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 Lakehouse for use in Analytics (24-25 November 2022 – Amsterdam)
Creating Data Products in a Data Mesh, Data Lake or Lakehouse for use in Analytics (17-18 October 2022 – Live Streaming Event)
Creating Data Products in a Data Mesh, Data Lake or Lakehouse for use in Analytics (26-27 September 2022, Live Streaming Event)
Centralised Data Governance of a Distributed Data Landscape (28-29 November 2022 – Live Streaming Event)
Centralised Data Governance of a Distributed Data Landscape (24-25 October 2022 – Live Streaming Event)
Centralised Data Governance of a Distributed Data Landscape (19-20 October 2022 – Live Streaming Event)
Following on from my last blog on data federation, the next data federation pattern I would like to discuss is a Master Data Virtual MDM pattern. This is as follows:
This pattern uses data virtualisation to provide one or more on-demand integrated views of master data entities such as customer, product, asset, employee, etc., even though the master data is fractured across multiple underlying systems. Applications, processes, portals, reporting tools and data integration workflows needing master data can acquire it on-demand via a web service interface or via a query interface such as SQL.
Pattern Example Use Case
A manufacturer needs to make sure that changes to its customer data are made available to its marketing, e-commerce, finance and distribution systems as well as its business intelligence systems to keep business operations, reporting and analysis running smoothly. A shipping group of companies needs to perform a routine maintenance upgrade on a particular type of asset. However, its assets are managed by different systems in multiple lines of business. In order to budget for this upgrade it needs to have a single view of assets to fully understand maintenance costs.
Reasons For Using It
To obtain a single integrated views of master data for consistency across business operations quickly at a relatively low cost.