Page 107 - DCAP208_Management Support Systems

Management Support Systems

Notes subject of the analysis, but BI can also encompass analysis of current and future states. Along
with OLAP, other data management techniques that fall into the realm of BI include data mining,
reporting, operational performance management, and predictive analytics.
Online Analytical Processing is used to answer the complex queries posted on data warehouse.
In order to solve the queries of nature ‘who?’ and ‘what?’ We can use the simple tools but to
answer the advanced queries like ‘what if?’ and ‘why?’, we require special tool that can support
online analytical processing (OLAP).

OLAP is a term that describes a technology that uses a multi-dimensional view of aggregate data
to provide quick access to strategic information for the purposes of advanced analysis. OLAP
enables users to gain a deeper understanding and knowledge about various aspects of their
corporate data through fast, consistent, interactive access to a wide variety of possible views of
the data.
OLAP enables decision-making about future actions. Atypical OLAP calculation can be more
complex than simply aggregating data, for example, ‘What would be the effect on property sales
in the different regions of Punjab if legal costs went up by 3.5% and Government taxes went
down by 1.5% for properties over ` 100,000?’.

Analytical Queries per Minute (AQM) is used as a standard benchmark for comparison of
performances of different OLAP tools. OLAP systems should as much possible hide users from
the syntax of complex queries and provide consistent response times for all queries no matter
how complex.
Online analytical processing is frequently used for ad hoc reporting, and typically generates
reports in a pivot or matrix format. Departments that may make use of OLAP include finance,
operations, sales, and marketing. Types of uses can include budgeting and forecasting.

One of the defining characteristics of online analytical processing is the OLAP cube. The concept
of the cube correlates the elements known as measures and dimensions, which describe the
various measures’ metadata. A relational database’s snowflake or star schema tables may be the
source of the metadata.
An example of a cube is using a business’ individual accounts receivable amount as a measure,
with a due date as a dimension.
OLAP uses databases that are designed with multiple dimensions. These databases may be
smaller than those needed for the data warehousing capabilities that are often used for business
intelligence. Compared to other types of analysis, fewer details of transaction are usually needed
in online analytical processing. Not only are the OLAP databases often smaller than data
warehouses, accessing the OLAP databases is often faster than accessing relational databases.
There are various specialties of online transaction processing. Several of the more frequently
used specialities include multidimensional, relational, and hybrid. Multidimensional OLAP
stores data in multidimensional arrays, relational OLAP uses relational databases, and hybrid
OLAP uses a combination of the relational and specialized tables.
Though online transactional processing is an important technique in BI, more sophisticated
tools or improvements to OLAP may be required for organizations that are interested in
predictive analysis and business analytics. Predictive analysis is frequently used to forecast
events such as customer buying behavior. Business performance data is usually the target of
business analytics.

7.2.1 Historical Background

The first fully functional online analytical system was introduced in 1970 by Express and later on
in 1995 the Oracle acquired the release for the resource of information in 2007 the official

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