Year after year, we become more aware of how data is now omnipresent in this world. As data has slowly established itself as the foundation of this modern era, many organizations now generate data in abundance—so much that more and more people are being inundated with this information, without enough understanding of how to make something out of all of it. Organizations now face the challenge of curating this data to try to monetize it so they can become more competitive in the market, bring in innovation, improve profitability, and, most importantly, survive.
Exploring data visualization
Sadly, most people think that because they’re not data scientists, they can’t do much with the data they have. But when people accept this data as absolute figures, they’re not made aware of the complete picture.
For example, we have this detailed dataset made by the National Oceanic and Atmospheric Administration.
Looking at the sheet, what did you learn? Now, check out the chart they made with the dataset.
With a time series chart, we’re now able to understand—albeit slightly—the trends of these surface temperature anomalies. Not to mention, the data is now more interactive and less… dull. Using simple visuals will allow us to review vast amounts of data in digestible chunks. When human resource managers maximize the data they generate every day, they’ll be able to identify how much employees’ perception of their manager affects their intention to stay or leave, and even churn out information that will help protect their employees in the midst of a pandemic.
These are among the simplest uses of data visualization. Data visualization is the process of taking raw data, and transforming it into graphs, charts, images, and even videos that explain the numbers and allow everyone to gain insights from them. By seeing information from a particular perspective instead of simply being told that this is the case, we’re able to create value, discover patterns, and identify trends. You can find other ways to visualize data here.
Humanity is visual
Red means stop. Green means go. This is a widely (and easily) accepted fact for the traffic control system of most populations. Content strategists go out of their way to ensure they use visuals wisely without interference. Customers are more engaged when infographics or videos are used, while photos help keep readers interested. It’s even proven that images help people retain information more quickly, improve their learning, and affect their decision-making. In fact, the part of the brain that manages visual processing takes up 30% of the cortex, while only 8% for touch and 3% for hearing. Evolving from hunter-gatherers, people scour the world to find things that are relatable and valuable.
Numbers then, evolutionarily speaking again, are not information humans are programmed to process naturally. When we see numbers, the human interpretation process is simply to identify whether it’s a big or small number, with an established baseline—but it’s almost always more than that. Organizations now take on the challenge of leveraging this limitation when explaining big or small numbers.
Strategizing your visualization
Harvard Business School highlights at least 17 ways to visualize your data, and that’s just what it deemed as essential for professionals. These include pie charts, bar charts, histograms, scatter plots, heat maps, word clouds, network diagrams, and many more, showing the many techniques you can use to allow your audience to interpret your data and draw conclusions more easily. Knowing which one to use and when will allow you to leverage your desired message.
Know that your data visualization technique should vary based on your purpose. Pie charts are helpful when illustrating proportions, histograms show data distribution over a defined interval, heat maps demonstrate differences through color variations, and scatter plots display the relationships between variables. There are many ways to start analyzing and integrating data into your business, making meaning out of its original raw form.
Now that you have a good idea of how to present your data, the question is: what’s your desired message?
Tell your story
“Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context for and inspire action from your audience.”
Narrative is highlighted as the most crucial part of storytelling, and data storytelling is no exception. Because everyone is inundated with data every single day, we choose what we consider as worthy of seeing, observing, and remembering. As a business owner, a student, a researcher, or whatever status in life that got you interested enough to read about data visualization, it’s now your role to turn data into insights, and insights into action.
Crafting a compelling narrative based on your data and visualizing this to get your message across is key. For example, let’s say you are on a mission to demonstrate the concerning plastic waste buildup in the world’s oceans. With this situation, we can assign the oceans as our main characters, the current state in the year 2022 as the setting, and the plastic waste buildup as the conflict.
Based on the chart above, the environment-loving side of you can now present a long-term goal for better sustainability of our plastic resources. Data visualization can help us walk our audience through each element of your story and communicate to them the message the data tells. Without effective storytelling, insights and ideas can be easily ignored by your audience.
Use the right tools
There are many tools available today to help you with data visualization. Platforms like Tableau, Power BI, Qlik, and Looker are just a few of the big names that offer true visual analytics and data made more accessible to your target audience through visualization. They provide solutions that make use of machine learning, artificial intelligence, statistics, and natural language processing, empowering more people and organizations to use the data they have to its fullest extent. Similar to identifying which data visualization technique is best for your data, knowing which tool is right for the job is also important. As data visualization is a subset of data science, we must give it the same level of care and thought as much as we would starting from data collection, analytics, and all the way to representation.
Collect your thoughts or collect dust
Visualization gets more challenging when you’re managing big data. Datasets regarding satellite observation data, crime rates, and vaccination rates are just some of the vast repositories that anyone can access. For example, the Federal Bureau of Investigation (FBI) came up with an interactive way of showing the crime rate per state, making the data easily read and interpreted by anyone who knows how to click on parts of the United States.
Big data is needed for these kinds of complex visualizations, and getting this accurately and efficiently is crucial. Nowadays, big data is even more necessary in solutions for natural language processing (NLP) tasks. Because of the inherent complexity of natural languages, big data is needed to harness the power of information and build data-driven solutions.
Peter Norvig, an American computer scientist who once worked as a director of research and search quality at Google, shared compelling findings on the frequency of English words, using over 743 billion words as a sample. This was after Mark Mayzner, an older researcher, persuaded him to use the power of Google and build up on his 1965 work on the same topic, but with a sample of only 20,000 words.
Here’s another visualization of Norvig’s data—this time, turned into bubbles!
Natural language data may be intricate, but with the emergence of big data, we can now tackle NLP tasks and even get rewarding, and even mind-blowing, results. Big data collection then requires a methodical approach to gathering and measuring vast amounts of data from multiple sources, as this step lays the foundation for your message.
Here at BAVL, we don’t shy away from tasks as huge as data collection. Our platform serves as an all-in-one venue for training data collection, data annotation, and data cleaning, allowing us to build language datasets of any size, especially for Asian language localization. In our ready-to-use BAVL Language Dataset Library, we have datasets with up to 1.5 million business-oriented English sentences tailored to specific situations and scenarios to help Korean companies run operations on a global scale—all with their respective Korean translations.
In collecting your data, the process should be efficient and accurate. When handling big data and training data, traditional software simply won’t do anymore as its management is often combined with machine learning or other data analytics processes that will make the system more automated and less of a headache. If people did this manually, they would spend years trying to accomplish the task.
Data is powerful. It can be used to improve our lives, but only when correctly processed. With so much data available, it’s essential to know how it’s being used, what it means for people’s privacy, and how we can maximize it for our own purposes. We’re excited for you to witness firsthand how BAVL can help you collect the training data you need and leverage it further with the many ways of data visualization, natural language processing, and machine learning and AI data solutions, and, hopefully, strike digital gold. Schedule a consultation with us or visit the website to learn more!
