information. These external data can be integrated into customer database and be
utilized as guiding or planning values.
Challenges with customer-oriented data warehouses:
1) When implementing a customer-oriented perspective, enterprises aim to align their
activities with customer parameters that are derived from the collected customer
data. These data are generated by sales over multiple sales channels (e.g. direct and
indirect sales) and by contacts over multiple customer touch points (e.g. phone, fax,
mail, e-mail). Thus, the data are scattered in different databases and applications
within the enterprise, such as call centre, databases of marketing, sales and service,
accounting and ERP systems. An implication is that, in most cases, this partial
information is not integrated and therefore highly inconsistent. If these data are not
carefully processed before loading into the data warehouse, then the quality of the
data warehouse is put at risk. A serious consequence is that the enterprise lacks a
“single view” on their customer, i.e. an integrated and consistent set of customer
2) To be able to analyse and understand their behaviour, customers ideally have to be
tracked over their complete lifecycles, from acquisition to retirement. But this would
require enterprises to collect and store all customer related transaction data on the
highest possible detail level, with the effect that the amount of analysable data is
predestined to grow exponentially. The sheer data volume has negative effects on
the performance and the response time of the data warehouse.
2) Why do some say that snowflake schemas a bad idea? Why do some say that they are not?
In your opinion, should snowflake schemas be used? Justify your answer.
//Snowflake schema isn’t good:-Multiple joins, speed, understandability of the model is
complexity of the data model because more tables, more joins….
When a hierarchical relationship in a dimension table is normalized, low-cardinality attributes
appear as secondary tables connected to the base dimension table by an attribute key. When
this process is repeated with all the dimension table’s hierarchies, a characteristic multilevel
structure is created that is called a snowﬂake. Although the snowﬂake represents hierarchical
data accurately, you should avoid snowﬂakes because it is difficult for business users to
understand and navigate snowﬂakes. They can also negatively impact query performance. A
ﬂattened denormalized dimension table contains exactly the same information as a snow-