The Extreme Data Challenge
From healthcare to energy, telecommunications to retail, logistics to financial services and more, the expansion of mobile, social, and cloud technologies led to the big data phenomenon: using data to make better business decisions across industries. Data streams have grown in volume, velocity, and variety. But storing and managing this data isn’t enough anymore.
As IoT devices and new data sources proliferate, data has never been as unpredictable and analysis as complex. Data may be big or small, static or streaming, structured or unstructured, long-lived or perishable, human or machine. Siloed legacy databases built for serial computing just can’t keep up.
We've moved beyond big data. We’re now experiencing an elemental shift from the service economy, informed by data, into the Extreme Data Economy, which runs on it.
A business’ most valuable asset isn’t a product or service. It’s data. But in the Extreme Data Economy, that data is only as good as the sophistication and speed of analysis.
Businesses must take action now: deal with the data deluge or drown in it.
Legacy databases can’t handle extreme data because they’re built for serial computing, not the IoT. But data analysis isn’t a linear task anymore. Saddled with legacy databases, companies can’t process the volume, velocity, or variety of data they receive in real-time, limiting their ability to analyze and act on that data.
With every new use case comes a new point solution. This cul-de-sac approach to technology has led to data silo after data silo, with no centralized way to process, manage, or connect data quickly or easily.