Mining Your Own Business in Telecoms Using DB2 Intelligent Miner for Data

Chapter 2. Business Intelligence architecture overview 17

2.3.1 Reporting and query

Creating reports is a traditional way of distributing information in an organization.

Reporting is typically static figures and tables that are produced and distributed

with regular time intervals or for a specific request. Using an automatic reporting

tool is an efficient way of distributing the information in your data warehouse

through the Web or e-mails to the large number of users, internal or external to

your company, that will benefit from information.

Users that require the ability to create their own reports on the fly or wish to

elaborate on the data in existing reports will use a combined querying and

reporting tool. By allowing business users to design their own reports and

queries, a big workload from an analysis department can be removed and

valuable information can become accessible to a large number of (non-technical)

employees and customers resulting in business benefit for your company. In

contrast to traditional reporting this also allows your business users to always

have access to up-to-date information about your business. This thereby also

enables them to provide quick answers to customer questions.

As the reports are based on the data in your data warehouse they supply a 360

degree view of your company's interaction with its customers by combining data

from multiple data sources. An example of this is the review of a client’s history

by combining data from: ordering, shipping, invoicing, payment, and support

history.

Query and reporting tools are typically based on data in relational databases and

are not optimized to deliver the “speed of thought” answers to complex queries

on large amounts of data that is required by advanced analysts. An OLAP tool

will allow this functionality at the cost of increased load time and management

effort.

2.3.2 On-Line Analytical Processing (OLAP)

During the last ten years, a significant percentage of corporate data has migrated

to relational databases. Relational databases have been used heavily in the

areas of operations and control, with a particular emphasis on transaction

processing (for example, manufacturing process control, brokerage trading). To

be successful in this arena, relational database vendors place a premium on the

highly efficient execution of a large number of small transactions and near fault

tolerant availability of data.

18 Mining Your Own Business in Telecoms Using DB2 Intelligent Miner for Data

More recently, relational database vendors have also sold their databases as

tools for building data warehouses. A data warehouse stores tactical information

that answers “who?” and “what?” questions about past events. A typical query

submitted to a data warehouse is: “What was the total revenue for the eastern

region in the third quarter?”

It is important to distinguish between the capabilities of a data warehouse from

those of an On-Line Analytical Processing (OLAP) system. In contrast to a data

warehouse — that is usually based on relational technology — OLAP uses a

multidimensional view of aggregate data to provide quick access to strategic

information for further analysis.

OLAP enables analysts, managers, and executives to gain insight into data

through fast, consistent, interactive access to a wide variety of possible views of

information. OLAP transforms raw data so that it reflects the real dimensionality

of the enterprise as understood by the user.

While OLAP systems have the ability to answer “who?” and “what?” questions, it

is their ability to answer “what if?” and “why?” that sets them apart from data

warehouses. OLAP enables decision making about future actions.

A typical OLAP calculation is more complex than simply summing data, for

example: “What would be the effect on soft drink costs to distributors if syrup

prices went up by $.10/gallon and transportation costs went down by $.05/mile?”

OLAP and data warehouses are complementary. A data warehouse stores and

manages data. OLAP transforms data warehouse data into strategic information.

OLAP ranges from basic navigation and browsing (often known as “slice” and

“dice”) to calculations, to more serious analyses, such as time series and

complex modeling. As decision makers exercise more advanced OLAP

capabilities, they move from data access to information to knowledge.

Who uses OLAP and why?

OLAP applications span a variety of organizational functions. Finance

departments use OLAP for applications, such as budgeting, activity-based

costing (allocations), financial performance analysis, and financial modeling.

Sales analysis and forecasting are two of the OLAP applications found in sales

departments. Among other applications, marketing departments use OLAP for

market research analysis, sales forecasting, promotions analysis, customer

analysis, and market/customer segmentation. Typical manufacturing OLAP

applications include production planning and defect analysis.