Big data has been a massive trend for the past few years now, and it's no surprise that analytics has become more and more ingrained in corporations across the globe. What started off as something only huge corporations spent money on, is now trickling down to much smaller companies. If you have way more customers than you can remember the names of, then they stop being people, and they become data. As much as we all want to offer personalized services to our customers and try and tailor-make solutions for their unique needs … the bigger a company grows the harder it is to achieve that. However, thanks to big data, little data, and analytics, with AI powering it all, it's possible to actually make every customer feel special and unique. However, a machine is dumb.It does what it is told, and then extrapolates, and this only improves slightly with machine learning. We still need humans to get the machines started. That's where you come in …
There's a dire requirement for individuals who can understand and present data in intelligible and captivating ways. Data visualization is not just charts and graphs, there's more to it than that! There are creative examples of data visualization such as heat maps, animated graphs, interactive data, choropleths, contour maps, and other such unorthodox methods that really stand out.
Some jobs actually use data more than others, such as data analysts, data scientists, journalists, researchers, academicians, and business analysts. But all jobs these days need it in some form or the other. Why? Because humans need visual aids to understand things, and despite the emergence of AI, you're always going to be talking to a human in the end …
Visuals are processed 60,000 times faster than simple text or numbers. Our brain requires only 13 milliseconds to process things like color, size, shape and other such concepts that visuals are trying to depict.
AI can easily find inconsistencies in data and remedy them in a simple context. However, when there are numerous data sources that intertwine with each other and advanced contextual knowledge is necessary, a human analyst's experience allows them to accelerate the process of correction. However, evidence shows that AI is getting increasingly better at correcting even complex-level errors with advances in contextual understanding. Humans are going to need to up their game to keep up.
While AI can tell you what data to show and put it in a graph, you still have the advantage of knowing your audience, and can add value to even the smartest AI's work. The AI is an ally, not an enemy in this field. In fact, by taking away the complexities of working with databases of different types and leveling the playing field for humans, AI will help open out this field to more of us. What will matter is asking the AI the right questions, knowing how to display the results, and knowing how to impress your audience. You may be able to do this in your company far better than an external data visualizer, who does not understand the nuances of your company or business.
If you're entering the 20s, still relying on your Excel sheets and boring Excel graphs, we're sorry to tell you that you might be left eating the dust of those who embrace the art of telling stories with data. we recommend you learn Tableau, as it can handle enormous amounts of data, has AI and ML applications, and integrates well with advanced database solutions such as Hadoop, Amazon AWS, MySQL, SAP, and Teradata. Others such as Qlikview, FusionCharts, Highcharts, Datawrapper, Plotly, Sisense, Looker, and more, are also solutions you should check out. Coding languages such as SQL, Python, JavaScript, and R are also good, as is learning about VR, as the future of data visualization may lie in how immersive an experience you create!