The National Institute of Standards and Technology wants to make sure everyone means the same thing when they talk about “big data."
In an attempt to standardize big data discussions, the Commerce Department agency issued a draft “Interoperability Framework,” defining data analytics-related phrases, outlining management templates and describing common use cases for large data sets and other large amounts of information traditional data architectures can’t efficiently handle.
“We want to create this ecosystem [in which] data scientists concentrate on their own data” instead of worrying about external factors, such as whether their data sets might work on new computing platforms, said Wo Chang, a NIST manager who worked on the report.
NIST led the “Big Data Working Group,” which included members from the private sector, academia and government, to write the framework. It released the first draft of the seven-volume publication earlier this week, and is accepting public comment until May 21.
The framework aims to “stimulate collaboration among industry professionals to further the secure and effective adoption of big data,” the report noted.
NIST is also pushing for international adoption of such a framework. Chang spoke to Nextgov from Germany, where he was meeting with international representatives about how to make the framework a global standard, he said.
Eventually, Chang added, the framework could evolve into an interface, in which data scientists could find guidance on common data applications and tools to help them comply with standards, among other features.
The framework also includes several general use cases, meant to guide governments, data scientists and organizations through big data applications. For instance, according to the draft, the Census Bureau is trying to use big data techniques to increase the quality and reduce the costs associated with field surveys.
The draft framework is the first step in a long path to interoperability. Once established, the draft noted, these definitions and outlines "will form the basis for evolution of existing standards," and could prompt data-focused groups to evaluate new standards.
(Image via Bloomua/ Shutterstock.com)