Description

Many businesses today are operating in a distributed computing environment with data and processes running across the data centre, multiple clouds and the edge. In this environment, with so much going on, master data, the most widely used data in any business, is becoming harder to find, manage and keep synchronised. This two-day course looks at this problem shows how to successfully implement master data management to create a 360 degree view of customers, products, suppliers and other core entities. 

This course takes a detailed look at the business problems caused by poorly managed master data including inconsistent identifiers, data names and policies, poor data quality, poor information protection, and piecemeal project-oriented approaches to data integration.  It also defines the requirements that need to be met for a company to confidently define, manage and share reference data, master data and transactional data across operational and analytic applications and processes both on-premise and in the cloud.

Having understood the requirements, you will learn what should be in a master data management strategy and what you need in terms of people, processes, methodologies, and technologies to bring your data under control. In addition, we will look at how to manage, leverage, make use of a business glossary, data modelling, data relationship discovery, data profiling, data cleaning, data integration, data service (Data-as-a-service) provisioning, reference data management and master data management.

During the course, we take an in-depth look at the technologies needed in each of these areas as well as best-practice methodologies and processes for data governance and master data management.

Why attend

You will learn how to set up an enterprise data governance program and to determine what technologies you need for enterprise data governance, data integration and master data management (MDM). In addition, you will learn when to use certain technologies over others and methodologies to use for metadata management, data integration, to provision master data and reference Data as a Service. We also look at how Customer Master Data is being combined with Data Warehouses and Big Data to create new Customer Data Platforms (CDP).

Who should attend

It is intended for chief data officers, enterprise architects, data architects, master data management professionals, business professionals, database administrators, data integration developers, and compliance managers who are responsible for management of specific master data like customer data, product data and supplier data as well as the governance of enterprise data.

Prerequisites

This course is intended for business and IT professionals responsible for enterprise data governance including metadata management, data integration, data quality, master data management and enterprise content management. It assumes that you have an understanding of basic data management principles as well as at least a high level of understanding of the concepts of data migration, data replication, metadata, data warehousing, data modelling, data cleansing, etc.

Code: DGMDM2020
Price: 1.450 EUR

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Outline

 
 
 
 
 
 
 
 
 
 

Why Is Management Of Core Data So Important

This module looks at the increasingly complex distributed data landscape, the problems it brings and why companies need to invest in provisioning trusted, commonly understood, high quality data services across the enterprise to guarantee consistency. It also looks at why data integration and data management should now be a core competency for any organisation.

  • The ever-increasing distributed data landscape
  • The impact of unmanaged data on business profitability and the ability to respond to competitive pressure
  • Is your data out of control?
  • Key requirements for Enterprise Data Governance (EDG)
  • Reference Data vs. Master Data
  • What is Master Data Management?
  • Why is MDM needed? - benefits
  • Establishing a strategy for governing your core data
  • Getting the organization and operating model right
  • Key roles and responsibilities - data stewards and data owners
  • Core processes needed to establish and govern commonly understood data
  • Types of policies needed to govern data
    • Data integrity rules
    • Data validation rules
    • Data cleansing rules
    • Data integration rules
    • Data privacy rules
    • Data access security
    • Data lifecycle management

A Methodology & Technologies To Get Data Under Control

Having understood why trusted data is so critical, this session looks at methodology for getting your core data under control. It also looks at the technologies needed to help apply it to your data to bring it under control. It also looks at how data fabric software provides the foundation in your enterprise architecture to manage information across the enterprise

  • A best practice step-by-step methodology for trusted data provisioning
    • Define, identify, assess, integrate, provision, monitor, protect and secure
  • Data fabric – the new platform for discovering, profiling, mapping, cleaning, integrating and provisioning data
  • The data fabric marketplace
  • The role of data fabric in your enterprise architecture
  • Data governance and data management implementation options
    • Centralized, distributed or federated
  • The impact of Self-service BI and self-service data integration – the need for data governance in our business units
  • Data management on-premise and on the cloud

Data Standardization & The Business Glossary

This module looks at the first step in getting in control – the need for data standardization. The key to making this happen is to create common data names and definitions for your data to establish a common business vocabulary in the a business glossary of a data catalog.

  • Data standardization using a common business vocabulary
  • The role of common vocabulary in Master Data Management, Reference Data Management, SOA, DW and data virtualization
  • Approaches to creating a common vocabulary 
  • Enterprise Data Models & the common vocabulary 
  • Business glossary products
    • Alation, ASG, Amazon Glue, Collibra, Global IDs, Informatica Axon & Enterprise Data Catalog, IBM Watson Knowledge Catalog, SAS Business Data Network, Talend Business Glossary
  • Planning for a business glossary
  • Organizing data definitions in a business glossary
  • Glossary roles and responsibilities
  • Glossary term ratings, approval and dispute resolution processes
  • Leveraging a common vocabulary in Data Modelling

Auto Data Discovery, Data Quality Profiling, Cleansing & Integration

Having defined your data, this module looks at the next steps in a methodology, discovering where your data is and how to get it under control

  • Using a data lake as a staging area for data cleansing and integration
  • Automated data relationship discovery using a Data Catalog
  • Automated data mapping
  • Automated data quality profiling
  • Approaches to integrating data
  • Generating data cleansing and integration services using common metadata
  • Data provisioning – provisioning consistent, data services in a data marketplace for use in MDM and other systems
  • Provisioning consistent data on-demand across cloud and on-premise systems using data virtualisation
  • Monitoring data quality and and policies across a distributed data landscape
  • Managing data quality on the cloud

Master Data Management Design and Implementation

This module looks at the components of a master data management (MDM) and RDM system and the styles of implementation.

  • What does MDM 360 mean for master data entities, e.g. Customer 360, Supplier 360, Product 360 …
  • Why is MDM needed? - benefits
  • Components of a MDM solution
  • MDM implementation styles options
    • Real-time master data synchronisation
    • Virtual MDM (Index / Registry)
    • Single entity hub vs. enterprise MDM
  • How does MDM fit into an SOA?
  • Identifying candidate entities
  • Understanding master data creation and maintenance
  • Master data implementation
  • Defining a common vocabulary for master data entities
  • Master Data Hierarchy management
  • Master data modeling
  • Master Data discovery – identifying where your disparate master data is located using a data catalog
  • Mapping your disparate master data 
  • Profiling disparate master data to understand data quality
  • Creating trusted master data entities using data cleaning and data integration
  • Implementing outbound master data synchronization
  • Identifying and re-designing master data business processes
  • The MDM solution marketplace
    • Ataccama, IBM, Informatica, Magnitude, Microsoft, Reltio, Oracle, Riversand, SAP, SAS, Semarchy, Stibo, Talend, TIBCO and more
  • Evaluating MDM products
  • Integration of MDM solutions with data fabric platforms
  • Implementing MDM matching at scale, e.g. IBM Big Match and MDM Server
  • NoSQL Graph DBMSs and MDM
  • MDM in the Cloud – what’s the advantage?
  • Sharing access to master data via master data services in a Service Oriented Architecture (SOA)
  • Leveraging SOA for data synchronization
  • Integrating MDM with operational applications and process workflows
  • Using master data to tag unstructured content

Transitioning to Enterprise MDM – The Change Management Process

This module looks at the most difficult job of all – the change management process needed to get to enterprise master data management. It looks at the difficulties involved, what really needs to happen and the process of making it happen.

  • Starting a MDM change management program
  • Changing data entry system data stores
  • Changing application logic to use shared MDM services
  • Changing user interfaces
  • Leveraging portal technology for user interface re-design
  • Leveraging a service-oriented architecture to access MDM shared services
  • Changing ETL jobs to leverage master data
  • Hierarchy change management in MDM and BI systems
  • Transitioning from multiple data entry systems to one data entry system
  • Transitioning change to existing business processes to take advantage of MDM
  • Planning for incremental change management

From MDM To Customer Data Platforms

This last module looks at the emergence of Customer Data Platforms (CDP) that combine Customer MDM, Big Data and Data Warehouses to create a Customer Data Platform to support Marketing, Sales and Customer Service in the digital enterprise.

  • What is a Customer Data Platform?
  • Customer MDM VS a CDP
  • Components of a CDP
  • The CDP Marketplace and what to look for
  • Integrating CDPs with digital and traditional marketing, sales and service applications
  • Creating a CDP in your enterprise

Instructor

Mike Ferguson

Mike is Managing Director of Intelligent Business Strategies Limited.  As an analyst and consultant he specialises in business intelligence and enterprise business integration. With over 35 years of IT experience, Mike has consulted for dozens of companies. He has spoken at events all over the world and written numerous articles.  Mike is Chairman of Big Data LDN – the fastest growing Big Data conference in Europe, and chairman of the CDO Exchange.  Formerly he was a principal and co-founder of Codd and Date Europe Limited – the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing Director of Database Associates.  He teaches popular master classes in Analytics, Big Data, Data Governance & MDM, Data Warehouse Modernisation and Data Lake operations.

Dates

15 Oct16 Oct
Virtual Live CET Time
Sold out!

Pricing

The fee for this 2-day course is EUR 1.450,00 (+VAT) per person.

We offer the following discounts:

  • 10% discount for groups of 2 or more students from the same company registering at the same time.
  • 20% discount for groups of 4 or more students from the same company registering at the same time.
 
Note: Groups that register at a discounted rate must retain the minimum group size or the discount will be revoked. Discounts cannot be combined.

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