Bridge: Young girl sitting on the steps of her duplex hmmm Why did God choose to make my life so complex?
Breathing to the beat of slow sad drum Trouble is her only source of fun I can almost guarantee you it will get better sooon Please click here if you are not redirected within a few seconds. They do not require the kind of political and financial capital that these other huge initiatives do, and that makes all the difference in the world.
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This makes it difficult to sell new products into this environment. If we move our knowledge bases out of language-level domain models, relational schemas, frameworks, etc. While any developer worth her salt would champion writing less code, many do not have that kind of foresight. They are not drawn toward technologies that seem to reduce their influence in the organization.
Department heads whose groups produce information are equally suspect about the fluidity we are considering. I have seen this negative reaction in too many places to ignore that many organizations have employees actively working against larger goals and interests in favor of career-boosting tactics. This makes introducing new technologies into the organization even more difficult through no fault of the technologies.
Fundamentally, however, I think the problem comes down to the fact that the Semantic Web technology stack gets a lot of criticism for not hiding the fact that Reality is Hard. The kind of Big Enterprise software sales that get attention promise to hide the details, protect you from complexity, to sweep everything under the rug. And, as far as I can tell, these are basically lies told by people who want to lock you in.
Trading off real complexity for the limitations of a tool that has its own shortcomings artificial complexity is not exactly a win.
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Even something as simple as a relational or XML schema is a simplification, a forgetting of complexity, a focus on only what is important in a certain circumstance. For specific uses, there is nothing wrong with this. In the big picture, however, this lossy storage and organization mechanism is a problem. To understand how organizations plan and make decisions requires an understanding of how Data becomes Information and then Knowledge and then Wisdom.
We go from observing and recording to organizing, understanding and predicting. Knowledge and Wisdom are contextual, validated, prioritized orderings of Data and Information. If we ignore that context and treat knowledge management as simple information retrieval, then we are losing the value of the perspective of experts.
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We come to conclusions, therefore, in contextual ways. Not everyone within an organization has the same needs of their underlying data. They may not have the same goals, abstractions or expectations. We currently attempt to solve the problem by extracting, transforming and loading ETL our data into separate systems to meet these different needs. This is a ham-fisted and antiquated way of thinking about it though. Semantic Web technologies allow a much finer level of granularity for reuse and expect information to be used in different contexts. Change is embraced at the most basic levels.
The desire to control manifests itself as a desire to own.
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We want to put everything into One Place so we can control it. Big Data helps enable these impulses, so it is a very popular set of technologies. The contexts that used to be scoped to individual applications are now becoming less well defined.
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Targeting information for specific channels and users with different needs can dramatically alter what is important or valid. We need data models that support this fluidity. Tweet Share Email.
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