Data Warehousing

Datawarehousing

Data you can trust

At DIKW Intelligence, a distinction is made between Data Warehousing on the one hand and Business Intelligence on the other. Where Data Warehousing ends, Business Intelligence starts there. Since DIKW Intelligence does not want to have a discussion about where exactly Data Warehousing ends (because the scholars still disagree about this), we state the following:

DIKW defines Data Warehousing as the necessary data logistic component to make Business Intelligence possible.

In general, a data warehouse is periodically filled with fresh data from operational systems such as ERP systems. The information in the data warehouse is usually not current and the system is therefore not used for operational decisions. The data in a data warehouse is often used for a strategic trend analysis, for example an analysis of all sales figures over the past period (s).

In contrast, data warehouses that are refreshed (near) real time are becoming increasingly popular. Certainly if the so-called Operational Intelligence must be supported. These types of data warehouses are often based on a Data Vault structure, a structure that can be refreshed both in batch and in real time.

DIKW is able to realize a new generation of data warehouses that are refreshed both in real time and in batches. These new systems are also "designed for change", allowing them to evolve with the wishes of your organization. This evolving is necessary because, unlike the current information needs, the future information needs cannot be predicted. To be somewhat future-proof, an adaptive intelligence system is a necessity.

With these evolving data logistic components, DIKW Intelligence no longer talks about data warehouses, but about organization memory (Enterprise Memory).

At DIKW Intelligence, a distinction is made between Data Warehousing on the one hand and Business Intelligence on the other. Where Data Warehousing ends, Business Intelligence starts there. Since DIKW Intelligence does not want to have a discussion about where exactly Data Warehousing ends (because the scholars still disagree about this), we state the following:

DIKW defines Data Warehousing as the necessary data logistic component to make Business Intelligence possible.

In general, a data warehouse is periodically filled with fresh data from operational systems such as ERP systems. The information in the data warehouse is usually not current and the system is therefore not used for operational decisions. The data in a data warehouse is often used for a strategic trend analysis, for example an analysis of all sales figures over the past period (s).

In contrast, data warehouses that are refreshed (near) real time are becoming increasingly popular. Certainly if the so-called Operational Intelligence must be supported. These types of data warehouses are often based on a Data Vault structure, a structure that can be refreshed both in batch and in real time.

DIKW is able to realize a new generation of data warehouses that are refreshed both in real time and in batches. These new systems are also "designed for change", allowing them to evolve with the wishes of your organization. This evolving is necessary because, unlike the current information needs, the future information needs cannot be predicted. To be somewhat future-proof, an adaptive intelligence system is a necessity.

With these evolving data logistic components, DIKW Intelligence no longer talks about data warehouses, but about organization memory (Enterprise Memory).