Course Descriptions

ACR/Detail Core Program

This course introduces you to the features and functionality of ACR/Detail and allows you to gain ACR/Detail proficiency in a controlled and supportive environment. In this course, you can learn how to:

• Extract and reconcile data to ensure the integrity of your input sources.
• Use historical data as input for reconciliation. Reformat and translate values to standardize data for reconciliation.
• Create customized reports using your presentation preferences.
• Manage databases and files with built-in utilities.

Who Should Attend

If you need the foundational knowledge and skills to use ACR/Detail, this course is for you. You should also have experience working in an MVS environment, and be familiar with MVS dataset names, JCL, and TSO/ISPF.

Course Length

This course lasts 2.5 days.

Platforms

This course is available for PC or MVS.

ACR/Detail Mastery Program

This course expands your base of product knowledge to bring you to the level of "expert." It addresses the following advanced product features through real-life examples and hands-on labs:

• Extraction Variables.
• External Translation Tables.
• Multi-Level Reconciliation.
• Suspense Processing.

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Who Should Attend

You can attend this course if you have completed the ACR/Detail Core Program (formerly the Standard Features Workshop), and have experience coding jobs in ACR/Detail.

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Course Length

This course lasts 2.5 days.

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Platforms

This course is available for PC or MVS.

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Extraction Variables

Extraction variables are temporary storage areas available during the extraction process that improve the efficiency and effectiveness of your ACR/Detail job coding. Learn about the most common uses of extraction variables within file definitions:

• Code file definitions more efficiently.
• Select records with more precision and accuracy.
• Create unique keys when the keys are identical.
• Carry values over from one input source for use on another input source.

External Translation Tables

External translation tables are useful when the data you are translating is dynamic. They also eliminate the need for dual maintenance of the translation table. Learn how to convert data from one field value to another using data from sources outside of ACR/Detail:

• Translate values without having to hard code the translation values.
• Reduce maintenance when translation values change.
• Translate values from one format to another, such as text to numeric.
• Flag values that meet the defined criteria.

Multi-Level Reconciliation

Multi-level reconciliation provides the opportunity to minimize processing time and maximize coding efficiency. Learn how to create multiple jobs that reconcile the same extracted data at different reconciliation key lengths:

• Run multiple jobs using the same extracted data.
• Use various key lengths to reconcile your data.
• Create multiple reports without having to recode or rerun jobs.

Suspense Processing

You can use suspense processing to temporarily store charges, credits, or debits that are pending disposition. Learn how to use the history database to track ongoing, non-matching conditions, and the controls required for reconciliation when all of the data is not available at the same time:

• Use the history database to store values over time for reconciliation.
• Code standard rules to reconcile against values stored over time.
• Manage your history database.

ACR/Summary Core Program

This course introduces you to the features and functionality of ACR/Summary and allows you to gain ACR/Summary proficiency in a controlled and supportive environment. In this course, you can learn how to:

• Extract and balance data to ensure the integrity of your input sources.
• Use historical data as input for balancing.
• Include multiple runs of data in one job.
• Manage databases and files with built-in utilities.

Who Should Attend

If you need the foundational knowledge and skills to use ACR/Summary, this course is for you. You should also have experience working in an MVS environment, and be familiar with MVS dataset names, JCL, and TSO/ISPF.

Course Length

This course lasts 2.5 days.

Platforms

This course is available for PC or MVS.

ACR/Summary Mastery Program

This course expands your base of product knowledge to bring you to the level of "expert." It addresses the following advanced product features through real-life examples and hands-on labs:

• Conditional Rules.
• History Analysis.
• Modeling.

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Who Should Attend

You can attend this course if you have completed the ACR/Summary Core Program (formerly the Standard Features Workshop), and have experience coding jobs in ACR/Summary.

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Course Length

This course lasts 2.5 days.

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Platforms

This course is available for MVS.

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Conditional Rules

Conditional rules use IF/THEN logic to determine whether a job has met defined conditions. Based upon the result, certain actions are available. Conditional rules also allow greater flexibility in coding a job to meet the balancing requirement. Learn when and how to use conditional rules within ACR/Summary balancing jobs:

• Code rules to address complicated balancing requirements.
• Identify conditions within the job requirements to determine the necessary rules.
• Use True logic and Boolean logic to determine if a condition is true or false.

History Analysis

The History Analysis function allows analysis of the values stored on the history database to identify trends and/or unusual fluctuations in data. Learn how to create a History Analysis job that will analyze history items:

• Use the history database to analyze values for trends and unusual fluctuations.
• Evaluate the job requirements to identify the analysis approach.
• Design a history analysis report based on your needs.

Modeling

ACR/Summary jobs can reference a template, known as a model job. This functionality allows you to use file and/or job definitions. Model jobs reduce the need to re-enter definitions for additional job IDs with similar balancing requirements. Balancing jobs can also model file definitions and/or job definitions. Learn about the role of modeling definitions, and the steps needed to define and reference the model job:

• Create a base job to serve as a template for other jobs.
• Use the definitions from the base job in subsequent jobs to reduce errors.
• Minimize coding when job requirements are similar.