Several of the readings on data architecture, Gartner’s “Managing Information as ans Asset” and Forrester Research’s “Simplifying Information Architecture”, provided great insight into how businesses could better align their information architecture with their business strategy. I found both articles relevant as I have seen both internally within my organization and with our sponsors a disconnect between business strategy and value and information. It is my opinion that this is an area where many enterprise architects struggle.
Many of the sponsors I work with, are quick to say that their information is their most valuable asset (forget the people). The problem with this is that not every piece of information has the same value to the business. Working with primarily DoD sponsors, I find that many of these people understand that some information is more valuable to the business than other information, but they tend to impose guidelines on the their information architecture that assumes all information has the same value. Obviously there are a number of problems with this view. As Gartner points out you need to determine the value of your data and its impact to the organization, risk, decisions, and business. Failure to perform this analysis often results in numerous problems. For example, truly not all information is of the same value, however, if it is all protected and maintained to the same standards, this often taxes the resources available to store and manage the data. We had one sponsor who required a large computational grid for engineering simulations. But their data management was all within the confines on one particular storage architecture that was not designed to handle the outputs this computational grid could produce. The solution was to upgrade the storage for this grid based around the specific use case of the engineering needs. What happened, was the sponsor thought all the rest of their organization needed this upgrade too. There were not enough funds to handle all this work and only parts got implemented with the rest phased in over the years.
These types of failures have far less to do with the skills of the data architects, but of the internal politics of the organization.