Creating Data Products in a Data Mesh, Data Lake or Data Lakehouse for use in Analytics (3-4 February 2022, Live Streaming Event)
Creating Data Products in a Data Mesh, Data Lake or Lakehouse for Use in Analytics (14-15 March 2022, Stockholm)
Creating Re-Usable Data Products for Analytics – Data Lake vs. Lakehouse vs. Data Mesh (28-29 March 2022, Live Streaming Event)
Smart Infrastructure & Smart Applications for the Smart Business – Infrastructure & Application Performance Monitoring
Data Warehouse Automation & Real-time Data – Reducing Time to Value in a Distributed Analytical Environment
Creating Data Products in a Data Mesh, Data Lake or Data Lakehouse for use in Analytics (3-4 March 2022, Live Streaming Event)
– Creating A Trusted Core and Customer Data Platform for the Data Driven Enterprise
Request information on running this seminar as an onsite (can be given as Live Streaming training)
Many businesses today are operating in a distributed computing environment with data and processes running across the data centre, multiple clouds and the edge. It 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 in-depth seminar 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. 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.
The seminar takes a detailed look at the business problems caused by poorly managed master data and how inconsistent identifiers and data names, poor data quality, lack of master data integration and synchronisation can seriously impact business operations, cause unplanned operational costs and destroy confidence in trust of business intelligence. It also defines the requirements that need to be met for a company to confidently define, manage and share reference and master data across operational applications and processes and analytical systems on-premises 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 master 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, to provision master data and reference data as a service (DaaS). We also look at how Customer Master Data is being combined with Data Warehouses and Big Data to create new Customer Data Platforms (CDP)
During the seminar 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 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.
Attendees 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, they 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.
Module 1: Why is Management of Core Data so Important?
Module 2: A Methodology & Technologies to Get Data Under Control
Module 3: Data Standardisation & the Business Glossary
Module 4: Auto Data Discovery, Data Quality Profiling, Cleansing & Integration
Module 5: Master Data Management Design and Implementation
Module 6: Transitioning to Enterprise MDM – The Change Management Process
Module 7: From MDM to Customer Data Platforms
Click here for a full brochure