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59 Data in AI Era : Master Data Management (MDM)

 From Data Warehouse to AI-Augmented Enterprise Master Data Management (MDM): Creating a Single Version of Business Reality Abstract As organizations modernize their data platforms, they often discover that technology alone cannot guarantee consistent business decisions. Different systems frequently maintain different representations of customers, products, suppliers, locations, and other core business entities. These inconsistencies create analytical confusion, operational inefficiencies, and increasingly, challenges for AI-driven systems. Master Data Management (MDM) emerged to address this problem by establishing a trusted and consistent representation of critical business entities across the enterprise. While often viewed as a data management initiative, MDM is fundamentally a business transformation capability that enables governance, analytics, operational efficiency, and AI readiness. This article explores the principles of Master Data Management, its evolution, implementati...
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58 Data in AI Era : Data Governance, Metadata, and Lineage

  From Data Warehouse to AI-Augmented Enterprise Data Governance, Metadata, and Lineage: Why Trust Becomes the Central Problem in AI-Era Data Systems Abstract Over the past decade, organizations have invested heavily in modernizing their data platforms. Data warehouses migrated to the cloud. ELT replaced traditional ETL. Data lakes evolved into lakehouses. Self-service analytics became a strategic objective. More recently, AI and Generative AI have accelerated expectations around data-driven decision making. Yet despite these technological advances, a common challenge continues to emerge across industries: Organizations have more data than ever before, but less confidence in the answers produced from it. The challenge is no longer data availability. The challenge is trust. When executives question dashboard numbers, when analysts spend more time validating data than analyzing it, or when AI systems generate insights that cannot be explained, the underlying issue is usually not tech...
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57 Data in AI Era : The Modern Cloud Data Stack

  From Data Warehouse to AI-Augmented Enterprise The Modern Cloud Data Stack: How Cloud Platforms Changed Data Engineering — and What They Didn’t Abstract The emergence of cloud-native data platforms fundamentally changed the economics, scalability, and operational model of enterprise analytics. Systems that once required expensive hardware procurement, rigid capacity planning, and highly specialized infrastructure teams can now be provisioned elastically through managed cloud services. This transformation enabled organizations to process data at unprecedented scale while simultaneously accelerating experimentation, analytics delivery, and AI adoption. Technologies such as Snowflake, BigQuery, Databricks, dbt, and cloud object storage redefined how modern data platforms are built and operated. However, while the tooling landscape evolved dramatically, the underlying architectural challenges remained largely unchanged. Organizations still need to solve for: Data integration...
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56 Data in AI Era : From Data Warehouse to AI-Augmented Enterprise

From Data Warehouse to AI-Augmented Enterprise SQL for Data Warehousing: Where Business Logic Becomes Analytical Reality Abstract Dimensional modeling defines how analytical data is structured. SQL operationalizes that structure into usable insight. Despite the emergence of modern orchestration tools, semantic layers, AI-assisted analytics, and low-code platforms, SQL remains the dominant language of analytical systems. This persistence is not accidental. SQL sits at the intersection of data structure, business logic, and computational execution. This article examines SQL in the context of data warehousing—not as a programming language to memorize, but as the mechanism through which organizations encode analytical meaning into operational systems. We explore how warehouse SQL differs fundamentally from transactional SQL, why schema design determines query quality, how modern cloud warehouses optimize analytical workloads, and why SQL continues to underpin AI-era data systems. 1...
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