Big Data is critical in order to gain insights from different information systems and is part of a wide array of initiatives that are evaluated by many organizations. Having said that, we at Eccella recommend to our customers not to overlook the criticality of visualization in unleashing the understanding of the data, even on a smaller scale. In fact, it is often advised to start with a simple visualization of the data before embarking on a full blown Big Data analytics effort.
Data Visualization for dummies
Data Visualization is a presentation of data in a picture or graph. It has been used by organizations both traditional and progressive for decades, beginning with Microsoft Excel and it’s ability to draw graphs and charts off a set of data.
Unlike the traditional Excel format, modern data visualization is interactive and can run on top of multiple large datasets, often in real time, allowing the end user to not just see but also explore and interact with the data by asking very complex questions and getting visualized answers immediately. This enables the user to easily focus on decision-making critical information and makes it accessible to all by appealing to users throughout the organization – IT and business alike. Examples of visualization tools include Tableau, Qlik, Yellowfin and many others.
But why is this necessary? Can’t we stick to the old format of a report with some pictures, a bunch of findings, numbers and bullet points? With the Volumes, Velocity and Variety of information that organizations process today, it is almost impossible to present all this data in a static way. Our brains simply cannot process this much information, let alone rely on these static presentation methods for quick decision making.
Data Visualization helps simplify all of this by presenting an easier way for our brains to consume and understand large amounts of information. You might consider data visualization as a form of storytelling, which registers more effectively in human memory and allows it to more effectively recognize patterns portrayed by visual imagery.
4 Typical needs for Data Visualization
Essential to the process of analyzing data - Visualization is one of the very first steps data scientists will take to better understand the data they work with. This is because they are trying to get a feel for what the data looks like, how accurate it is and if there are any obvious patterns or connections that can be established. Without the visualization, especially when dealing with big data, this would be a very difficult and labor intensive task.
Accessibility of data - Without visualization, data is just a bunch of numbers accounting for the facts. It doesn’t tell a story and is open to different interpretations depending on the qualification of the person looking at it and the breadth of investment they made into analyzing it. Visualization tells a story based on analysis of the facts, often transforming the counterintuitive into a narrative that others can understand.
Time critical decision making – With decisions sometimes needing to be made on a dime, there might not be enough time to do deep-dive analysis of the data, process it for days or weeks and bring it back with insights. Visualization is critical in such situations as it makes all this data accessible and insights discoverable.
3D exploration of data - Virtual Reality (VR), which is expected to be one of the hottest trends in 2016, will play a big role in taking data visualization to the next level, allowing more effective 3D exploration of the data across multiple realms. This is currently done with layering visualizations onto a 2D display, which is somewhat cumbersome and ineffective. I recently got first hand experience with startups operating in this space, and I am convinced it is an industry game changer in how we will analyze and consume data in the very near future.
Are you planning to use data visualization on top of the data you worked so hard to bring into your organization, standardize and master? Are you visualizing data you just dumped into a data lake and looking to get insights from the raw information? We’d love to learn what tools you are using and how. Share with usor ask questions at [email protected]