pushed into a remote and digital way of working. This shift undoubtedly accelerated the use of the use of digital and data to keep their services as efficient and effective as possible.  Derak Cowan of Cohesity, the Information Technology company, talks with ITWire about the importance of the continued use of digital transformation and data post-pandemic, even after restrictions are relaxed and we move away from this overtly virtual world.  He says: “Business transformation is more than just a short-term tactic of buying software. If you want your business to thrive in the post-COVID age, it will need to place digital transformation at the heart of its business strategy and identify and overcome the roadblocks.” Read more about long-term digital transformation for your business here.  We've loved seeing all the news from Data and Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

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