An IT mindset has dominated the way organizations view and manage their data. Even as issues of quality and consistency raise their ugly head, the solution has often been to turn to the tool and approach data governance in a project oriented manner. Sustainability has been a challenge, relegated often to IT managing and updating data management tools (MDM, data quality, metadata management, information lifecycle management, and security). Forrester research has shown that less than 15% of organizations have business lead data governance that is linked to business initiatives, objectives and outcomes. But, this is changing. More and more organizations are looking toward data governance as a strategic enterprise competence as they adopt a data driven culture.
This shift from project to strategic program requires more than basic workflow, collaboration, and data profiling capabilities to institutionalize data governance policies and rules. The conversation can't start with data management technology (MDM, data quality, information lifecycle management, security, and metadata management) that will apply the policies and rules. It has to begin with what is the organization trying to achieve with their data; this is a strategy discussion and process. The implication - governing data requires a rethink of your operating model. New roles, responsibilities, and processes emerge.
Gene briefly explores the misunderstanding between “Enterprise IA” and “User Experience IA.” This tension was well characterized by Peter Morville almost 10 years ago (See “Big Architect, Little Architect.” Personally I think it’s clear that content is always in motion, and unsupported efforts to dominate and control it are doomed. People are a critical element of a successful IA project, since those who create and use information are in the best position to judge and improve its quality. Many hands make light work, as the saying goes.
For example, if you want a rich interactive search results page, you need to add some structure to your content. This can happen anytime from before the content is created (using pre-defined templates) to when it is presented to a user on the search results page. Content is different than data, a theme Rob Karel and I explored in our research on Data and Content Classification. For this reason, IA is both a “Back end” and a “Front end” initiative.