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
Following on from my last blog, the next data federation I would like to discuss is the Data Discovery pattern. This is as follows.
This pattern uses data virtualization to query structured data held in multiple underlying core operational and analytical databases and file systems to answer business questions. It uses a search like user interface that can return results as to where data associated with items being searched on can be found, e.g. a search could be done on a customer name, an order and a sales representative name. The data discovery pattern allows users to query the virtual views of a data held in multiple systems via the data virtualization server. Through this mechanism users can find relationships between different data items across systems, view the data as if in a single system to discover answers to business questions.
Pattern Example Use Case
Call centres are receiving a lot of enquiries as to why their orders are not being fulfilled. Data can be queried using a customer name, products ordered and the sales representative who took the order. Results returned show all occurrences of data about orders, the customer and the sales representative across multiple systems. Using the virtual views, this data can be analysed across systems to see what the reason is for delays in deliveries are, e.g. order exceeds credit limit or order cannot be fulfilled due to inventory levels being too low.
Reasons For Using It
This pattern has the effect of broadening access to enterprise data from a much larger user base who are confident in using a search box interface but who are not aware of where the data they need is located and who do not have the time and/or skills to use BI tools.