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What We Measure with ProfilerSuitcase™
 We calculate and compile
metrics from several dimensions to create a detailed map of your data landscape.
These measurements identify where data anomalies could block your ability
to synchronize multiple data sources (learn how data profiling can help solve
your data management problems).
The measurements also indicate where you have data quality issues that
can amplify as you merge or transform source data.
 | Source
and target database specifications
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Provides a summary of
profiling scope.
| Known and discovered business rules |
Allows data content to
validate adherence to business rules, as well as uncovers undocumented rules.
| Attribute frequency, syntax and domain |
Attribute level
analysis provides information about business rules, data structures, and
numbers of occurrences. This
allows you to manage duplication, inconsistency, and “noisy records.”
“Noisy records” no longer provide any use for the business.
They may be leftovers from development and testing phases, or may be
invalid records that were not deleted.
| Lineage |
Tells you exactly where
an anomaly occurs so that you can fix it.
| Dependencies |
By identifying synonyms
and homonyms, shows how data relates to other data.
| Redundancy and replication |
Identifies
exact copies of data in controlled environments (replication), as well as the
data copies that have assumed a life of their own (redundancy).
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