OVERVIEW
Many businesses today are operating in a distributed computing environment with data and processes running across on-premises systems, multiple clouds, on SaaS applications and the edge. It this environment, with so much going on, data is much harder to find and govern. Also, the number of data sources continues to grow and master data, the most widely used data in any business, is becoming harder to find, manage and keep synchronised across so many systems in a hybrid computing environment. This two-day in-depth class looks at this problem and shows how to successfully implement a data governance program to govern data across a distributed data landscape. This includes governing data access security, data privacy, data sharing, data retention and data quality with data quality encompassing master data management to create a 360-degree view of customers, products, suppliers, and other core entities. It is intended for chief data officers, heads of data governance, enterprise architects, data architects, MDM professionals, business professionals, database administrators, data engineers, and compliance managers responsible for data governance.
The class takes a detailed look at the business problems caused by poorly governed data and how it can seriously impact business operations, cause unplanned operational costs, and destroy confidence in accuracy of business intelligence, machine learning model predictions and recommendations. It also defines the requirements that need to be met for a company to confidently define, manage, and govern data as well as create and share consistent reference and master data across operational applications and analytical systems both on-premises and in one or more clouds.
Having understood the requirements, you will learn what should be in a governance programme. This includes a data governance framework that includes data governance roles and responsibilities, processes, policies, technologies, and a core set of data governance capabilities to govern data across a distributed data landscape. It also includes a master data management strategy and what you need to do to bring your master data under control. We will look at how to make use of a business glossary, a data catalog with automated data discovery, data quality profiling, sensitive data classification, enterprise-wide and domain-oriented data governance policy definition by data owners around the business and policy enforcement across a distributed data landscape. We look at data cleaning and data integration, to provision master data and reference data products and how Customer Master Data can be combined with data warehouse and big data to create a Customer Data Platform (CDP) for a customer intelligent omni-channel front-office.
During the seminar we take an in-depth look at the technologies and best practice methodologies and processes for governing data across on-premises systems, SaaS applications, multiple clouds, and the edge.
AUDIENCE
This seminar is intended for business and IT professionals responsible for enterprise data governance including data access security, data privacy, data sharing, data usage, data retention, data quality (includes master data management) of both structured data and content. It assumes a basic understanding of data governance, data management, metadata, data warehousing, data cleansing, data integration, etc.
LEARNING OBJECTIVES
Attendees will learn how to set up an enterprise data governance program to systematically govern data and content across their distributed data landscape from a single place. Using a data governance framework and key technologies like data catalogs, data classifiers, data fabric and MDM they will learn what is needed to discover, classify and govern data and content. This includes data access security, data privacy, data loss prevention, data sharing, data retention, and data quality.
MODULE 1: WHAT IS DATA GOVERNANCE AND WHY DO WE NEED IT?
This session looks at what data governance is and what the main reasons are for needing to implement a data governance program. It looks at the need to comply with multiple data privacy regulations and legislation in a global business, the need to avoid data breaches and the challenges posed by a growing number of data sources and an increasingly complex distributed data landscape. It looks at the problems ungoverned data can bring and how they impact business operations, decision making and increase risk.
MODULE 2: WHAT ARE THE REQUIREMENTS AND WHAT’S NEEDED TO GOVERN DATA ACROSS DISTRIBUTED DATA LANDSCAPE?
This session looks at what the requirements are to govern data in a modern enterprise and what is needed to make it happen.
MODULE 3: THE IMPORTANCE OF A BUSINESS GLOSSARY
This session looks at the need to understand your data landscape from a business perspective. The key to making this happen is to establish a common business vocabulary in the business glossary of a data catalog to create common data names and definitions for your data. This enables you to search for and govern data across your data estate from a business perspective
MODULE 4: UNDERSTANDING YOUR DATA LANDSCAPE – AUTO DATA DISCOVERY, CATALOGUING AND MAPPING TO A BUSINESS GLOSSARY
Having defined your data, this session looks at discovering what data you have, where it is and how it maps to your business glossary to provide a business understanding of your data landscape
MODULE 5: CLASSIFYING DATA AND CONTENT TO KNOW HOW TO GOVERN IT
This session looks at manually and automatically labelling data to know how to govern it using predefined classifiers, user-defined classification schemes and trainable classifiers. It then looks at how classified data shows up in a data catalog and how policies can be assigned to labelled data to govern it across your data estate.
MODULE 6: GOVERNING DATA SECURITY ACROSS YOUR DISTRIBUTED DATA LANDSCAPE
Having classified the data and content in your data estate, this module looks at protecting data and content in your data estate with focus on that which is classified as sensitive or confidential. It looks at setting and enforcing policies to govern data access and usage security as well as governing data loss prevention.
MODULE 7: GOVERNING DATA PRIVACY ACROSS YOUR DISTRIBUTED DATA LANDSCAPE
This session looks at governing access to personal data across your data estate to remain compliant with legislation in multiple jurisdictions that your company operates
MODULE 8: GOVERNING DATA RETENTION ACROSS YOUR DISTRIBUTED DATA LANDSCAPE
This session looks at governing the lifecycle of data across your data estate and how you can set policies to control how long data is kept for and what happens to it on expiry. It also looks a special purpose condition such as “legal holds” placed on data by legal departments.
MODULE 9: GOVERNING DATA SHARING ACROSS YOUR DISTRIBUTED DATA LANDSCAPE
This session looks at producing trusted, compliant data to be shared across the enterprise and beyond and how data sharing can be governed
MODULE 10: GOVERNING DATA QUALITY ACROSS YOUR DISTRIBUTED DATA LANDSCAPE
This session looks at consistently governing data quality across your data estate.
MODULE 11: GOVERNING DATA QUALITY USING MASTER DATA MANAGEMENT
This session looks at master data management (MDM) and reference data management (RDM) and how to implement them to improve data quality.
MODULE 12: TRANSITIONING TO A COMMON APPROACH TO CENTRALISED MASTER DATA MAINTENANCE – THE CHANGE MANAGEMENT PROCESS
This session looks at the most difficult job of all – the change management process needed to get to common approach to master data maintenance. It looks at the difficulties involved, what really needs to happen and how to make it happen.
MODULE 13: COMBINING MDM WITH BIG DATA AND YOUR DATA WAREHOUSE TO CREATE A CUSTOMER DATA PLATFORM
This last session looks at combining Customer master data, Big Data, and your Data Warehouse to create a Customer Data Platform to support Marketing, Sales and Customer Service in the digital enterprise.
Managing Director, Intelligent Business Strategies Limited
Mike Ferguson is Managing Director of Intelligent Business Strategies Limited. As an independent IT industry analyst and consultant, he specialises in BI / analytics and data management. With over 40 years of IT experience, Mike has consulted for dozens of companies on BI/Analytics, data strategy, technology selection, data architecture, and data management. Mike is also conference chairman of Big Data LDN, the fastest growing data and analytics conference in Europe. 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. He teaches popular master classes in Data Warehouse Modernisation, Big Data Architecture & Technology, Centralised Data Governance of a Distributed Data Landscape, Practical Guidelines for Implementing a Data Mesh (Data Catalog, Data Fabric, Data Products, Data Marketplace), Real-Time Analytics, Embedded Analytics, Intelligent Apps & AI Automation, Migrating your Data Warehouse to the Cloud, Modern Data Architecture and Data Virtualisation & the Logical Data Warehouse.
Datum | Starttid | Sluttid | Anteckning! |
---|---|---|---|
28.10.2024 | 08:45 | 17:00 | |
29.10.2024 | 08:45 | 17:00 |
Schedule
(indicative)
8.15-8.45 Breakfast
8.45-11.30 Teaching
11.30-12.30 Lunch break
12.30-14.00 Teaching
14.00-14.15 Coffee break
14.15-17.00 Teaching
17.00 Training ends
If you can not participate this course, you can send someone else instead of you. If cancellation is done less than 14 days before the course start, we will charge 50% of the price. In case of no show without any cancellation, we will charge the whole price. Cancellation fee will also be charged in case of illness.