Last week at a holiday dinner, a friend of mine who is a partner at a large accounting firm shared with me this story: He asked someone to prepare an archive of all of the important documents produced this past year. After three weeks of work, this person proudly showed his boss (my friend) a shelf containing 11 binders with all of the documents neatly printed. My friend opened one of the binders and quickly realized there was no order to these documents, not even by date or client! “But how can I get all of the documents for Customer XYZ that I wrote in June?” my friend asked. Complete silence. Painful silence.
Although this story was a source of frustration for my friend, it was a source of fascination for me. This is a perfect example of what is happening all around us: data is becoming metadata. Data values like ‘Customer XYZ’ are expected to become metadata tags to help with search and retrieval of other data such as documents, web pages, and images.
Search engines spoil us and we expect everything around us to be neatly organized and at our fingertips after typing in a keyword or two. What we type in is data that we expect to behave as metadata and describe some other data (Remember that “data about data” definition we like to give for the term ‘metadata’?). Data becoming metadata leads to more sophisticated requirements we as data people need to address. This raises a number of interesting challenges for us. What challenges does data becoming metadata raise for us data folks?