Marlabs Outliers Analysis

Field Level

Special characters Numbers in character field Length of column Max/Min value in column Nulls Value based

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How credible is your raw data? What are the recent initiatives you

have taken to assess and improve the quality of data that is being fed

into your data warehouse? How much time it takes to reprocess your

data?

A product is only as good as the raw material it is derived from. The

same is true in case of data and information. Therefore it is of utmost

importance that good quality data enters the warehouse since it

directly impacts the quality of the information that comes of the

warehouse.

To achieve good quality data in the data warehouse, the data quality

problems have to be tackled at all phases. ETL and Data Staging is one

of the most crucial stages. It is a prime location for validating data

quality from source or auditing and tracking down data issues. It is

here that Outlier Analysis plays a very important role.

What’s new in Marlabs Outlier Solution?

Identifies outlier in data

load stage at file level Effectively saves response

time No need to wait for

analysis report Hierarchy drill reaches till

the raw data

File Level

File size Record count Length of record Count of column

Marlabs Outlier Analysis methodology identifies data outliers at data load stage. Marlabs outlier’s algorithm

identifies anomalies using Advanced Statistical techniques and works in data warehousing framework.

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