[IMAGE: YUTA ONODA]
Over the past decade, we have entered the Age of Big Data, where digital technology allows people around the world to transmit information at an unthinkable rate. Data is useless unless you can convert it to information and ultimately into knowledge. In recent years, Big Data has been what organizations use to describe their attempts to converting all of their data into useful information.
What is Big Data?
Big Data is a noun, but it also describes a process. To big data is a process that involves six steps. The first two are gathering and handling large amounts of data. The next two are the structuring and examining of the data. And finally, it is about discovering something within the data and delivering that back to your organization or the world. A lot of organizations are cycling around the first four stages. They are hoarding and warehousing large amounts of data, and they are doing a little bit of analytics. But they are not really penetrating the discovery and delivery side.
Companies are wrangling more customer data sources than ever before. Predictive analysis of exponentially large data sets. Data generation is increasing rapidly and its volume is astounding, it is also coming from a wide variety of sources. A report found that while consumers generate 80% of the data, while businesses have become custodians of this data.
Consumers are producing data through devices such as phones, tablets, TVs, drones, cars, GPS systems, social media, messaging apps, games consoles, and wearables. But most of this data isn’t usable.
For most businesses the data comes in and it sits there, like a big giant hairball, that is very difficult to unravel. It is the job of Data Engineers to look at this hairball and find the useable threads in it.
Most of their work entails cleaning up databases, so getting rid of unusable data, and finding information that can be translated into something useful.