Course Introduction
Organizations often accumulate duplicate and inconsistent records as information is collected across applications, databases, departments, research systems, customer channels, and external sources. Data deduplication and entity resolution provide essential techniques for determining which records represent the same real-world entity and creating more accurate, consistent, and trustworthy data assets.
The Data Deduplication and Entity Resolution Training Course provides a practical framework for identifying duplicate records, matching related entities, resolving conflicting attributes, and establishing trusted representations across heterogeneous datasets. Participants learn how to combine data profiling, standardization, deterministic matching, probabilistic methods, and validation to improve data quality.
The course explores entity resolution challenges involving customers, patients, suppliers, employees, products, organizations, locations, research participants, assets, and other important entities. Participants examine how differences in names, addresses, identifiers, contact details, formats, languages, missing values, and source-system conventions can make seemingly identical records difficult to match accurately.
Particular attention is given to the design of matching rules, similarity measures, blocking strategies, candidate generation, scoring models, thresholds, survivorship rules, and exception handling. Participants learn how to balance automation with human review and establish transparent processes that minimize both false matches and missed matches.
The course also examines modern approaches using machine learning, natural language processing, graph techniques, embeddings, and AI-assisted matching. These approaches are considered alongside traditional deterministic and probabilistic methods, with emphasis on validation, explainability, privacy, governance, and responsible use of automated entity resolution techniques.
By completing this course, participants will be equipped to design, implement, evaluate, and maintain deduplication and entity resolution processes across complex data environments. The course emphasizes measurable data quality improvements, traceability, scalable matching workflows, and practical strategies for creating reliable entity-level views from fragmented information sources.
Duration
5 days
Who Should Attend
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Data quality professionals responsible for identifying and resolving duplicate records across enterprise data environments.
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Data engineers developing automated deduplication, matching, transformation, and entity resolution pipelines.
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Data analysts working with datasets containing duplicate, inconsistent, incomplete, or conflicting records.
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Data architects designing master data, customer data, reference data, and cross-system identity resolution solutions.
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Master data management professionals responsible for creating trusted golden records and maintaining consistent entity information.
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Database administrators managing duplicate records and inconsistent entity representations across multiple database systems.
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Data scientists developing statistical, machine learning, or AI-assisted approaches to entity matching and record linkage.
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Analytics engineers preparing standardized and deduplicated datasets for reporting, business intelligence, and advanced analytics.
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Research data professionals linking participants, observations, studies, samples, organizations, or other entities across research datasets.
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CRM and customer data professionals resolving duplicate customer, account, contact, and organization records across operational platforms.
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Data governance professionals establishing policies for identity resolution, survivorship, stewardship, quality, privacy, and data ownership.
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Technical leads and project managers overseeing data consolidation, migration, integration, master data, and data quality improvement initiatives.
Course Objectives
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Develop a comprehensive understanding of data deduplication and entity resolution concepts and their role in improving trust, consistency, and usability across complex data environments.
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Identify duplicate and related records by applying systematic profiling, normalization, standardization, candidate generation, matching, scoring, and validation techniques.
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Design deterministic matching rules that use identifiers, business keys, exact attributes, and domain-specific conditions to establish reliable entity relationships.
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Apply probabilistic and similarity-based matching techniques that handle incomplete information, inconsistent formats, spelling variations, missing identifiers, and uncertain relationships.
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Implement effective blocking and candidate-generation strategies that reduce unnecessary comparisons while maintaining high coverage for large-scale entity resolution workloads.
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Develop scoring models, thresholds, confidence levels, and classification rules that distinguish likely matches, possible matches, and non-matching records.
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Establish survivorship and golden-record strategies for resolving conflicting attributes while preserving source information, provenance, history, and business context.
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Design human-in-the-loop review processes that allow uncertain matches and exceptional cases to be evaluated consistently without undermining automated processing efficiency.
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Apply machine learning, natural language processing, embeddings, graph techniques, and AI-assisted approaches while addressing validation, explainability, privacy, governance, and bias risks.
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Develop scalable and maintainable entity resolution workflows with monitoring, quality metrics, lineage, audit trails, continuous improvement, and appropriate controls for sensitive data.
Comprehensive Course Outline
Module 1: Foundations of Deduplication and Entity Resolution
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Understanding the difference between data deduplication, record linkage, entity matching, entity resolution, and master data management.
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Examining why duplicate and inconsistent records emerge across operational systems, research datasets, applications, migrations, and external data sources.
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Identifying common entity resolution challenges involving names, addresses, identifiers, contact details, organizations, products, and other entity attributes.
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Establishing business objectives, quality requirements, matching scope, entity definitions, and measurable success criteria for resolution initiatives.
Module 2: Data Profiling, Normalization, and Standardization
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Profiling datasets to identify duplicate patterns, missing values, inconsistent formats, invalid attributes, unusual values, and potential matching keys.
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Applying normalization techniques to names, addresses, phone numbers, email addresses, identifiers, dates, codes, and other matching attributes.
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Developing standardization rules that reduce superficial differences while preserving meaningful distinctions between real-world entities.
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Evaluating data quality characteristics that influence matching accuracy and determining which fields provide the strongest evidence for entity identification.
Module 3: Deterministic Matching and Rule-Based Deduplication
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Designing exact and rule-based matching strategies using unique identifiers, composite keys, business rules, and trusted source attributes.
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Developing multi-field matching rules that combine several attributes to improve confidence when no single identifier is consistently available.
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Establishing precedence rules for handling conflicting identifiers, missing fields, inconsistent source values, and hierarchical entity relationships.
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Testing deterministic matching logic against representative datasets to identify false matches, missed matches, edge cases, and unexpected outcomes.
Module 4: Probabilistic Matching and Similarity Techniques
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Understanding probabilistic record linkage concepts and how evidence from multiple attributes can be combined to estimate matching likelihood.
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Applying string similarity, edit distance, token comparison, phonetic methods, numerical similarity, geographic comparison, and other matching techniques.
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Designing field-level similarity functions that account for differences in spelling, formatting, abbreviations, ordering, missing values, and data quality.
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Combining similarity evidence into interpretable matching scores and confidence categories suitable for automated and assisted resolution processes.
Module 5: Candidate Generation, Blocking, and Matching Performance
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Understanding why all-to-all record comparison becomes computationally expensive as dataset sizes increase and why candidate reduction is necessary.
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Designing blocking strategies using geographic, categorical, temporal, phonetic, identifier, or other attributes to reduce unnecessary comparisons.
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Evaluating blocking effectiveness by balancing computational efficiency, candidate coverage, matching recall, and the risk of excluding legitimate matches.
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Optimizing large-scale matching workflows through indexing, partitioning, parallel processing, incremental resolution, and efficient data structures.
Module 6: Golden Records, Survivorship, and Conflict Resolution
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Designing golden-record strategies that represent trusted entity information while maintaining links to contributing source records.
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Establishing survivorship rules for resolving conflicts between source systems based on data quality, authority, recency, completeness, or business context.
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Preserving source values, transformation history, confidence information, and lineage so golden records remain auditable and explainable.
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Managing entity hierarchies, relationships, merges, splits, historical identities, and changes in entity attributes over time.
Module 7: Machine Learning and AI for Entity Resolution
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Exploring supervised and unsupervised machine learning approaches for identifying patterns in matching evidence and improving entity resolution performance.
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Applying natural language processing and embeddings to handle complex textual attributes, semantic similarity, multilingual information, and unstructured entity descriptions.
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Examining graph-based approaches for resolving entities through relationships, networks, shared attributes, and contextual evidence across interconnected datasets.
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Evaluating AI-assisted matching techniques while maintaining human oversight, explainability, validation, privacy, governance, and responsible decision processes.
Module 8: Validation, Quality Measurement, and Human Review
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Designing evaluation datasets and reference labels for measuring precision, recall, false-match rates, missed matches, and overall resolution effectiveness.
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Establishing confidence thresholds that determine which records can be automatically resolved and which require additional review or investigation.
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Designing human-in-the-loop workflows that prioritize uncertain matches and capture reviewer decisions for future rule or model improvement.
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Monitoring matching quality over time and identifying data drift, source changes, new duplicate patterns, and declining resolution performance.
Module 9: Governance, Privacy, Security, and Operational Management
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Applying governance practices for entity definitions, matching rules, data ownership, stewardship, auditability, and lifecycle management.
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Protecting sensitive identity information through access controls, encryption, masking, pseudonymization, secure processing, and appropriate data retention practices.
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Maintaining audit trails that document matching decisions, confidence scores, rule versions, model changes, reviewer actions, and entity history.
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Establishing operational monitoring for pipeline health, processing performance, matching volumes, exception rates, quality indicators, and resolution outcomes.
Module 10: Advanced Entity Resolution Architecture and Emerging Practices
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Designing scalable entity resolution platforms that integrate profiling, standardization, candidate generation, matching, review, golden records, and downstream data delivery.
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Exploring real-time entity resolution approaches for applications requiring immediate identity matching across transactions, interactions, or event streams.
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Examining emerging applications of generative AI, vector similarity, knowledge graphs, and intelligent data quality automation for complex matching challenges.
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Developing a practical roadmap for implementing continuous deduplication and entity resolution capabilities across enterprise, research, analytical, and cloud data environments.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
