What Good Is Data Anyway?
Data has taken over. Are you using it, or is it overwhelming you?
Between social media, traffic metrics, and the Internet of Things, people are making more data than ever before. Unfortunately, data literacy isn’t spreading as quickly. Making informed decisions based on data, and communicating clearly with data, mean not just collecting it but forming hypotheses and understanding context. If you’re a developer looking for ways to fulfill clients’ data wishes, their data literacy determines how they interact with the platform, and you can make design decisions that help them make better data decisions.
Industry experts estimate that about 2,500,000,000,000,000,000 (2.5 quintillion) bytes of data are created every day. That doesn’t just include the 347,222 Tweets posted every minute, which are about 560 bytes each (adding up to 1 quadrillion bytes per year), but the metadata that accompanies each Tweet: date, time, location, mentions, retweets, favorites.
Consider also that, as Kenneth Cukier wrote in The Economist in 2010, “the amount of digital information increases tenfold every five years,” while Moore’s Law states that computing power doubles every 18 months. That’s just 10.67-fold every ten years. If the rate of data proliferation has increased at all since 2010, our ability to handle all that data isn’t keeping up.
Data literacy is not so prolific. It’s a major concern among teachers, who need to understand the data behind classroom assessments, and journalists, who dive for stories in complicated datasets. Health professionals need data communication skills too, and it’s so important that the UN advisory group on Data Revolution for Sustainable Development listed data literacy as a priority in its 2014 report.
If your goal is improving business strategy or communicating findings to clients, simply having the data is unlikely to accomplish it. Instead, how you use the data determines its benefit, and visualization is a central technique for becoming more familiar with a data set. H.O. Maycotte at Forbes also recommends using data in testing. Applying the scientific method to data is a great framework: the process involves making observations, making hypotheses, testing those hypotheses, making more observations, revising the hypotheses, testing again, and so on.
Once you are data literate, clients’ data literacy is the next way to improve data communication between you. If you’re developing a platform that lets clients explore their data, a white paper from the Data Pop Alliance recommends removing obstacles to data literacy by simplifying the data, providing context to understand the numbers, making everything from the data to the findings human-centric, clearly communicating the literacy lesson, and again, visualizations!
If you’re looking for an API that helps convey data’s message to others through your platform, talk to the folks at Popily about whether it’s the tool you need.
