|Business Analysis with
|The Right Information, In
the Right Place, At The Right Time, for the right person
OLAP - on-line analytical processing - is the
foundation for a range of essential business applications, including sales and
marketing analysis, planning, budgeting, statutory consolidation, profitability
analysis, balanced scorecard, performance measurement and data warehouse
reporting. Although OLAP is neither a new nor an obscure concept, it is still
not widely understood.
In a business environment characterized by fierce
competition and rapid change, you need to make decisions quickly and on the
basis of reliable facts and figures. But what if that information is
incomplete? Or simply unavailable? What happens if executives and knowledge
workers are left stranded in an ivory tower, unable to obtain the answers to
pressing questions on customers, products, profitability and potential new
sources of revenue?
The information you need is somewhere within your
organization, but it is often locked away in operational applications, and
difficult to obtain. And when you do get hold of the statistics you need, it is
often not in the form you require, and you have little scope to explore that
information in more detail to identify the precise cause of a sudden surge in
costs or the reasons for the unexpected popularity of a particular product in a
The recognized technical concept for meeting this challenge
is OLAP. OLAP is a separate environment with a dedicated database drawing on
diverse data sources and designed to support queries and analysis.
first wave of data warehouses has encountered a number of difficulties,
including technical integration and laborious and lengthy implementation. One
major obstacle has been gearing the warehouse to the business processes of the
Online analytical processing (OLAP) is an increasingly
popular technology that can dramatically improve business analysis, but that
has been characterized historically by expensive tools, difficult
implementation, and inflexible deployment. Many companies have tackled the OLAP
problem and created a solution that makes multi-dimensional analysis accessible
to a broader audience, and potentially, at a significantly lower cost of
In an OLAP data model, information is conceptually viewed as
cubes, which consist of descriptive categories (dimensions) and quantitative
values (measures). The multi-dimensional data model makes it simple for users
to formulate complex queries, arrange data on a report, and switch from summary
to detail data, and filter or slice data into meaningful subsets. For example,
typical dimensions in a cube containing sales information would include time,
geography, product, channel, organization, and scenario (budget or actual).
Typical measures would include dollar sales, unit sales, inventory, headcount,
income, and expense.
Within each dimension of an OLAP data model, data can be
organized into a hierarchy that represents levels of detail on the data. For
example, within the time dimension, you may have the levels years, months, and
days; similarly, within the geography dimension, you may have the levels
country, region, state/province, and city. A particular instance of the OLAP
data model would have the specific values for each level in the hierarchy. A
user viewing OLAP data will move up or down between levels to see more or less
Many organizations know that they need OLAP-based solutions,
but those tasked to select and implement them may be new to the area, or may
have lost track of its rapid developments. Selecting the right OLAP product is
hard, but very important, if projects are not to fail; many buyers struggle
even to produce an appropriate shortlist. Now the most widely used specialist
OLAP resource worldwide.
The need of end users to analyze corporate data for the
purpose of making better decisions is of paramount importance. Fast, consistent
response to end-user requests is critical to interactive, ad-hoc exploration,
comparison and analysis of data, regardless of database size and complexity.
End users must be able to manipulate and derive data for analysis purposes by
applying analytical operations such as ratios, cumulative totals, trends and
allocations across dimensions and across hierarchical levels. OLAP technologies
are essential to delivering this end-user value and are a critical component of
broader information technology architecture.