“Buzz” words are suitably named. The term perfectly describes the swarm of annoying phrases and acronyms flitting around our heads with every new idea in the reporting and analytics world. The latest such pesky expression is “big data”.
Used to define both the volume of data and the technologies designed to handle it, big data typically refers to data sets that have grown beyond the limits of conventional methodologies.
The usual examples used to illustrate the big data landscape are large-scale scientific, e-commerce, or social media ventures. The immense magnitude of big data is expressed in terms of size like tera-, peta-, or exabytes (1 quintillion bytes/a billion gigabytes). On the low end, the volume of data in the Library of Congress (20 terabytes) is often cited in order to give a relative measure of these gigantic data sources.
However, average reporting and analytics practitioners ask the question, “What relevance do these massive data sets have to my day-to-day activities”?
Although big data seems like the concept du jour or the domain of gigantic enterprises, it is significant to everyday analysts for two reasons.
First, even in a small enterprise, the volume of data available to analyze is growing fast. The latest versions of ERP and CRM software create more data and metadata (data about data) than ever before. The data boom is being fueled in many cases by data arising from “smart” hardware. Even such mundane machines as heating and cooling equipment are piling up potentially useful data faster than most organizations can use it.
Second, Big Data growth is spawning technology innovations that will find their way into everyday practitioners’ hands in the near future.
In-memory processing – By eliminating constant disk reads, large volumes of data are maintained in RAM memory which dramatically speeds up analytical tasks.
Analytics-as-a-service – Rather than invest in the infrastructure to handle large data volumes, organizations will increasingly be able to have their data hosted externally, accessing it remotely on a fee for use basis.
Mobile analytics - it would seem logical that pushing analytics out to mobile devices such as the iPad would not be dependent on large data volumes. However, the environment of pervasive data that results from these large volumes encourages mobile technology development.
Big data might not yet be an elephant in your room but the reporting and analytics innovations it creates will eventually make their way into your organization.