Question: What is the tool in Informatica Master Data Management which does not require Lock?

Merge manager (Write lock Not required)

Data Manager (Write lock Not required)

Hierarchy Manager (Write lock Not required)

Audit Manager (Compose Lock Not Required)

Question: What are the various loading phases in MDM?

Following are the various stages in which data is stored into hub stores in a sequential process in informatica Master Data Management.

1. Landing process              2. Stage process        3. Load  process (Staging to Base object)  4. Match   process         5. Merge process

Question: What are the tables that are linked with staging data in Informatica Master Data Management?

1. _PRL Table         2. _Raw Table          3. _REJ Table          4. _OPL Table

Question: What is parallelism for data loading properties in MDM?

This indicates the degree of parallelism established in the base object table and its related tables. It doesn't happen in every batch process, but once used it can have a positive impact on performance. However, usage is limited by the number of CPUs in the database server automaton and the amount of available memory. 1 is the default.

What are the various components of the Informatica hub console?

The Informatica Hub Console components are:

Design Console: This element is useful for configuration solutions during the deployment phase and can be modified according to the needs of the run configuration in Informatica hub Console.

Data Stew Console: This field is used to see the consolidated data and the data queued for exception handling in Informatica hub Console.

Administration Console: This component is used to assign various database administration activities and role-based security in Informatica hub Console.

Question: What are the biggest management and technical challenges in adopting Informatica Master Data Management?

Below mentioned are some difficult challenges in adopting MDM.

Model Agility: The data management model that has selected must be agile to suit the overall business day to day operation and for hassle free data integration.

Data Governance: A strong data governance strategy should be employed to identify, collect, measure, and remediate data quality issues in source systems.

Data Standards: The data standards set for master data should also be suitable for all other data types in the business entity.

Data Integration: To avoid errors in the data integration process that can lead to data loss in transit, a strict data integration policy should be established.

Data Stewardship: Data management is a vital step in maintaining data quality of the organization.

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