Convergence of data management and content management
I would like to get your view on the convergence between enterprise data and content management. Is there such a convergence going on and how does it show? What are the major challenges you see for such a convergence to happen? Can it even be done? Should it be done (what are the main benefits of such a convergence)?I also provided one of my own graphics (see below) to illustrate what I see as the main differences between data and content. The point with the graphic is that data, as it is more structured than content, provides less context than content.
Billy Cripe, Director of Product Management for Oracle Enterprise Content Management Products, responded as follows:
He continued the discussion on his own blog where he developed his answer to my question further. I believe that Billy Cripe's reasoning is very much aligned with my own view on Enterprise Information Management (EIM) as a "unifying" discipline. ECM has done a good job at unifying various disciplines that deal with different aspects of managing unstructured content of various types and formats (Records Management, Document Management, Content Management, Digital Asset Management...), but I don't see how ECM could also bring Data Management under its umbrella. Instead, this role is tailor-made for EIM.This is a great question. I believe there is a convergence going on. I think that as unstructured information (content) becomes increasingly transportable (think XML) and reference-able (think micro formatting, semantic structures, etc) it becomes more and more data like.
Furthermore, I think there is increasing demand for context around data that is produced by, stored in, and leveraged by fairly sophisticated data management systems (e.g. databased, data warehouses, transactional systems). In the BI community there is a growing awareness that the contexts provided by content flavors the analysis / results that is easy to produce on/for structured data. That awareness is breeding calls for a convergence of the information (structured and unstructured) to provide a complete picture.
As organizations boot-strap themselves with home grown systems that bring together content and data for specific purposes, they see the clearer, sharper, stronger results.
The convergence of unstructured and structured data management into what some analysts have called Enterprise Information Management structures is the mechanism by which the enterprise data picture goes from blurry to sharp.
EIM focuses on information needs and how information assets need to be described, structured and organized in order to support these needs - regardless of how (structure, format, type) they have been encoded or what technologies are required to capture, manage and deliver them. Separating data and content only makes sense from a management perspective, but not from a usage perspective. My fellow blogger Henrik has expressed this nicely in his post "Bridging Data And Content For Enterprise Information Management (EIM)":
Information assets are based on data and content which means that successful EIM needs to bridge the traditionally separated areas of Data Management and Content Management. Both areas have been oriented to the production side of data and content including techniques for creation, integration, administration, access and delivery. Data and Content Management also work with e.g. security, quality assurance and consolidation into master sources.I would be happy if you would share your own thoughts and opinions on this subjects, so please don't hesitate to post a comment.