Preliminary Considerations / Motivation

Brief Content Summaries for Each Chapter:

Cost Development in Companies for Data Management and Integration

Examines the increasing costs of data management and integration, highlighting their impact on organizational IT budgets and emphasizing the growing need for efficient solutions.

Importance of MDM, Data Lake, and Data Fabric Solutions

Discusses the critical role of MDM, Data Lake, and Data Fabric solutions in modern data management, stressing their significance in the absence of industry-wide standards.

The Impact of Introducing an Open Industry Standard for MDM

Explores the benefits and transformative potential of establishing an open industry standard for MDM, both for the software industry and organizational data management.

The Ripple Effect of a Standardized MDM Framework

Analyzes the hypothetical impacts of a standardized MDM framework on software users and vendors, detailing both advantages and challenges.

The Impact of Introducing an Open Industry Standard for MDM on Data Mesh Solutions

The chapter describes how the introduction of an Open Industry Standard for Master Data Management (MDM) significantly impacts data mesh solutions. It highlights four key aspects.

Impact of an Industry-wide MDM Standard on BI Solutions Providers

The chapter discusses the significant effects of an industry-wide Master Data Management (MDM) standard on Business Intelligence (BI) solution providers. Positively, it would improve data quality, simplify data integration, enhance system interoperability, create new product feature opportunities, and potentially expand the market. Negatively, it might increase adaptation and compliance costs, reduce competitive edges for custom solutions, increase dependence on the standard, risk market homogenization, and potentially lead to an overreliance on the standard.

Impact of an Industry-wide MDM Standard on Data Warehouse Solutions Providers

This section analyzes the implications of an MDM standard on data warehouse solutions providers. Advantages include standardized data integration, enhanced interoperability, improved data quality, innovation opportunities, and market expansion. Disadvantages involve adaptation costs, reduced differentiation, compliance challenges, potential obsolescence risks, and dependency on standard development.

Master Data Management Solutions

The chapter covers Master Data Management (MDM) solutions, focusing on their role in ensuring data uniformity, accuracy, and consistency. Key aspects include data consolidation, governance, quality, integration capabilities, stewardship, data relationship management, flexibility, scalability, and providing a single version of truth. Benefits highlighted are improved data accuracy, operational efficiency, decision-making, regulatory compliance, customer satisfaction, and cost reduction.

Delimitation/ Definition Data Mesh

The chapter provides a concise overview of Data Mesh, an architectural paradigm in data management centered around decentralization. The Key elements of Data Mesh are outlined.

Delimitation/ Definition Data Fabric Solutions

This chapter explains data fabric solutions as architectures providing integrated data services across various environments. They focus on seamless integration, data management, and governance, supporting a range of data types. Data fabric solutions facilitate real-time data processing, self-service access, and utilize AI and ML for data management, offering scalability and flexibility.

Delimitation/ Definition Data Lake Solutions

Data Lake Solutions are discussed as systems for storing, processing, and analyzing vast amounts of raw data. They are designed for handling high-volume, diverse data and are scalable. The chapter highlights their flexibility in data processing, governance, security, and integration capabilities. Data Lakes are cost-effective and provide self-service access for data analysis.

Distinctions and Overlaps between Data Lake and Data Fabric Solutions

This chapter compares and contrasts Data Lake and Data Fabric solutions. Data Lakes focus on storing large data volumes, while Data Fabrics integrate and manage data across multiple sources. The chapter explores their overlapping features in data integration and analytics support, and their role in scalability and cloud-based deployment.