Cost, in terms of financial outlay and the organizational effort, are common roadblocks to beginning work on a knowledge graph. An alternative and beneficial approach would be a boutique knowledge graph--a way to start small and return actual value.
Starting small, however, should not be considered a second-tier solution, as they are a better choice for two reasons. One, knowledge graphs have the innate ability to scale. Besides, their foundation in shared tools makes these smaller systems a better option. Two, enterprises can test the waters before they dive into making larger systems.
The design principles for building a small foundational system that will work, scale, and deliver value, should incorporate the use of standards and unique identifiers; structure content for reuse; address business objectives; and plan for larger contexts.
Incorporating these principles is important. For example, adopting standards makes data interoperable and readily reusable. Using unique identifiers ensures that each data point-each fact-can be uniquely identified. This is crucial to maintaining the integrity of the combined data set as it scales.
Furthermore, it is much easier to determine what should be built, identify when it is done, and evaluate the degree to which it is effective if the business objective behind the initiative is met. Also, by structuring content for reuse, the facts content communicates, and the relationships between those facts are described in ways that both humans and machines can directly understand. Keeping in mind the next larger context anticipates how the data we capture now will interact with the larger data environment in which it is situated. It is also why standards, unique identifiers, structuring content for reuse, addressing the business objectives, and planning for the larger context, are the fundamental design principles for boutique knowledge graphs.
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