Dataleaf Technologies, Inc: HR System migration, Archiving, Analysis, Modeling, Interim HRIS
The process of 'data cleaning' — whether in support of a system migration, or as part of a data engineering program — can be a huge sink of client staff resources.
We have noticed that 'in-house' data repair or data cleanup initiatives have certain common shortcomings:
Dataleaf's data repair methods are sharply different. In a Dataleaf system migration project (or data quality project), (1) all values of all legacy data elements are tabulated up front; (2) data cleaning is integrated with fit analysis and data conversion if applicable.
Data repair is iterative, rule-driven, and cyclic. The normal user interface is often not used. Users NEVER need to make the same manual data change on multiple records. A very complete audit trail, is always generated. It is tagged with cleanup heuristics and user decisions (which, in the case of mass corrections, are usually entered by filling in blanks on an Excel spreadsheet). Dataleaf data repair operations are always reversible in every detail.
A large class of automated tools called 'Consistency Reporting Tools' -- developed through tens of system migrations -- are deployed by Dataleaf from the very beginning of the project...
When Dataleaf's data-cleaning mechanisms are used, much less staff effort is required and a much more controlled, more auditable result is achieved.
The goal is to "eliminate jokers" from the data.