Asset Integrity Management requires data from multiple sources some of which are vintage paper records and antiquated or proprietary systems. RadixData has a process for identifying those sources, analyzing the content and developing a process for mining data from those sources. The process is completed when all data is gathered, and a complete data set exists to import into an Asset Integrity Management System. Our clients leverage our expertise in filling addressing the problems with gathering vintage records and data to fill the gaps of missing data. The process defining of records and data in-scope the gathering of the data is iterative and continues until all gaps in data are filled.
Data Investigation and Collection
Radix Data will organize interviews with key stakeholders in the operator’s organization to fully understand record descriptions, search criteria, physical and virtual locations for data and records of interest. The interview yields information held by long time employees and client subject matter experts, providing a history of the types, locations, and nomenclature associated with legacy records. This input feeds the collection process, where collection teams are fielded to defensibly query and gather both physical and electronic records. Collected records and data are processed in our facilities to populate the project database.
Content Analytics
Radix Data performs “Records Viability Analytics” of current metadata to assess the availability of critical records types. Although generally sparse and incomplete, the metadata hits will point to the “low hanging fruit” records which appear to be readily available. This metadata typically includes digital and physical file inventory descriptions and searchable index fields in content management systems. Performing analytics on the metadata creates a profile of the available records, facilitating decisions by the project team to intelligent processing.
RadixData What’s in the Box (WIB) is complimentary to content management systems. WIB can create a detailed inventory expanding the current content management system inventory to broaden the scope of records for processing. This can be done at a low cost with high returns for AIM projects.
Data Mining Objectives
The data mining objectives associated with preparing a highly curated engineering data set for an AIM program typically consist of the following components:
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