In this installment we look at how to identify interesting data and solve difficult information problems in your organization. A taxonomy of knowledge gaps is described and how Knowledge Maps help bridge these gaps and provide real value.
In this piece we consider the future of "meaning" based data design versus what has historically been a structural approach. The transition from structure to meaning will impact data management practices for decades to come. To read more click here... http://www.dataversity.net/knowledge-maps-structure-versus-meaning/
The third article in my series on Knowledge Maps has been posted on Dataversity. This article looks at the traditional relational database approach to metadata repositories and ITIL CMDB's, why they don't work, and how Knowledge Maps built on the property graph model as exemplified by Neo4j is the answer. You can read it here... http://www.dataversity.net/knowledge-maps-new-model-metadata/
The second article in my series on Knowledge Maps has been posted on Dataversity. This article describes the components of knowledge maps and describes some of their common characteristics.
Part 1 in the series on Knowledge Maps has been published on Dataversity. This article explores the common information (lack of) awareness problem all organizations have and introduces the solution - Knowledge Maps. Please read the full post at http://www.dataversity.net/knowledge-maps-problem-trying-solve/
John Singer will be speaking on graphs and metadata knowledge management at the Data Architecture Summit this November.
Great News - my blog posts will be appearing on DATAVERSITY !