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In all situations, the outcome is undoubtedly dependent on the inputs provided. Hence the quality of data entering the warehouse directly impacts the quality of the information that comes out of it. To ensure the superior quality of data in the data warehouse, issues related to quality have to be tackled at all phases. ETL and Data Staging is one of the most crucial of all stages. It is a chief location for validating data quality from source or auditing and tracking relative data issues. Outlier Analysis plays a pivotal role in addressing these challenges.
Marlabs’ Outlier Analysis methodology identifies outlier in data load stage at file level by meritoriously saving the response time, with no further delay in analysis report. Marlabs Outlier Solution has an upper edge since hierarchy drill reaches till the raw data to provide the best upshots. Marlabs provides Field Level maintenance with special characters, numbers in character field, length of column, range value, nulls, and value based support, while the File Level assistance takes into account the uniform index of the file size, record count, length of record, and count of column. Marlabs Outliers algorithm isolates the glitches using advanced statistical techniques and mechanisms in data warehousing framework.
To know in detail about Marlabs Outliers Analysis and other Data Warehousing Solutions, visit http://www.marlabs.com/technologies/bi-analytics/offerings