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Business Management


last update : 07/01/2014

Enterprise Information Management - From Strategy to Implementation




This three-day, in-depth, seminar takes a detailed look at the business problems caused by poorly managed information including inconsistent identifiers, data names and policies, poor data quality, poor information protection, and piecemeal project oriented approaches to data integration. It looks at reference data, master data, transaction data, metrics, big data and unstructured content (e.g. documents, email, etc). It looks at the requirements that need to be met for a company to confidently be able to define, govern, manage and share trusted high quality information across operational and analytic applications and processes. In addition it addresses the issues of managing data in a hybrid cloud computing and on-premise environment.
Having understood the requirements, you will learn what should be in an EIM 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 information management disciplines including data naming, enterprise metadata management, data modelling, data relationship discovery, data profiling, data cleaning, data integration (batch, on-demand and event-driven), data provisioning, master data management and enterprise content management .
During the three days 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. 



This seminar is intended for data architects, chief data officers, master data management professionals, content management professionals, database administrators, big data professionals, data integration developers, and compliance managers who are responsible for enterprise information management (EIM).  This includes 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 a high level of understanding of the concepts of data migration, data replication, metadata, data warehousing, data modelling, data cleansing, etc. 

Learning Objectives

Attendees will learn how to define a strategy for enterprise information management and how to implement it within their organisation. They will also learn the importance of data standardisation and business glossaries when defining data to be managed and the operating model for effective information governance. They will learn what technologies they need and an EIM implementation methodology to get their data under control.  They will learn how to apply this methodology to get master and reference data, big data, data warehouse data and unstructured data under control whether it be on-premise or in the cloud.


Mike Ferguson is Managing Director of Intelligent Business Strategies Limited.  As an analyst and consultant he specialises in business intelligence / analytics, data management, big data and enterprise business integration.  With over 33 years of IT experience, Mike has consulted for dozens of companies on business intelligence strategy, technology selection, enterprise architecture, and data management.  He has spoken at events all over the world and written numerous articles.  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 Big Data, New Technologies for Data Warehousing and BI, Operational BI, Enterprise Data Governance, Master Data Management, Data Integration and Enterprise Architecture.
This session introduces enterprise information management (EIM) and looks at the reasons why companies need it. It looks at what should be in your EIM strategy, the operating model needed to implement EIM, the types of data you have to manage and the scope of EIM implementation. It also looks at the policies and processes needed to bring your data under control. 
  • What is EIM and why is it needed?
  • Key requirements for EIM 
  • Structured data – master, reference and transaction data
  • Semi-structured data – JSON, XML, email 
  • Unstructured data - text, video, audio
  • Dealing with new data sources - cloud data, sensor data, social media data, smart products (the internet of things)
  • Understanding EIM scope 
    • OLTP systems
    • Data Warehouses
    • Big Data systems
    • MDM and RDM systems
    • Data virtualisation
    • Messaging, ESBs and process data flows
    • Enterprise Content M’gmt
  • Building a business case for EIM 
    • The impact of unmanaged data on business performance 
  • Defining a strategy for EIM
  • Resources - key roles and responsibilities
  • Getting the organisation and operating model right
  • Types of EIM policy
    • Data integrity rules
    • Data validation rules
    • Data cleansing rules
    • Data integration rules
    • Data provisioning rules
    • Data privacy rules 
    • Data access security
  • Formalising EIM processes, e.g. the dispute resolution process
  • EIM in your enterprise architecture



Having understood strategy, this session looks at methodology for EIM and the technologies needed to help apply it to your data to bring it under control. It also looks at how EIM platforms provide the foundation in your enterprise architecture to manage information across the enterprise

  • A best practice step-by-step methodology for EIM and data governance
    • Define, Identify, Assess, Integrate, Provision, Monitor, Protect and Secure
  • The EIM technology platform
  • The EIM Marketplace: Actian, Global IDs, IBM InfoSphere, Informatica, Oracle, SAP, SAS, Talend
  • The EIM platform in your enterprise architecture
  • EIM implementation options
    • Centralised, distributed or federated
    • Self-service BI – the need for data governance at the edge 
    • EIM on-premise and on the cloud



This session looks at the first step in EIM – the need for data standardisation. 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. 

  • Semantic data standardisation using a shared business vocabulary
  • SBV vs. taxonomy vs. ontology
  • The role of a SBV in MDM, RDM, SOA, DW and data virtualisation
  • Approaches to creating an SBV
  • Enterprise Data Models & the SBV
  • Business glossary products
    • ASG, Collibra, Global IDs, Informatica, IBM InfoSphere Business Glossary, SAP Information Steward Metapedia, SAS Business Data Network
  • Planning for a business glossary Organising data definitions in a business glossary
  • Business involvement in SBV creation
  • Using governance processes in data standardisation
  • Enterprise Data Modelling using a SBV 



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

  • Implementing systematic disparate data and data relationship discovery 
  • Data discovery tools Global IDs, IBM InfoSphere Discovery Server, Informatica, Silwood, Sypherlink, SAS DataFlux
  • Automated data mapping
  • Data quality profiling
  • Best practice data quality metrics
  • Key approaches to data integration – data virtualisation, data consolidation and data synchronisation
  • Generating data cleansing and integration services using common metadata
  • Taming the distributed data landscape using enterprise data cleansing and integration 
  • The Enterprise Data Refinery - Hadoop as a staging area for enterprise data cleansing and integration
  • Data provisioning – provisioning consistent information into data warehouses, MDM systems, NoSQL DBMSs and transaction systems
  • Provisioning consistent on-demand information services using data virtualisation
  • Achieving consistent data provisioning in a SOA
  • Consistent data management across cloud and on-premise systems 
  • Data Entry – implementing an enterprise data quality firewall
    • Data quality at the keyboard
    • Data quality on inbound and outbound messaging
    • Integrating data quality with data warehousing & MDM 
    • On-demand and event driven Data Quality Services
  • Monitoring data quality using dashboards
  • Managing data quality on the cloud



This session 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 a MDM solution
  • How does MDM fit into a SOA?
  • MDM implementation options –Master Data Synchronisation 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 modelling
    • Disparate master data discovery and mapping 
    • Disparate master data profiling 
    • Creating a master data hub using data cleansing and integration 
    • Implementing master data synchronisation 
    • Identifying and re-designing master data business processes
  • The MDM solution marketplace 
  • Evaluating and combining MDM products
  • Integration of MDM solutions with EIM platforms
  • Integrating MDM with enterprise portals
  • Sharing access to master data via master data services in a Service Oriented Architecture (SOA)
  • Leveraging SOA for data synchronisation 
  • Integrating MDM with operational applications and process workflows
  • Using master data to tag unstructured content



This session 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



This session looks at how EIM methodology and processes can be applied to managing, governing and provisioning data in a Big Data analytical ecosystem and in traditional data warehouses. How do you deal with very large data volumes and different varieties of data? How does loading data into Hadoop differ from loading data into a data warehouse? What about NoSQL databases? How should low-latency data be handled? Topics that will be covered include:

  • Types of Big Data 
  • Connecting to Big Data sources, e.g. web logs, clickstream, sensor data, unstructured and semi-structured content 
  • The role of information management in an extended analytical environment
  • Supplying consistent data to multiple analytical platforms 
  • Best practices for integrating and governing multi-structured and structured Big data
  • Dealing with data quality in a Big Data environment
  • Loading Big Data – what’s different about loading Hadoop files versus NoSQL and analytical relational databases
  • Data warehouse offload – using Hadoop as a staging area and data refinery 
  • Governing data in a Data Science environment 
  • Joined up analytical processing from ETL to analytical workflows
  • Mapping discovered data of value into your DW and business vocabulary
  • Big data protection



Over recent years we have seen many major brands suffer embarrassing publicity due to data security breaches that have damaged their brand and reduced customer confidence. With data now highly distributed and so many technologies in place that offer audit and security, many organisations end up with a piecemeal approach to information audit and protection. Policies are everywhere with no single view of the policies associated with securing data across the enterprise. The number of administrators involved is often difficult to determine and regulatory compliance is now demanding that data is protected and that organisations can prove this to their auditors.  So how are organisations dealing with this problem?  Are data privacy policies enforced everywhere? How is data access security co-ordinated across portals, processes, applications and data? Is anyone auditing privileged user activity? This session defines this problem, looks at the requirements needed for Enterprise Data Audit and Protection and then looks at what technologies are available to help you integrate this into you EIM strategy

  • What is Data Audit and Security and what is involved in managing it? 
  • Status check - Where are we in data audit, access security and protection today? 
  • What are the requirements for enterprise data audit, access security and protection? 
  • What needs to be considered when dealing with the data audit and security challenge? 
  • What about privileged users? 
  • What technologies are available to tackle this problem? – IBM Optim and InfoSphere Guardium, Imperva, EMC RSA
  • How do they integrate with Data Governance programs? 
  • How to get started in securing, auditing and protecting you data


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