Master Data Management Basics
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Questions and Answers

The "Cleanse" pillar within OCH primarily focuses on which aspect of customer data?

  • Enhancing data quality through analysis, cleansing, and de-duplication (correct)
  • Importing and managing customer data from various sources
  • Creating a central hub for managing customer data
  • Providing tools for reporting and visualization of customer data

    • What does "Data Decay" refer to in the context of OCH?

  • The process of updating customer data with latest information
  • The process of identifying and removing duplicate customer records
  • The process of analyzing customer data to identify patterns and trends
  • The degradation or obsolescence of customer data over time (correct)

    • How does OCH address "Data Decay"?

  • By automatically deleting outdated customer records
  • By integrating with external data sources to update customer information
  • By providing dashboards for monitoring and correcting data decay issues (correct)
  • By creating a backup of all customer data at regular intervals

    • Which of the following is NOT a component of OCH's data decay management?

    <p>Decay Synchronization (D)</p> Signup and view all the answers

    What is the purpose of the "Guided Merge" feature in OCH?

    <p>To provide a user-friendly interface for reviewing and merging duplicate records (D)</p> Signup and view all the answers

    Which of the following is NOT a feature offered by the "Cleanse" pillar?

    <p>Data Segmentation (D)</p> Signup and view all the answers

    What is the primary objective of the "Consolidate" pillar?

    <p>To provide a centralized hub for managing customer data (A)</p> Signup and view all the answers

    What is the purpose of the "Un-Merge" feature in OCH?

    <p>To reverse a previously performed merge of duplicate records (B)</p> Signup and view all the answers

    What is the main problem with fragmented and inconsistent product data?

    <p>All of the above (D)</p> Signup and view all the answers

    Which data type is primarily concerned with operational processes and is managed by operational MDM?

    <p>Transactional Data (D)</p> Signup and view all the answers

    Which of these technologies is NOT primarily related to operational applications?

    <p>Business Intelligence (BI) (B)</p> Signup and view all the answers

    What is the primary role of Extraction, Transformation, and Loading (ETL) in analytical systems?

    <p>All of the above (D)</p> Signup and view all the answers

    Which of the following processes is NOT a core Master Data Management (MDM) process?

    <p>Optimize (B)</p> Signup and view all the answers

    What is the role of the Enterprise Service Bus (ESB) in the MDM Platform Layer?

    <p>Acting as a central communication hub (D)</p> Signup and view all the answers

    Which of the following is NOT a service offered within the MDM Analytic Services layer?

    <p>Data Quality Services (D)</p> Signup and view all the answers

    What is the primary function of the MDM Aware Applications in the Application Integration Architecture?

    <p>All of the above (D)</p> Signup and view all the answers

    Which of the following is a key benefit of using Oracle Customer Hub?

    <p>All of the above (D)</p> Signup and view all the answers

    What is the primary function of the Import Workbench in Oracle Product Hub?

    <p>Importing and managing product data from various sources (C)</p> Signup and view all the answers

    What is a key feature of Oracle Site Hub?

    <p>All of the above (D)</p> Signup and view all the answers

    What is a primary benefit of using Oracle Supplier Hub?

    <p>All of the above (D)</p> Signup and view all the answers

    What is the primary function of Oracle Hyperion Data Relationship Management?

    <p>All of the above (D)</p> Signup and view all the answers

    What is a key aspect of MDM Industry Verticalization?

    <p>All of the above (D)</p> Signup and view all the answers

    What is the primary function of Data Watch and Repair for MDM?

    <p>All of the above (D)</p> Signup and view all the answers

    Which of these is NOT a factor to consider when making a 'build vs buy' decision for MDM implementation?

    <p>Data warehousing technology used (C)</p> Signup and view all the answers

    Which of the following is NOT a key process for any MDM system?

    <p>Developing data quality goals (B)</p> Signup and view all the answers

    What is the primary goal of data profiling in an MDM implementation?

    <p>To determine the data quality of each source system before deploying MDM (D)</p> Signup and view all the answers

    What is the significance of understanding the data quality in each contributing source system?

    <p>It provides the necessary information to focus resources on the most critical data quality issues (C)</p> Signup and view all the answers

    Which of the following is a feature of Oracle Data Integration Suite (ODI) that supports data profiling?

    <p>Data Profiling and Correction (C)</p> Signup and view all the answers

    What is the purpose of consolidating master data into a central repository?

    <p>To ensure all systems have a unified view of master data (B)</p> Signup and view all the answers

    Which of these describes the principle of "governing" master data?

    <p>Ensuring data quality through cleansing, de-duplication, and enrichment (C)</p> Signup and view all the answers

    What is the advantage of having a single version of the truth for master data objects?

    <p>It provides a foundation for accurate business intelligence and reporting (A)</p> Signup and view all the answers

    What is the purpose of "sharing" master data?

    <p>To make it accessible to all enterprise business processes (C)</p> Signup and view all the answers

    What is a primary benefit of consolidating master data attributes?

    <p>It prevents data duplication and ensures data integrity. (B)</p> Signup and view all the answers

    What type of data models are used in Oracle MDM Hubs?

    <p>Operational data models (A)</p> Signup and view all the answers

    Which of the following is NOT a benefit of using Oracle MDM Hubs?

    <p>Automatic data migration and transformation (A)</p> Signup and view all the answers

    What is the primary function of Data Quality Servers in Oracle MDM Hubs?

    <p>To perform data cleansing, standardization, and deduplication tasks. (C)</p> Signup and view all the answers

    Why is Data Governance essential in master data management?

    <p>To establish ownership and accountability for data assets. (C)</p> Signup and view all the answers

    Which of the following is NOT a capability provided by Oracle MDM applications?

    <p>Data visualization and reporting (A)</p> Signup and view all the answers

    What is the key prerequisite for achieving true master data consolidation?

    <p>Consolidating all master data attributes from various sources. (B)</p> Signup and view all the answers

    Which of the following is an example of a business object that Oracle MDM Hubs can manage?

    <p>Product specifications (A)</p> Signup and view all the answers

    What is the primary purpose of Business Process Orchestration?

    <p>To combine various services into end-to-end business processes. (C)</p> Signup and view all the answers

    How does Business Process Orchestration relate to Master Data Management (MDM)?

    <p>MDM is a necessary component of Business Process Orchestration, providing consistent data for processes across different applications. (D)</p> Signup and view all the answers

    What is the main limitation of EAI and SOA in terms of data management?

    <p>They cannot ensure data integrity across multiple applications, leading to inconsistencies. (A)</p> Signup and view all the answers

    Why is data quality a challenge for cross-application business processes?

    <p>The quality of data in each application is often inconsistent, making it difficult to combine them reliably. (A), Data quality problems can hinder the ability to track and analyze data accurately, hindering business decision-making. (B), Different applications often have different data standards and formats, leading to inconsistencies. (C)</p> Signup and view all the answers

    How does MDM address the data quality issues related to cross-application business processes?

    <p>MDM establishes clear data ownership and accountability, reducing data errors and inconsistencies. (A), MDM offers a single, unified view of data across multiple applications, eliminating inconsistencies. (B), MDM implements strict data quality rules and validation processes, ensuring the accuracy and reliability of data. (D)</p> Signup and view all the answers

    What is the core benefit of using MDM in the context of cross-application business processes?

    <p>It enables a single, consistent view of business data, improving decision-making and process efficiency. (B)</p> Signup and view all the answers

    Which of the following is NOT a characteristic of MDM?

    <p>It focuses on managing and optimizing the flow of data within a single application. (A)</p> Signup and view all the answers

    What is the primary goal of Oracle Product Data Quality (PDQ)?

    <p>To enhance the accuracy and consistency of product data. (C)</p> Signup and view all the answers

    Which of these characteristics of product data does PDQ specifically target for improvement?

    <p>Data that is prone to errors and inconsistencies. (C)</p> Signup and view all the answers

    What key feature of PDQ enables it to handle the vast variability in product data?

    <p>Semantic-based recognition and auto-learning capabilities. (B)</p> Signup and view all the answers

    Which of the following is NOT a benefit provided by Oracle Product Data Quality?

    <p>Automated data analysis and insights generation. (D)</p> Signup and view all the answers

    How does PDQ address the challenge of duplicated product data?

    <p>By employing sophisticated semantic matching algorithms to identify duplicates. (A)</p> Signup and view all the answers

    What is the significance of PDQ's ability to "plug-in" to existing systems?

    <p>It enables the application of data quality standards across the entire enterprise. (C)</p> Signup and view all the answers

    What is the key takeaway from the statement that PDQ represents a "breakthrough" in product data solutions?

    <p>It is a more advanced and comprehensive solution compared to traditional methods. (A)</p> Signup and view all the answers

    What is the main benefit of PDQ's integrated capabilities for recognizing, cleansing, matching, governing, validating, correcting, and repurposing product data?

    <p>It reduces the need for separate tools and processes for each task. (A)</p> Signup and view all the answers

    Flashcards

    Service Oriented Architecture (SOA)

    A design framework allowing services to communicate over a network.

    Business Process Orchestration

    The coordination of multiple services to execute a business process.

    Master Data Management (MDM)

    A comprehensive method to manage critical data assets of an organization.

    Data Quality Problems

    Issues arising from inconsistent or inaccurate data across systems.

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    Application to Application (A2A) Integration

    Integration of different applications within the same organization.

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    Fragmentation in Data

    Dispersed and inconsistent data across multiple systems.

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    End-to-End Business Processes

    Processes that span across different functions within a business.

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    Data Silos

    Isolated data repositories that hinder data sharing across systems.

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    Baseline Data Quality Rules

    Initial standards to maintain data quality in MDM.

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    Data Consolidation

    Bringing together master data from multiple sources.

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    MDM Hub

    Central repository for master data management.

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    Operational Data Models

    Data models designed for OnLine Transaction Processing (OLTP).

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    Data Quality Servers

    Tools to standardize, cleanse, and match master data attributes.

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    Data Governance

    Discipline for managing data as a key enterprise asset.

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    Master Data Attributes

    Key characteristics or properties of master data.

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    Heterogeneous IT Environments

    Diverse systems and sources within an organization.

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    Enterprise Service Bus (ESB)

    A middleware architecture that enables communication and integration between different applications.

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    Data Profiling

    The process of analyzing data sources to assess their quality and completeness.

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    Consolidation of Data

    The act of combining master data from various sources into a central repository.

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    Data Quality Assurance

    The ongoing process of ensuring data is accurate, complete, and reliable.

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    Data Deduplication

    The process of eliminating duplicate entries in master data.

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    Data Enrichment

    Enhancing master data by adding relevant information from third-party sources.

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    Synchronization of Data

    The process of ensuring all systems have consistent and up-to-date master data.

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    Transactional Data

    Data generated from transactions or operational processes within a business.

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    Analytical Data

    Data used for analysis and decision-making based on past performance and trends.

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    Operational MDM

    Manages the master data required for daily operations within a business.

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    Enterprise Data Warehousing (EDW)

    A system used for reporting and data analysis, integrating data from various sources.

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    Enterprise Application Integration (EAI)

    Tools and techniques for integrating various applications across an enterprise.

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    Business Intelligence (BI)

    Technologies and strategies for analyzing data to support better business decisions.

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    Data Migration Services

    Processes that move data between storage types, formats, or systems securely.

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    Golden Record

    A single, reliable source of truth for master data in an organization.

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    MDM Pillars

    Core components that support Master Data Management frameworks, such as govern, consolidate, and share.

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    Composite Application Development

    Creating applications from reusable components that communicate across systems.

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    Supplier Lifecycle Management

    Framework overseeing the lifespan of supplier data from onboarding to discontinuation.

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    Implementation Best Practices

    Guidelines to ensure successful deployment and optimization of MDM strategies.

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    Oracle Product Data Quality

    A system designed to assess and improve product data management across various forms and industries.

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    Semantic Parsing Capabilities

    Advanced techniques used to understand and structure poorly formatted product data.

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    Standardization in Data Management

    Aligning data to established standards to ensure quality and consistency.

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    Exception Management and Remediation

    Integrated processes for handling data quality issues and correcting them.

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    Duplicated Data Handling

    Techniques for identifying and managing duplicate product data entries.

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    Category-Specific Rules

    Customized rules applied to data based on its specific category or context.

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    Automated Data Quality Processes

    Technology-driven methods that reduce manual labor in managing product data quality.

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    Enterprise-wide Applicability

    A system that can be integrated into various existing processes across the organization.

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    Cleanse Pillar

    A component in Customer Hub solutions for managing data quality through standardization, cleansing, matching, and enrichment.

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    Data Decay

    The degradation of information over time, making it stale or obsolete.

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    Decay Detection

    Identifying updates on attributes/relationships of records to measure data decay.

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    Decay Metrics Re-Calculation

    The process of updating data decay metrics based on predefined rules.

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    Decay Correctness

    Identifying stale data and triggering actions based on certain criteria.

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    Decay Report

    Periodically generated charts that show decay metrics for data records.

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    Guided Merge

    A feature allowing users to review and merge duplicate records with options for final versions.

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    Un-Merge Feature

    Rolls back a previously committed merge request to restore old records.

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    Study Notes

    Master Data Management (MDM)

    • MDM is a combination of applications and technologies used to consolidate, cleanse, and augment corporate master data, synchronizing it with applications, business processes, and analytical tools.
    • It significantly improves operational efficiency, reporting, and fact-based decision-making.
    • Master Data is critical information for transactional and analytical business operations.
    • Fragmented and inconsistent data creates significant issues regarding sales, marketing, supplier relationships, etc. and results in higher costs.

    Introduction

    • Fragmented and inconsistent data increases time-to-market, creates supply chain inefficiencies, weakens market penetration, and increases compliance costs.
    • Inconsistent customer data hides revenue, introduces risks, and creates sales inefficiencies.
    • Inconsistent supplier data reduces supply chain efficiency, impacts spend control initiatives, and increases the risk of supplier exceptions.

    Enterprise Data

    • Enterprises have three types of data: Transactional, Analytical, and Master.
    • Transactional data supports applications by recording transactions. Analytical data enables decision-making.
    • Master data represents business objects (e.g., Customers, Products).

    Operational MDM

    • Solutions focusing on managing transactional data within operational applications.
    • They heavily rely on integration technologies.
    • They improve operational efficiency but do not influence reporting or analytics.

    Analytical Data

    • Used for decision-making (e.g., customer buying patterns).
    • Analyzes large datasets in data warehouses for insight.
    • Includes identification of failure patterns, profitability, and marketing segments.

    Analytical MDM

    • Solutions focusing on managing Analytical Master Data.
    • Focuses on providing high-quality dimensions with their multiple simultaneous hierarchies for data warehousing and business intelligence (BI) technologies.
    • Improves reporting accuracy and fact-based decision-making, but has no influence on operational systems.

    Master Data

    • Represents shared business objects across multiple transactional applications.
    • Creates a single version of the truth across the operational IT landscape
    • Examples include Customers, Suppliers, Sites, Accounts, and Products.

    Information Architecture

    • Almost all companies have a heterogeneous set of applications (home-grown, purchased from vendors, or inherited).
    • MDM must seamlessly integrate with modern Service-Oriented Architectures (SOAs) and Business Process Orchestration to manage master data across multiple systems.
    • MDM becomes the central source for accurate, fully cross-referenced, real-time master data.

    Enterprise Application Integration (EAI)

    • EAI uses a metadata-driven approach to synchronize data across applications by storing details about which data to move, when to move it, what transformations to perform, and error recovery processes.
    • MDM enhances and manages SOA and BI systems in addition to supporting data governance, which defines data standards, access rights, and quality rules.

    Service-Oriented Architecture (SOA)

    • SOA exposes application features and functions as shared services using standardized interfaces that can be combined into end-to-end business processes.

    Data Warehousing

    • The Enterprise Data Warehouse (EDW) stores transaction history from operational applications.
    • Master Data delivers accurate dimensions, hierarchies, and cross references to the data warehouse, reducing reporting time.

    Business Intelligence (BI)

    • BI tools leverage a Data Warehouse solution for business users to gain report-based information.

    Data Quality Issues

    • EAI and SOA don't address fragmentation and poor-quality data problems that exist in pre-EAI/SOA environments.

    Oracle MDM High-Level Architecture

    • Oracle Fusion Middleware provides the supporting infrastructure
    • The Application Layer includes pre-built MDM Hubs and shared services.
    • The Governance Layer includes data governance solutions.

    MDM Application Layer

    • Oracle MDM includes purpose-built master data management applications (Hubs) and related data quality servers.

    Oracle MDM Pillars

    • Customer Hub, Product Hub, Supplier Hub, Site Hub, and Data Relationship Management.

    MDM Processes

    • Profile—Understanding data sources and quality.
    • Consolidate—Centralizing data into a master repository.
    • Govern—Establishing policies, rules, and quality standards for master data.
    • Share—Distributing master data to applications and ensuring consistency across the IT landscape.
    • Leverage—Using master data for enhanced reporting and decision-making.

    Oracle Data Quality Services

    • Addresses issues of unstructured, non-standard, and incomplete product data.
    • Used for standardization, cleansing, and matching of master data.

    Data Watch and Repair (DWR)

    • Provides monitoring capabilities for data quality and enforcement of governance rules in MDM Hubs.
    • Supports data profiling, discovery, and correction, augmenting the data quality process.

    MDM Implementation Best Practices

    • Strong governance, a robust data quality solution, and integration tools, are crucial for effective MDM implementations.
    • Oracle’s approach for MDM implementations is designed to support large or small projects and organizations that must handle large volumes of data.

    Conclusion

    • MDM delivers significant business value and enhances operational efficiency.
    • Utilizing Oracle MDM enables companies to govern data assets, operationalize data warehouses, consolidate systems, modernize applications, and improve reporting.
    • Oracle's MDM provides a more complete set of products and capabilities than other solutions in the market.

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    Related Documents

    Master Data Management PDF

    Description

    Test your knowledge on the foundational concepts of Master Data Management (MDM) and Operational Customer Hub (OCH). This quiz covers topics like data cleansing, data decay, and key features of MDM processes. Dive deep into understanding how data management impacts organizational efficiency.

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