Information architecture and indexing is the “heart” of information management as it is very relational. Information governance is all about ROI and records management is about compliance. What sets information architectures apart is that it deals with knowing the customer and caring for them, whether they are external or internal. According to the Certified Information Profession program, information architecture when done right will “improve user experience and build brand loyalty among customers, and organizational loyalty among employees.”
Questions to ask when assessing your information architecture are as follows
- Are the “buckets” to organize and label shared information to granular and not returning enough results?
- What kind of controlled vocabulary is facilitating the usability and findability of information?
- Is the taxonomy being used intuitive to your users?
- Are knowledge sharing tools in place such as thesauri?
- How often are content and metadata audits performed?
The way you architect your information is dependent on your user community. If you are working with unstructured information that does not have a systematic standard of categorization or classification then a folksonomy or social tagging may best meet your needs. Folksonomy is a largely unregulated, but collaborative and personalized approach to tagging that takes a group of individuals’ perspective on how information should be tagged. They often provide clusters of tags that communities can rally around. Social tagger is web-based end user tagging. Through simple free-form interfaces users leave feedback and others can tag and retag content as they deem appropriate. Although it takes time, by consensus a group can develop new categories of information. In today’s overflow of digital information folksonomies and social tagging are everywhere, from enterprise content management such as SharePoint, to “personal” collaboration sites like Flickr or Delicious. A down side to systems like this is that you can retrieve broad results with little precision as the vocabulary is not controlled.
A customer base in academic and scientific communities typically prefer highly structured information architectures to help analyze domain knowledge, examples of such include ontologies and topic maps. These take make work but provide more precision as they are tightly structured controlled vocabularies. An ontology represents an entire domain of knowledge and applies rules to define terms and the relationship between terms. A topic map is a type of ontology that provides a visual representation of a knowledge domain by showing the number of occurrences and all of its associations. A great example of ontologies and topic-maps that are not in academic and scientifics domains are the Music Genome Project and The Music Map.