Description

This course takes a detailed look at the business problems caused by poorly managed 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 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 introduce governance into different data management disciplines including data naming, enterprise metadata management, 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 they 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, and designing and implementing data governance and MDM solutions.

Who should attend

This course is intended for chief data offices, enterprise architects, data architects, master data management professionals, business professionals, database administrators, data integration developers, and compliance managers who are responsible for management and 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.

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Outline

 
 
 
 
 
 
 
 
 
 

An Introduction to Enterprise Data Governance

This module defines what enterprise data governance is and looks at why companies need to invest in provisioning trusted, commonly understood, high-quality information 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 organization.

  • An introduction to enterprise data governance
  • 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)
  • Establishing a strategy for data governance
  • Getting the organization and operating model right
  • Key roles and responsibilities - data stewards and data owners
  • Formalizing EDG processes e.g. the dispute resolution process
  • 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

Data Governance Methodology & Technologies

Having understood strategy, this module looks at the methodology for data governance and data management. 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 management platforms provide the foundation in your enterprise architecture to manage information across the enterprise

  • A best practice step-by-step methodology for data governance and data management
    • Define, identify, assess, integrate, provision, monitor, protect and secure
  • The data management technology platform
  • The data management marketplace: Global IDs, IBM, Informatica, Microsoft, Oracle, SAP, SAS, Talend, Collibra
  • The data management platform 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 data management – 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 shared business vocabulary (SBV). The SBV should be defined and stored in a business glossary.

  • Data standardization using a shared business vocabulary
  • The role of an SBV in Master Data Management, Reference Data Management, SOA, DW and data virtualization
  • Approaches to creating an SBV
  • Enterprise Data Models & the SBV
  • Business glossary products
    • Alation, ASG, Collibra, Global IDs, Informatica Enterprise Data Catalog, IBM Information Governance Catalog, SAP Information Steward Metapedia, SAS Business Data Network
  • Planning for a business glossary
  • Organizing data definitions in a business glossary
  • Business involvement in SBV creation
  • Using governance processes in data standardization
  • Enterprise Data Modelling using an SBV

Data Discovery, Data Quality Profiling, Cleansing & Integration

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

  • Implementing systematic disparate data and data relationship discovery
  • Automated data discovery, automated data quality profiling and automated data mapping using information catalog software
  • Best practice data quality metrics
  • Key approaches to data integration – data virtualization and data consolidation
  • Taming the distributed data landscape using enterprise data cleansing and integration
  • The Enterprise Data Refinery - Hadoop or cloud storage as a staging area for enterprise data cleansing and integration
  • Using a data lake and information supply chain to create ready-made data products
  • Data provisioning – provisioning consistent, trusted data into data warehouses, MDM systems, NoSQL DBMSs and transaction systems
  • Provisioning consistent on-demand data services using data virtualization in an enterprise data marketplace
  • Achieving consistent data management across cloud and on-premise systems using data fabric software
  • Data Entry – implementing an enterprise data quality firewall using on-demand and event-driven data quality Services
  • Data quality at the keyboard
  • Data quality on inbound and outbound messaging
  • Integrating data quality with data warehousing & MDM
  • Monitoring data quality using dashboards
  • Managing data quality on the cloud

Master Data Management Design and Implementation

This module introduces master data management and looks at why businesses are serious about introducing it. It also looks at the components of an MDM and RDM system and the styles of implementation.

  • Reference data vs. master data
  • What is master data management?
  • Why is MDM needed? - benefits
  • Components of an MDM solution
  • How does MDM fit into a SOA?
  • MDM implementation options
    • Master data consolidation and synchronization vs. virtual MDM
    • Single entity hub vs. enterprise MDM
  • Identifying candidate entities
  • Understanding master data creation and maintenance
  • Master data implementation
  • Defining an SBV for master data entities
  • Hierarchy management
  • Master data modeling
  • Data discovery – identifying where your disparate master data is located
  • 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
  • Trends – Blockchain and MDM
  • The MDM solution marketplace
    • Ataccama, IBM, Informatica, Magnitude, Microsoft, Oracle, Riversand, SAP, SAS, Semarchy, Stibo, Talend, Teradata, TIBCO (Orchestra Networks)
  • Evaluating MDM products
  • Integration of MDM solutions with data management platforms
  • Implementing MDM matching on Hadoop, e.g. IBM Big Match and MDM Server
  • NoSQL Graph DBMSs and MDM
  • MDM in the Cloud – what’s the advantage?
  • Integrating MDM with enterprise portals
  • 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 MMD – 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 an 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

Instructor

Mike Ferguson

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
Stockholm

Pricing

The fee for this two-day course is EUR 1.450 (+VAT) per person. This includes two days of instruction, lunch and morning/afternoon snacks and course materials.

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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