Smart Infrastructure & Smart Applications for the Smart Business – Infrastructure & Application Performance Monitoring
Creating Data Products in a Data Mesh, Data Lake or Lakehouse for Use in Analytics (9-10 June 2022, Stockholm)
Data Warehouse Automation & Real-time Data – Reducing Time to Value in a Distributed Analytical Environment
Yesterdays announcement by IBM on its web site that it has signed an agreement to acquire DataMirror is more evidence that the software giants of the industry are starting to compete for more marketshare in the data management marketplace. In this announcement IBM stated that its intention is to use the DataMirror technology to strengthen its Information Server suite of data management tools particularly in the areas of real-time change data capture, heterogeneous data replication and synchronisation and also high availability.
The trend here is clear and that is that data management tools such as business vocabulary tools (aimed at business users), metadata discovery and mapping tools, data modelling, data profiling and cleansing as well as data integration are all converging into a common platform of tools that have been integrated on a common metadata repository. Why is this needed? The reason is of course that enterprises are pushing for a common toolset for any kind of data management whether it be data consildation for data warehousing and master data management, data federation for on-demand reporting, data replication or data synchronisation. It is a natural thing to expand the use of these tools beyond popular areas like data warehousing into other applicational uses across the enterprise. Even unstructured data integration is now possible. The world of XML data integration is also on the increase including RSS Feeds as a data source. All of this is demanded at lower and lower latency. Data and metadata services in a SOA is a trend but the challenge for most of use is to identify and prioritise business areas that need to exploit these services in order to improve data supply and data flow throughout the enterprise and between enterprises.
Also as companies increasingly invest in software as a service (SaaS) offerings it means that access to corporate data housed outside the enterprise is on the increase. Data in SaaS applications needs to be kept consistent with data inside the enterprise and brought back inside the enterprise to integrate with internal operational data if you have an internally hosted data warehouse.