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Today, information management is no longer about Enterprise Content Management (ECM) or Knowledge Management (KM) systems. It is multi-dimensional. Furthermore, data explosion has led to fragmentation and creation of data silos hosted in diverse systems with different characteristics. This has resulted in confusion around search/retrieval, reporting, and lifecycle governance. Additionally, there is the risk of losing business intelligence opportunities, and convoluted discovery costs and effort. In this backdrop, how does a taxonomy help in information governance?

Corporate taxonomy is defined as, “The hierarchical classification of entities of interest of an enterprise, organization or administration, used to classify documents, digital assets, and other information.” Another way of describing this concept is a taxonomy in a biological context. Straight away, it becomes evident that the two key concepts are inheritance and specialization of characteristics. In addition, it is apparent that there is very little difference between a biological taxonomy and its object-oriented counterpart. For instance, in both the contexts, characteristics are inherited from the top parent levels, down to the children. However, those inherited in the corporate context are metadata, syntax, and context, and retention requirements. Furthermore, each subsequent level offers opportunities to specialize— to define some new characteristics unique to the child.

In a complex data ecosystem, understanding the types of relationships can yield deep and meaningful insight into enterprise data. This type of insight may reveal hitherto unknown relationships between traditionally siloed data. Significantly, the core principles behind this type of architecture design pattern can be used to model unstructured, semi-structured and fully structured data sets.

The architecture design pattern approach of viewing data is borrowed from a style of programming called object-oriented programming (OOP). At a high-level, it is a way of managing clusters of code or content as objects and creating blueprints and instances out of that blueprint. Furthermore, depending on the requirement, it is possible to expose or abstract parts of the object. In addition, the objects come from classes having properties – characteristics that describe the class (i.e., metadata). In the corporate context, a document class defines the type of document, and a folder class determines the type of folder, etc.

This object-oriented method of modeling data structures has been around for many years, and it can be adopted for enterprise information governance. For instance, in this architectural approach, the out-of-box document class contains several fundamental properties that are generic. They are the taxonomy designer’s starting framework. The subsequent level is the first level of specialization: the enterprise document class. Its key properties are bound by the enterprise, and it makes sense within its context. Every unique document, every single record will have at least these properties because they will be inherited downwards to every subclass of that base enterprise document class. The third level will involve content-centric design, organizational design, and functional design. This design style can be used in an object-oriented taxonomy.

Adopting an object-oriented approach to enterprise information governance begins with the end-user. The idea is to start with a line of business and engage with them to generate a checklist and synthesize an understanding of the document characteristics (volume, format, input); organizational structure; process; security; retention and reporting.

The goal is to get the narrative of what drives the data. Here, it is important to remember, taxonomy development is largely a siloed activity, with department-specific esoteric nuances. Therefore, deep diving into these six related dimensions at an enterprise-level will bring it all together and might make the stakeholders realize they need additional properties. Subsequent to capturing the required metadata, it is time to discover why it is needed. Once the blueprint is created, it should not be difficult to implement it.

An enterprise object-oriented data design blueprint is an enabler. Whether it is getting information into an API-ready state, or enabling a common language, this technical data modeling is the path that allows positive momentum. Overall, enterprises that adopt and manage a taxonomy should operate more efficiently, have a lower risk profile, and be set up to have a successful information governance program.

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