Informatica Master Data Management Concepts (MDM)

Understand the three-layer mdm architecture—social systems, medium implementation, and consumption—and how etl, data cleansing, standardization, duplicate detection, and consolidation produce golden copies for governance and analytics.

Explore the MDM system architecture, including the hub store, MGM hub, and services integration framework, and see how the master database and operational reference store manage configuration, data, and rules.

Explore the Informatica MDM batch job lifecycle from landing to golden copy, including staging, data quality cleansing, delta detection, tokenization, match, and the consultation process to finalize the golden record.

Trace data flow in the MDM hub from landing to staging to the base objective, detailing cleansing, tokenization, matching, and golden copy consolidation, with rejects, history, and data lineage.

Navigate the MDM hub infrastructure tables, including landing, staging, and base object tables, and understand their link to subject tables, system, history, and tokenization processes.

Understand how Informatica MDM uses repository tables in the MDM hub to store metadata, configuration, and history, including system, reports, archival, and match process tables.

Explore the cmx system tables that maintain metadata for the mdm hub within the Cimic system schema, including security access, external authentication, and message queue configurations.

Learn how the landing process loads solar system data into landing tables via external batch and real-time processing, using ETL, mapping to staging, and hub data models.

Configure the landing table with the abbreviated name c_l, aligned to the party model, and define columns such as first name, middle name, last name, full name, and gender.

Discover the landing data processing workflow by simulating the EDL process with a CSV file, loading into the Imbrium landing table via SQL Developer, and validating the import.

Understand how the stage process transfers data from landing to staging, with or without the staging platform, managing audit trails, rejects, and history in the MGM hub and Informatica platform.

Configure a base object table by defining subject tables, consolidating data to a golden copy, specifying business and system columns, enabling history and state management, and creating related lookup tables.

Configure staging tables in Informatica MDM by creating source-specific staging tables that map to a single base object, including system columns and reject tables.

Understand how the Pkey_Src_Object maps landing to staging using a unique source key or a constructed key from multiple fields, and how nulls and duplicates are managed.

This lecture explains data flow from landing tables to the MGM staging tables, covers mapping and base object staging tables, PRL and supporting tables, plus job execution and validation.

apply cleansing and standardization rules in the mapping from landing to staging tables, using the clean function and uppercase transformation to normalize strings and verify results.

Enable a filter condition in the mapping to push only selected records from the landing table to the staging table, using criteria such as source system and gender.

Remove duplicate records from source data through deduplication in the ETL and MDM layers, enabling distinct in landing-to-staging mappings to push unique records only.

Explore delta detection in informatica mdm to identify new and updated records by comparing landing and staging data, using a previous learning table and a delta column configuration.

Configure staging table retention to preserve history in the roundtable as an audit trail, with a defined number of loads and retention period from landing to roundtable.

Explore how master data management rejects records between landing and staging, applying validations for future or null dates, invalid gender codes, and business rules, with demo in MDM hub.

Explore how landing, staging, and alert processes move data into the MDA landing and base object tables, build history, retention, delta prediction, and apply truth during consolidation with validation rules.

Explore how data flows from the landing table to the subject base object in Informatica MDM, with staging and load jobs populating subject, history, control, dirty, and external match tables.

Trace data lineage in MDM by mapping sources to the golden copy in the subject table. Learn how foreign keys and bridge tables connect party, party phone, and phone data.

Learn to run MDM jobs using batch groups and repos tables, configure staging and base objects, and verify results with the job control table, applied log table, and job metrics.

Explore how tokenization in the MDM hub generates fixed eight-character sticky tokens stored in the master table, with counts driven by match configuration.

Explore how tokenization generates tokens and populates the script table using the subject, strip, and dirty tables, and how archetype data types guide fuzzy and exact matches.

Learn how Informatica MDM tokenization relies on configuration thresholds and match keys, generating tokens on load or by a batch job, with table inputs and outputs guiding the process.

Explore the match and merge settings in MDM, configure parent-child relationships, set match columns and key types, and tune exact or fuzzy matching with search levels.

Understand the hub_state_ind column in MDM, which marks record states as active (1), deleted (-1), pending (0), or deleted with history (-9), guiding survivorship and data workflow across tables.

Explain the consolidation indicator, a system column that tracks record cancellation status as records move through load, match, merge, and unmatched processes, with values 1, 2, 3, 4, and 9.

Explore the Informatica MDM match process with a practical example. Learn base object configuration, manual consolidation limits, batch size, and fuzzy versus exact strategies.

Explore the theory of the trust, which guides survivorship and the golden copy during consolidation by measuring trust for fields like first name, last name, age, gender, country, and city.

Understand how trust and validation tables relate to the subject table, including history storage and how VCT and VCs tables prefix with the subject or base object name.

Configure trust for the four name columns in the MDM base object, set maximum and minimum trust per source, and apply a linear decay model with defined durations.

Explore validation rules in the MDM hub, distinguishing them from clean functions, and learn how validation rules influence survivorship by downgrading trust values to cleanse data.

Configure validation rules in the MDM hub by enabling validate on a base object column. Create existence, domain, referential integrity, pattern, and custom rules that adjust trust and trigger consolidation.