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Featured Content

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Practical Guidelines for Implementing Data Products (10-11 April 2025, Helsinki)

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Practical Guidelines for Implementing Data Products (8-9 May 2025, Italy Livestream Training)

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Streaming Analytics & Embedded BI – the Route to Always On Smart Business Operations

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Delivering Operational Analytics using Spark and NoSQL Data Stores

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AI-Assisted Active Data Governance (19-20 June 2025, Italy Livestream Training)

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Simplifying Data Connectivity in a Data-Driven Enterprise

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Architecting a Big Data Platform for Analytics

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Building a Modern Data Warehouse on a Distributed Data Cloud

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Data Observability – What Is It and Why Should You Care?

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Governing Data across a Distributed Data Estate

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Interactive Data Exploration with an In-Memory Analytics Engine

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Data Intelligence – Curating Business Ready Data and Analytics in a Data Driven Enterprise

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IoT Analytics Architecture

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Maximising the Value of Your BI and Machine Learning Models in a Data-Driven Enterprise

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Practical Guidelines for Implementing Data Products (25-26 March 2025, Cologne)

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An Analytical Platform for the Smart Enterprise

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Active Intelligence for Smart Business

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Self-Service Data Integration, Data Discovery & Analysis for the Citizen Data Scientist

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The Business Value of Master Data

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Eight Steps to Deploying an Enterprise Portal – Part Six

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Building a Data & AI Strategy for a Data-Driven Enterprise (7-8 April, Italy Livestream Training)

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A Universal Data Authorisation Security Fabric for the Data Driven Enterprise

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Trends in Establishing a Data Driven Enterprise

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Data Fabric, Data Mesh or Both – What is Best for Your Organisation?

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Enabling a Customer Data Platform Using Data Virtualisation

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The Value of Data Virtualisation in a Data Mesh

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Industrialising Data and AI for The Real-time Enterprise

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The Business Value of Kubernetes in an Analytical Environment

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IoT Analytics Architecture

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Asset Performance for Smart Manufacturing

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Modern Data Architecture (5-6 May 2025, Amsterdam)

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Modern Data Architecture (16-17 June 2025, Italy, Live Streaming)

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Reducing ETL Costs and Integrating Analytical Workloads in Hyperscale Data Warehousing

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Challenges of Migrating your Data Warehouse to the Cloud

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Accelerating Workload Migration to the Cloud using Data Virtualisation

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Data Science Workbenches & Machine Learning Automation

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The Enterprise Data Refinery

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The Value of Data Fabric in a Data-Driven Enterprise

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Embedded Analytics, Intelligent Apps, AI Agents & AI Automation (19-20 May 2025, Italy Livestream Training)

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The Challenge of Big Data

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Are Data Catalogs Important in a Data Driven Enterprise?

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Smart Infrastructure & Smart Applications for the Smart Business – Infrastructure & Application Performance Monitoring

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Unified Data Integration in a Distributed Data Landscape

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Scalable, In-Memory SQL on Hadoop – Why Open Source is Not the Only Option

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Data Warehouse Automation & Real-time Data – Reducing Time to Value in a Distributed Analytical Environment

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Data Virtualisation – Flexible Technology for the Agile Enterprise

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Shortening Time to Value by Building Data Products in a Lakehouse

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Accelerating Data Science Using Machine Learning Automation

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AI-Assisted Active Data Governance (12-13 June 2025, Amsterdam)

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Interactive Data Exploration Using a Context-Aware Analytics Engine

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Organising the Data Lake – The Central Role of an Information Catalog

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Conquering Data Complexity – Using Smart Data Management

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Building Big Data Analytical Applications at Scale Using Existing ETL Skillsets

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The Impact of Big Data on ETL

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Edge Advanced Analytics

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Practical Guidelines for Implementing Data Products (3-4 April 2025, Stockholm)

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MDM / RDM


Home / Data Management / MDM / RDM
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    The Business Value of Master Data

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    Reference Data Management for Solvency II

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    Implementing Customer Master Data Management

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    Getting Started with Master Data Management

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    Integrating MDM & Business Intelligence, Part 1

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    Integrating MDM & Business Intelligence, Part 2

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    Transitioning to Enterprise MDM – The Change Management Process, Part 1

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    Transitioning to Enterprise MDM – The Change Management Process, Part 2

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