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 Oracle Enterprise Data Quality 12c: Match and Parse training teaches you how to configure match processors to find and optionally merge exact and similar (fuzzy) matching records. You'll learn how to create sophisticated match rules, configure merge options and review match results.And Profile, Audit and Operate topics cover on how to profile the quality of a set of data. Expert Oracle University instructors will explore Oracle Enterprise Data Quality's user interface and how to use its profiling tools to delve into data and quickly identify inconsistencies, missing data and other problems.


By the end of this training you will learn to:

Configure match processors to identify and optionally merge matching records.

Use parse processors to extract key data from free text fields.

Use the address verification processor and interpret its results.

Standardize data using a number of Oracle Enterprise Data Quality processors.

Use and interpret results from the Oracle Enterprise Data Quality Address Verification server.

Tailor a customer data extension pack parse processor to extract, standardize and re-structure data from a free text field.

Configure the Parse processor from scratch.

Reach a semantic understanding of free text by using the Phrase Profiler.

Understand EDQ's Customer Data Services Pack and case management functionality.

Identify data problems and check data validity.

Import and export data.

Check data quality using audit processors.

Create data quality processes and jobs to run in batch and real-time.

Reuse configuration in different processes.

Transform data for auditing.

Set up audit checks on the data and export the data.

Deploy strategies to re-use your work by publishing processors, creating and importing packages and making use of data interfaces and run profiles.

Understand Oracle Enterprise Data Quality's Server Console user interface and its Customer Data Extension Pack.

Course Contents

Day 1

Match Overview

Discussing Business Examples of Matching

About the Match Processors

What Constitues a Match?

Oracle Enterprise Data Quality Matching Fundamentals

Discussing Inputs to Match

About Mapping Identifiers

Discussing Fundamentals of Clustering

Setting up Simple Match Rules

Browsing Results

Match Rule Lists and Fuzzy (inexact) Matching

Using Multiple Comparisons

Identifying Fuzzy (inexact) Matches

Tuning Match Rules


Clustering for Performance

Clustering Strategies

Tuning Clusters


Discussing Defaults for Merging

Merging Options

Match Review

Discussing Review Groups

About the Match Review Interface

Day 2

Customer Data Extension Pack Match Processors

About the Match Entities processor

About the Match Individuals processors

About the Match Households processor

Match Case Studies

Enhancing Records

Describing Deduplication

Introduction to Case Management

Overview of Case Management

Address Verification

Overview of Address Verification

Using the Address Verification Processor

About Accuracy flags: Interpreting Address Verification Results

Standardizing Data

Overview of Standarization

Overview of Simple Standardization

About the Character Replace and Replace Processors

About the Pattern Transform and RegEx Replace Processors

About the Merge Processor

Overview of Transliteration Capabilities

Introduction to the Customer Data Servcies Pack

Customer Data Services Pack Overview

Parse Overview

About Business Uses of Parsing

Parsing in Oracle Enterprise Data Quality

Day 3

The Phrase Profiler

Using the Phrase Profiler

Identifying Common Words and Phrases

Identifying Misplaced Data

Tailoring a Customer Data Extenstion Pack Parse Profiler- Part I

Understanding Tokenization

Using the Classify sub processor

Tailoring a Customer Data Extenstion Pack Parse Profiler- Part II

Using the Reclassify sub processor

Classification vs. Reclassification

Tailoring a Customer Data Extenstion Pack Parse Profiler - Part III

Using the Select sub processor

Using the Resolve sub processor

Creating Exact and Fuzzy Resolution Rules

Optional- Case Studies

Discussing Parse Case Study

Discussing Overall Enterprise Data Quality Case Study

Enterprise Data Quality Overview

Overview of Enterprise Data Quality and its Features

Overview of High-level architecture

Day 4

Director User Interface and its Key Objects

About Process Canvas, Tool Palette, Results Browser and Project Browser

Saving Results to a Results Book

Setting up Projects, Data Stores, Snapshots and Processes


Using the Quickstats Profiler for a Fast Overview of Data

Identifying Trends with the Frequency Profiler

Examining Outliers with the Max / Min Profiler

Profiling Patterns

Assessing Record Completeness


Using Audit Processors to Check Your Data

Understanding Flags

Using Audit Processors to Branch Processes


Using Lookup and Return to Enrich Your Data

Using the Group and Merge Processor

Transforming Data to enable better auditing

Writing and Exporting Data

Using the Writer

Setting up an Export

Automated Processing: Jobs

Configuring and scheduling jobs

Day 5

Re-using Your Work: Publishing, Packaging and Copying

Publishing processors

Creating and importing packages

Introduction to the Customer Data Extension Pack

Understanding the use of Customer Data Extension Pack

Installing the Customer Data Extension Pack

Examining a Customer Data Extension Pack Processor

Real-Time Processing Via Web Services

Configuring a web service within Enterprise Data Quality

Creating and testing a real-time process

Data Interfaces

Introducing Data Interfaces

Creating a data interface

Using a data interface in a process

The Server Console

Overview of the Server Console user interface

Running, Scheduling and Monitoring jobs from the Server Console user interface

Run Profiles

Overview of Run Profiles

Creating a Run Profile

Running a job with a Run Profile


Sampling options

Introduction to Case Management

Overview of Case Management Functionality



Training Hours

5-6 Hrs starting from 12:00 NOON GMT | 08:00 AM EST | 5:00 AM PST | 7:00 AM CST | 6:00 AM MST | 5:30 PM IST  | 01:00 PM GMT+1


Functional Implementer

Reports Developer

Business Intelligence Developer

End Users

System Analysts

Data Warehouse Administrator

Sales Consultants

Technical Consultant

Business Analysts

Project Manager

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