A recent discussion was started in an online group that I frequent. The original post asked the question about the importance of contextualizing Big Data. I think there are a few “dimensions” to the question 🙂
One covers the point that is that data is not just a discrete resource. It’s actually a living asset, which has a lifecycle and that data lifecycle management spans acquisition through termination and all the lies in between. Another point is that it can be helpful to distinguish between Master Data and Metadata, though (IMO) Master Data is just a subset of Metadata, where Master Data represents a much smaller subset of Metadata which can be “mastered”. The rest of Metadata is left to pattern based discovery – which some of the newer tools are beginning to address.
This is both the challenge and opportunity for information architecture going forward. Contextual relevance is the vast set of patterns that support POVs, which makes the data meaningful for individual consumption. The same lifecycle approach applies to this data, but we are still in the early stages of discovery tools and the ability to continuously improve this type of data. The concern that I see with current practices with capturing, warehousing, and using contextual relevance is the lack of consistency and understanding that it is far more complex than traditional “master data” since we’re now having to blend together Rules + Structures + Data. Once we “master” this capability it will become one of the core competencies for every organization.
I’m actually looking forward to when this emerges as a consistent and accurate discipline because it will create a multiplier effect for organizations, their stakeholders, and entire value chains.