Data exchange between organizations and data sharing inside organizations is necessary to effectively communicate needs and news. Digital data standards are intended to facilitate the massive amounts of data that results from the increasing volume, velocity, variety, and variability of newly acquired analytical data, which is necessary for confident, comprehensive material characterization.¬†Genetic Engineering and Biotechnology News brought this interesting information to our attention in their article, “Discovering the Fourth Paradigm in Data Standardization.”

In data workflows of all sizes, two factors contribute greatly to the onslaught. The first aspect is the automation and/or parallelization of specific high-throughput analyses on particular instruments. The second is the challenging implementation of the Internet-of-things (IoT) due to the tremendous assortment of computer-based data sources and their diversity of parts, performance attributes, and output of analytical data formats.

Analytical data is generally used for qualitative and quantitative investigations. Modern systems should address, first and foremost, the need to communicate.

Melody K. Smith

Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.