Recently, as I was driving in listening to National Public Radio (NPR), the topic of conversation was Edward Snowden and the National Security Agency (NSA), again. If you’ve been listening to any of this coverage, it seems the news media has a new catch word “metadata” that they are throwing around when discussing the data the NSA has been collecting.
In the 1980s, “The Bob Newhart Show” featured the brothers Darryl who never talked until the last episode. They were always introduced as “This is my brother, Darryl, and this is my other brother Darryl”, which meant it was at times difficult to distinguish between the two. If there had been some “metadata” on the two Darryl bothers that uniquely identified each brother, perhaps their appearance during the show might have been less confusing to others that had to interact with them.
So what is metadata and why is it important? Basically, metadata is data or specifics about the actual data. Metadata within software solutions like SAS Data Management and SAS Visual Analytics is used to uniquely identify a table, text file, analytic data model or exploration. It identifies your data as numeric or string, the length of your data, when the data was created or modified and many other facts about the data. Metadata can be used to define the relationship of your data to other data, known as lineage, when assessing the impact of a data to an organization.
Metadata may be a new word to many, but for SAS, metadata is a term that’s been used for years. SAS has understood that metadata and lineage are the keys to providing a consistent view and understanding of data and its relationship to other data as it was shared across SAS’ solutions. Metadata management and adherence to metadata standards are ways to apply a form of data governance to ensure interoperability of these applications. Ensuring that your metadata is complete, accurate and current at any given time during processing is key to maximizing the benefits of any data management or analytic solution.
As companies struggle with understanding their data, how to leverage their data and treat their data as a corporate asset, a solution that utilizes a shared metadata technology across its platform will eliminate any confusion as to who that “Brother Darryl” really is as it moves throughout an organization.
Companies that acquire technology through acquisitions to gain market share or presence in data management and analytics are faced with having to integrate or stitch together disparate metadata management technologies. In many cases, the integration of metadata technology into an existing metadata framework can be complicated, and in some cases, non-existent. Unless you have an underlying, consistent metadata story that allows you to uniquely identify one “Darryl” from the other “Darryl”, organizations will struggle to identify and understand their data, extract value from their data, and streamline processes built around and on top of their technologies.
