| |
Only a Minute or Two
Since each of Applaud's report generators was designed for a specific purpose, defining a report usually only takes a minute or two. It is so easy to define reports in Applaud; it is not uncommon to define hundreds in just the first week of the project.
Embed Reports in Any Component
All four of Applaud's report generators may be used to build reports within other Applaud components. In other words, all four of the report generators may be used to build reports in any feature constructed in Applaud. This makes it easy to perform any type of analysis as an integral component of any solution
Advanced Sampling Techniques
All four of Applaud's report generators can be coupled to Applaud's advanced sampling tools to select samples of large volume data for testing and analysis. Applaud supports random sampling, attribute sampling, interval sampling, and monetary unit sampling. All of Applaud's sampling capabilities can be coupled to any of Applaud's analytical tools to enable sampling within any component process.
Identify Redundant Data
Applaud's report generators speed the process of matching duplicate/redundant data from disparate systems. Applaud provides the ability to match any type of data based on any number of flexible criteria. Applaud's rapid application development approach allows the matching tools to be applied flexibly in any combination to solve any cleansing requirement. Multiple criteria may be established. Weighting may be applied to results to obtain "most likely" match combinations. Negative criteria may be applied to reduce the possibility of "false positives".
Match Duplicate Name/Address Data
Applaud also includes comprehensive name/address cleansing tools. Among many other capabilities, Applaud's name/address matching capabilities provide the ability to equate names and words (Bob = Robert = Rob = Robt, etc.); ignore "noise" words ( Company, Co, Corp, Corporation, Inc, Ltd, Limited, LLC, etc.), and phonetically encode names/words to find duplicates that may have misspellings (Parsippany = Parsipany = Parsipanie, etc.) Applaud uses these tools to automate the process of identifying the "hard to find" duplicates.
Match any Data - Complete Flexibility
Applaud's duplicate matching features are not limited to only name and address data. Applaud's tools for word replacement, "noise" word elimination, phonetic encoding and duplicate identification are equally valuable in identifying duplicate records in any type of data including products, inventory, services, contracts, assets, etc. For example, the same tools used to match Bob = Robert = Rob = Robt may also be used to equate units of measure, product abbreviations, asset names, services names, etc. The same phonetic encoding tools can be used to match misspellings in any type of data, not just name and address data. Literally the full suite of matching tools may be used for any type of data. Complete flexibility is provided to support the most challenging data cleansing requirement.
Analyze Results of Cleansing and Transformation
Applaud's four styles of report generators and advanced analytical tools are designed to be able to analyze the results of cleansing and transformations as well as the source data. This makes it easy to ensure that data quality issues are fully corrected and resolved and that all transformation components operate as expected. Applaud analysis components may be run (and re-run) at every test date to confirm that all processes are successful, and identify any new issues.
Parallel Test Analysis
Applaud's analysis tools can also assist in analyzing the results of parallel tests (i.e., comparing legacy system results to new system results). It is easy to extract data from both the legacy and target systems into Applaud's Data Repository after parallel processes have been run. The data repository makes it easy to normalize/reconcile the two data formats and compare the results. Differences are quickly identified on Applaud's analytical reports, which can include any data from both systems. The result is a very fast way of ensuring the new system operates exactly as expected.
Post-Implementation Audit
Applaud's analytical tools can also be used to perform post-implementation audit and analysis. Applaud can quickly (and if desired continually) test new system data to ensure that processes are operating properly. All of Applaud's analytical tools are independent of the data source. All tools can analyze data in source system tables, Applaud's data repository tables, and target system tables. This makes it easy to ensure that the new system is operating as planned.
Document Data Corrections
Applaud can also make corrections to live production data and can provide simultaneous analysis of the changes made. For example, suppose that it is learned that a specific production process has been operating incorrectly, resulting in several RDBMS tables containing incorrect data. Applaud can easily make the correction to the affected data. But more important, Applaud's analytical tools can easily provide a complete audit trail, showing the "before" and "after" values for every change as an integrated part of the correction. With Applaud, all changes are fully documented.
Data Query Screens
Applaud also includes the ability to build robust data query screens for source tables, target tables, and tables in the Applaud data repository. Query screens offer the ability to perform ad hoc queries, quickly find selected rows, reorder the data, “freeze” columns of data while scrolling other data, summarize selected numeric data, as well as many other important features.
Automatic Link to Related Rows in Related Tables
Applaud also includes an analytical feature not available in any other software. Applaud automatically links related rows in related tables. This tool is extremely valuable in discovering the reason for data quality problems.
For example, suppose that it is determined that a row in a relational table contains a data problem. When you click on that row using Applaud's data query screens, Applaud automatically provides an immediate link to every related row in every related table. This process automatically links all related tables - all source system tables, all target system tables, all staging tables, all crosswalk/look-up tables, and all tables in the Applaud data repository. Analysis of the interrelated data is as simple as clicking on a table and viewing the related data in the table. It is then easy to determine if the problem is due to invalid source system data, incorrect values in a crosswalk/look-up table, erroneous assumptions regarding the source data, incorrect transformation logic, etc. This dramatically speeds the time required to perform data analysis.
Parse Unstructured Data
Multiple data values are often contained in larger unstructured data elements and must be separated (parsed) and analyzed. Applaud includes a family of tools that makes it easy to parse data components. For example, Applaud makes it easy to parse address data into separate fields: "123 South Main Street Suite 456" is parsed into separate components for street number (123), directional (South), street name (Main), street type (Street), unit type (Suite), and unit identifier (456). In another example, a system may have multiple fields for company name, address and person name, but there may not be a consistent way of using the fields. Again, Applaud makes it easy to determine the content of each separate field so it is applied consistently. Applaud's family of parsing tools works with names, street addresses, city/state/zip/county names, as well as other data.
Accurate Facts Guide Cleansing and Transformation
Applaud's analysis tools provide extensive information regarding the content and structure of the data. This information is used to define the requirements for the cleansing and transformation activities.
|
|
|
|