Page 81 - DCAP208_Management Support Systems

Management Support Systems

Notes “We want to select, group, and manipulate data in every possible way!” Decision-making
processes cannot always be planned before the decisions are made. End users need a tool
that is user-friendly and flexible enough to conduct ad hoc analyses. They want to choose
which new correlations they need to search for in real time as they analyze the information
retrieved.
“Show me just what matters!” Examining data at the maximum level of detail is not only
useless for decision-making processes, but is also self-defeating, because it does not allow
users to focus their attention on meaningful information.
“Everyone knows that some data is wrong!” This is another sore point. An appreciable
percentage of transactional data is not correct—or it is unavailable. It is clear that you
cannot achieve good results if you base your analyses on incorrect or incomplete data.
We can use the previous list of problems and difficulties to extract a list of key words that
become distinguishing marks and essential requirements for a data warehouse process, a set of
tasks that allow us to turn operational data into decision-making support information:

accessibility to users not very familiar with IT and data structures;
integration of data on the basis of a standard enterprise model;
query flexibility to maximize the advantages obtained from the existing information;

information conciseness allowing for target-oriented and effective analyses;
multidimensional representation giving users an intuitive and manageable view of
information;

correctness and completeness of integrated data.

Did u know? Data warehouses are placed right in the middle of this process and act as
repositories for data. They make sure that the requirements set can be fulfilled.
Data warehouses are subject-oriented because they hinge on enterprise-specific concepts, such
as customers, products, sales, and orders. On the contrary, operational databases hinge on many
different enterprise-specific applications.
We put emphasis on integration and consistency because data warehouses take advantage of
multiple data sources, such as data extracted from production and then stored to enterprise
databases, or even data from a third party’s information systems. A data warehouse should
provide a unified view of all the data. Generally speaking, we can state that creating a data
warehouse system does not require that new information be added; rather, existing information
needs rearranging. This implicitly means that an information system should be previously
available.

Operational data usually covers a short period of time, because most transactions involve the
latest data. A data warehouse should enable analyses that instead cover a few years. For this
reason, data warehouses are regularly updated from operational data and keep on growing. If
data were visually represented, it might progress like so: A photograph of operational data
would be made at regular intervals. The sequence of photographs would be stored to a data
warehouse, and results would be shown in a movie that reveals the status of an enterprise from
its foundation until present.
Fundamentally, data is never deleted from data warehouses and updates are normally carried
out when data warehouses are off-line. This means that data warehouses can be essentially

74 LOVELY PROFESSIONAL UNIVERSITY