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Profile
Quickly obtain comprehensive statistics regarding data content.
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Base Decisions on Facts Instead of Assumptions
Applaud's profiling tool enables you to quickly obtain statistics regarding the data content. With Applaud, you are not dependent on out-of-date documentation and incorrect assumptions. Applaud's profiling arms you with extensive facts regarding the content and quality of your data.
For example, profiling identifies each distinct value of code fields and shows a frequency distribution for each. (In other words, a list of all code values and the number of occurrences of each unique value.) For numeric data it shows many types of statistics such as highest value, lowest value, average value, and total value, as well as counts of positive values, negative values, zero/null values, etc. For dates it shows information such as the earliest date, the latest date, the number of blank/null values, etc. |
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Profile As an Integrated Component in Any Solution
Profiling is accomplished automatically whenever data is extracted from source systems into Applaud's data repository. Profile reports may also be included in any component constructed in Applaud. Applaud provides the ability to profile any combination of data at any time desired. Data profiled may include multiple tables and/or only selected columns. Profile results may be broken down into any desired categories (for example business units, groups, types, etc.) to satisfy each project's individual needs.
Profile Data in Flat Files (Before Loading into RDBMS)
Applaud is unique in that it can profile data in flat file before it is loaded into an RDBMS table. Most other profiling tools require the data to be placed in RDBMS tables before profiling. When the source data is in mainframes and other legacy systems, it is very important to obtain the results of profiling before the data is placed in RDBMS so that data issues can be resolved up-front, before wasting time trying to get the data into RDBMS tables. Applaud makes it easy to profile data before it is loaded into RDBMS tables, regardless of the data source.
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“Understanding the data in the operational system is particularly critical for enterprises starting e-business initiatives, customer relationship management (CRM) strategies, data warehouse implementations, or enterprise resource planning (ERP) migrations, yet it is often ignored. Unless enterprises take the time to understand the data in their current and planned applications, they will end up with problems with their data quality.”
Gartner |
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Automatic Assessment of Data Exceptions
Applaud's profiling tools also automatically produce a detail analysis of data content problems such as non-numeric data in a numeric field, invalid date value in a date field, etc. For example, suppose that a mainframe file contains non-numeric data in a numeric field. Applaud automatically produces a report showing the key values of every affected record and the actual value of the non-numeric data found in the numeric field.
Historical Data Dashboard
Applaud's analysis tools may also be used to analyze data profiling statistics over time, providing the ability to analyze "data Dashboards" to quickly see quality metrics. For example, components can be constructed to compare the differences in data values at various times (e.g., test run to test run, month to month, etc.) and report the differences. Applaud's robust batch scheduler can be set to automatically run analytical processes at pre-determined times. Alternatively any other batch scheduler can initiate Applaud processes as desired.
Applaud's Analysis Tools Expand the Data Knowledge
The result of profiling is extensive data knowledge regarding the content of the data. Armed with the profiling results, the project moves into the analysis phase where the content issues are further investigated and the data structure is analyzed.
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Copyright © 2008 Premier International Enterprises, Inc. All rights reserved.
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