Most Recent Dama CDMP-RMD Exam Dumps

Deterministic matching, also known as exact matching, relies on predefined rules and algorithms to parse and standardize data, ensuring that records are compared based on exact or standardized values. This approach uses defined patterns and rules to determine whether two records represent the same entity by matching key attributes exactly. Deterministic matching is precise and unambiguous, making it a common approach for high-certainty matching tasks, although it can be less flexible than probabilistic methods that allow for variations in data.

DAMA-DMBOK2 Guide: Chapter 10 -- Master and Reference Data Management

'Entity Resolution and Information Quality' by John R. Talburt

Reference data plays a crucial role in financial trading. It includes data such as financial instrument identifiers, market data, currency codes, and regulatory classifications. Despite the dynamic nature of financial trades, reference data provides the necessary static information to execute and settle transactions. Industry estimates suggest that approximately 70% of the data used in financial transactions is reference data, underscoring its importance in the financial sector.

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 11: Reference and Master Data Management.

'The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling' by Ralph Kimball and Margy Ross.

Industry publications and whitepapers on reference data management in financial services.

Management of Reference and Master Data aims to mitigate the challenges of disparate data, which typically arise from:

Organic Growth:

Unplanned Expansion: Over time, organizations often develop new systems and applications organically, leading to isolated and redundant data stores.

Inconsistent Data: These disparate systems often result in inconsistent and unreliable data.

Isolated Systems:

Siloed Applications: Independent systems that do not communicate effectively with each other can lead to multiple versions of the same data.

Lack of Integration: Without proper integration, data consistency and quality suffer.

Mergers and Acquisitions:

Combining Systems: Mergers and acquisitions introduce the challenge of integrating different data systems and standards.

Data Redundancy: Newly acquired systems often come with their own data sets, leading to redundancy and conflicts.

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, 'The DAMA Guide to the Data Management Body of Knowledge (DMBOK)'

Definitions and Context:

Reference Data Dictionaries: These are authoritative resources that provide standardized definitions and classifications for data elements.

External Sources of Data: These are data sources that come from outside the organization and are used for various analytical and operational purposes.

Reference Data Dictionaries often contain listings and definitions for data that are used across different organizations and systems, ensuring consistency and interoperability.

They typically include external data sources, which need to be standardized and understood in the context of the organization's own data environment.

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.

ISO/IEC 11179-3:2013, Information technology - Metadata registries (MDR) - Part 3: Registry metamodel and basic attributes.

Several factors can deter a company from developing a master data program, including the perceived value, commitment level, disruption, and data quality priorities.

Fail to See Value in Integrating Their Data:

If a company does not recognize the benefits of integrating and managing master data, it may not invest in an MDM program.

Lack of Commitment:

Developing an effective MDM program requires long-term commitment from leadership and stakeholders. Without this commitment, the program is unlikely to succeed.

The Process is Too Disruptive:

Implementing an MDM program can be disruptive to existing processes and systems. The perceived disruption can deter companies from pursuing it.

Data Quality is Not a Priority:

If a company does not prioritize data quality, it may not see the need for a robust MDM program. Poor data quality can undermine the effectiveness of business processes and decision-making.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials