We have described the full process of knowledge management, from data acquisition, to cleaning, model building and querying.
In contrast to this view, we have seen in the previous part of this paper, that the implementation of additional analytics capabilities contributes to extend the use of BI technologies, making the combination between knowledge management and data highly beneficial for decision-makers and players involved in the delivery of knowledge services.
By transforming the structure of jobs, the computer has changed the skills requirements: the knowledge, management and data category of workers are closely associated with the use of computers while for good workers, the relationship is a substitutive one due to expert systems software.
Work to strengthen knowledge management, including data quality for evidence-based decisions and identification of gaps has also been identified as a core quality area in the context of HSS [ 3].
These include: 1) workflow; 2) knowledge management; 3) data as a foundation for CDS; 4) user computer interaction; 5) measurement and metrics; 6) governance; 7) translation for collaboration; 8) the meaning of CDS; 9) roles of special, essential people; and 10) communication, training, and support.
They not only attempt to capture in a more formal way the meaning (semantics) of a particular domain based on community consensus (29) but are also a key element for database interoperability and querying, as well as knowledge management and data integration (30).
In sum, the value addition of integration of knowledge management technologies and data analytics techniques resides in providing alternative scenarios and therefore a more complete view of decision-making problematic, contributing to measure "how well it promotes and enhances knowledge, and how well it improves the mental model(s) and understanding of the decision maker(s)" [17].
It requires first-, second-, and third-party data knowledge, PII data management, data integration, experience across a number of systems, and the ability to gain insight from data and to execute on that insight across different media.
The ESG provides data and knowledge management system built on the data infrastructure that covers over 500 TB of data collections for climate model comparisons and supports a total download volume of over a petabyte to over 20,000 registered users [4].
Both ClBR and EbCA-DEA may be regarded as hybrid or mixed qualitative-quantitative techniques (knowledge management, knowledge engineering and data analysis) which combine classical data analysis techniques and AI methods that permit prior knowledge processing [ 30].
