Data Cleansing and Deduplication for ERP Training Course

Data Cleansing and Deduplication for ERP Training Course

Introduction

In the age of Digital Transformation and Data-Driven Decision Making, a robust Enterprise Resource Planning (ERP) system is the central nervous system of any modern business. However, the efficacy of this multi-million-dollar investment is critically dependent on the quality of its input data. Dirty Data defined by inconsistencies, incompleteness, and most crucially, Duplicate Records is a silent killer of business efficiency, leading to flawed reporting, wasted resources, poor customer experience, and major compliance risks. Data Cleansing and Deduplication for ERP Training Course is engineered to empower IT professionals, Data Stewards, and functional users with the Advanced Data Quality skills and Best Practices essential for successful ERP implementation, migration, and sustained Data Governance. By mastering proven methodologies in Data Cleansing and Deduplication, participants will not only mitigate the substantial risks associated with poor data but also unlock the true potential of their ERP system for optimized operations and unparalleled Business Intelligence.

This intensive, hands-on program focuses on tactical and strategic approaches to achieve a Single Source of Truth within the ERP environment, directly addressing the "Garbage In, Garbage Out (GIGO)" principle. The curriculum is built around Real-World Case Studies and the practical application of Data Profiling, Standardization, and Fuzzy Matching tools to proactively identify and rectify data anomalies. Upon completion, participants will be the internal champions of Data Integrity, equipped to establish continuous Data Quality Monitoring frameworks and implement scalable, automated solutions that ensure a high-quality, Clean Data foundation a prerequisite for achieving operational excellence, seamless cross-module integration, and compliance with modern Data Privacy regulations like GDPR and CCPA.

Course Duration

5 days

Course Objectives

1. Implement a holistic framework for achieving and maintaining high Data Integrity across all core ERP modules.
2. Master tools and techniques for Data Profiling to accurately assess current data quality dimensions
3. Strategize and execute effective Deduplication processes using Fuzzy Matching algorithms and unique identifier key generation.
4. Define and enforce robust Data Standardization rules for seamless ERP data migration.
5. Successfully manage the Legacy Data Migration process, minimizing data errors 
6. Utilize AI/ML-Powered tools to implement Data Cleansing Automation for continuous, real-time data maintenance.
7. Understand the role of cleansing and deduplication within a broader Master Data Management strategy.
8. Design and establish a sustainable Data Governance policy with clear roles and responsibilities for data quality ownership.
9. Ensure all data cleansing activities adhere to stringent GDPR and CCPA regulations and Data Privacy requirements.
10. Identify and fix root-cause data entry errors to enable Optimized Business Processes post-ERP implementation.
11. Enhance the reliability of Financial Reporting and compliance through rigorous cleansing of financial master data.
12. Minimize database bloat caused by redundant records to improve overall ERP System Performance and query speed.
13. Define, measure, and track critical Data Quality KPIs for continuous improvement.

Target Audience

1. ERP Implementation Team Members (Focus on pre-migration preparation)
2. Data Stewards and Data Governance Analysts
3. IT/Data Migration Specialists
4. Business Analysts who validate ERP data requirements
5. Functional Consultants (Finance, Supply Chain, CRM)
6. Master Data Management (MDM) Professionals
7. Data Architects and Database Administrators (DBAs)
8. Project Managers overseeing Digital Transformation or ERP upgrades

Course Modules

1. The Critical Link: Data Quality and ERP Success

• The "Garbage In, Garbage Out (GIGO)" principle in ERP Systems.
• Impact of Dirty Data on cross-functional processes
• Defining the five dimensions of Data Quality
• Case Study: A global manufacturer's failed S/4HANA implementation due to $5M in overstock and inaccurate forecasting caused by duplicate material master records.
• The business case for continuous Data Governance and quality monitoring.

2. Foundational Data Profiling and Auditing

• Introduction to Data Profiling Tools and techniques
• Identifying data anomalies.
• Establishing a Data Quality Baseline and defining acceptable error thresholds.
• Case Study: A retail company using data profiling to discover 40% of customer addresses were inconsistently formatted, delaying their new eCommerce integration with the ERP.
• Documenting the Data Quality Audit report for stakeholder review.

3. Core Data Cleansing Techniques

• Techniques for data standardization.
• Handling missing values
• Data validation using business rules, look-up tables, and external authoritative sources.
• Case Study: A healthcare provider standardizing 20+ variations of product names into a single, compliant format to successfully load into their new Infor CloudSuite module.
• Implementing data cleansing scripts and reusable transformation logic.

4. Advanced Data Deduplication Strategies

• Understanding the difference between exact matching and Fuzzy Matching algorithms
• Developing Survivorship Rules for merging duplicate records
• Techniques for linking and resolving duplicates across multiple ERP modules
• Case Study: A financial services firm merging over 100,000 duplicate vendor records from a legacy system, saving $500K annually in procurement process inefficiencies.
• Best practices for creating a Unique Identifier strategy.

5. ERP Data Migration and Go-Live Readiness

• Data cleansing as a critical phase of the ERP Implementation Roadmap
• Developing and testing the Extraction, Transformation, Loading mapping with clean data.
• Conducting User Acceptance Testing and mock data loads with high-quality data.
• Case Study: A manufacturing firm reducing its final data migration failure rate from 15% to $<1\%$ by dedicating a 3-month cycle to pre-migration cleansing and deduplication.
• Cutover Strategy and post-go-live data validation checks.

6. Tools and Technologies for Automation

• Overview of enterprise-level Data Quality Tools
• Leveraging Machine Learning for predictive error detection and automated cleansing.
• Using native ERP tools and utilities for in-system deduplication
• Case Study: A telecommunications company implementing a real-time data quality firewall using automated tools to prevent the entry of duplicate customer records at the source.
• Setting up continuous Data Quality Monitoring dashboards and alerts.

7. Sustaining Data Quality: Governance and Ownership

• Defining the roles and responsibilities of the Data Steward team and Data Owners.
• Establishing a formal Data Quality Policy and remediation workflow.
• Designing and implementing effective Data Entry Standards and user training programs.
• Case Study: A major utility company creating a cross-functional Data Governance Council which reduced the recurrence of duplicate asset records by $80\%$ within one year.
• Measuring the Return on Investment of a clean data environment.

8. Legal, Ethical, and Security Considerations

• Adherence to global Data Privacy regulations in cleansing processes.
• Handling sensitive and Personally Identifiable Information during migration.
• Data security and access control in the Data Quality Process.
• Case Study: A European bank reviewing its deduplication process to ensure merged records comply with "Right to Be Forgotten" clauses under GDPR.
• Ethical implications of data transformation and ensuring data lineage.

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.