ISSN

2231-3915 (Online)
2231-3907 (Print)


Author(s): John Sushil Packiaraj, Bhasker Garg, Prateek Bansal

Email(s): jspackiaraj@vit.ac.in , jsp@jsp.net.in , bhasker.garg2012@vit.ac.in , prateek.bansal2012@vit.ac.in

DOI: DOI: 10.5958/2231-3915.2015.00004.8   

Address: John Sushil Packiaraj, Bhasker Garg, Prateek Bansal
School of Mechanical and Building Sciences, Vellore Institute of Technology, Vellore, India
*Corresponding Author

Published In:   Volume - 5,      Issue - 2,     Year - 2015


ABSTRACT:
At the end of a project, wrong classification of data leads to loss of a significant portion of knowledge. All EPC contractors require similar data for taking decisions. With differing standards of documentation, much of data lands in the inappropriate place, effectively making them untraceable. Adding information about data by the use of meta tags is common. In the EPC segment, effective application and interpretation of data requires a meaningful classification. Eventually this will pave way for continuous improvement and enables standardization of work methodologies. This proposal is an innovative approach to tag data which focuses towards becoming a body of knowledge management. Factual Information of past experience connected with a type of work is limited. The players may not have incentive or opportunity to share knowledge gained from handling mammoth projects across project locations. A preliminary conclusion is the fact that risk management in construction projects will become more effective by structuring knowledge. Application of outlined principles will enable EPC Contractors to develop systems which can learn and relate to similar projects. Thereby, establishing effective bench marks for running jobs and proposals quoted for in the present and future. Such an approach opens scope which will enable big data handling tools to take cognizance of the knowledge embodied and return relevant benchmark data.


Cite this article:
John Sushil Packiaraj, Bhasker Garg, Prateek Bansal. Classifying and Collating Enterprise Knowledge and Data Management. Int. J. Tech. 5(2): July-Dec., 2015; Page 100-104 doi: DOI: 10.5958/2231-3915.2015.00004.8


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DOI: 10.5958/2231-3915 


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