Rick van der Lans

Organizations store more and more data in ever-larger volumes. However, most of that data is not new or original, but copied. Companies excel at duplicating data. For example, information about a customer is stored in a CRM system, a staging area, a data warehouse, several data marts, and a data lake. Even within one database, data is stored multiple times to support different users. In addition, copies of data are stored in development and test environments. There is also data redundancy between organizations when exchanging data. Usually, the receiving organization stores the data in its own systems, resulting in even more copies.

This unrestrained duplication of data has many disadvantages and challenges, including higher data latency, complex data synchronization solutions, more complex data security and privacy enforcement, higher development and maintenance costs, higher technology costs, and more complex database and metadata administration. Additionally, all this data copying involves processing and storage which costs energy.

In this session, Rick van der Lans explains how you can design data-on-demand architectures in which data copies and unnecessary processing are minimized. The technology is available for developing such architectures. The extra benefit is that data-on-demand architectures are also greener architectures.