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The strategy gap lavaraging thechnology to execute winning strategies - Goveney M.

Goveney M. The strategy gap lavaraging thechnology to execute winning strategies - Wiley & sons , 2003. - 242 p.
ISBN 0-471-21450-7
Download (direct link): thestrategygapleveraging2003.pdf
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To reduce maintenance time, CPM models also have an indicator of the current period. This indicator is used to determine the focus of any report. For example, if a report shows the current month and last month, setting the indicator to “May” will tell the model to automatically show results for May and April. Built-in time intelligence makes CPM models much easier to set up and helps organizations cope with the move toward continuous planning.
Data Model Technologies
The database technology used to implement a CPM data model can be multidimensional, relational, or a hybrid of both. Each has unique characteristics and needs to be chosen carefully to match the organization’s requirements. However, regardless of the technology selected,
Corporate Performance Management Systems
users should be allowed to choose, where appropriate, the way in which information is displayed down and across the screen or page.
Multidimensional Databases. Multidimensional databases were developed to overcome the limitations of relational databases. Relational technology initially was developed for IT departments to support transaction processing and record keeping. Due to the lack of calculation capabilities, there was little or no support for viewing data in a multidimensional way or for creating organizational models that reflected the different business dimensions. In contrast, multidimensional databases were designed specifically to ease the setting up of business models and to enable interactive multidimensional analysis.
In a multidimensional database, data is stored in “cubes” that combine the various business dimensions of an organization. Leading vendors in this space include Applix, with its TM1 database, and Hyperion, with Essbase. Interestingly, Oracle used to be the market leader with its product, Express, but Oracle recently dropped this product in favor of a relational approach.
The advantages of multidimensional databases are that they perform extremely well in complex data analysis and are relatively easy to set up and maintain. The disadvantages are that they are number based and need additional technologies to handle information in text and date form, which is essential for CPM solutions. They also lack standards, meaning that many applications featuring multidimensional databases are proprietary. At best, the organization has to learn a new technology to maintain or extend the application. At worst, it means the organization is forever at the mercy of the database vendor in providing updates and new functionality to allow the organization to retain a competitive advantage with its IT infrastructure.
Relational Databases. Relational databases have been around for over 30 years and are common in every organization. They underpin the general ledger, ERP, CRM, and HR systems and are used by IT departments to create customized systems. Vendors such as Microsoft with SQL Server, Oracle, and IBM with DB2 dominate the industry. Their products can be found in most organizations. Because of the prevalence of relational databases, standards have emerged that all vendors comply with in terms of updating records and providing access.
In a relational database, information is stored in relational tables rather than in cubes. Tables are two-dimensional in that they consist of records, and each record consists of fields or columns (see Exhibit 5.7).
The Strategy Gap
Exhibit 5.7 Relational database star schema.
Dimensional Table Dimensional Table
Dim1_Key Dim3_Key
Dim1_Name Dim3_Name
Dim1_Field2 Fact Table Dim3_Field2
Dim1_Field3 - Dim1_Key Dim3_Field3
- Dim2 Key
Dim1_FieldN Dim3_Key Dim3_FieldN
Dimensional Table Data Field1 Dimensional Table
Dim2 Key Data Field2 DimN_Key
Dim2_Name DimN_Name
Dim2_Field2 Data_FieldN DimN_Field2
Dim2 Field3 DimN_Field3
Dim2_FieldN DimN_FieldN

The number of fields is fixed for each table. A relational database can consist of many tables that can have different numbers of fields. Multidimensional analysis is achieved by creating a number of tables containing specific fields that are related. This type of design is known as a star schema or snowflake schema.
A star schema consists of a central fact table with a multipart key that holds data and results and a set of smaller tables called dimensional tables. Each dimensional table contains a key and dimensional members along with their attributes. For example, a location dimension table would contain a set of records defining specific locations. An individual dimension table is joined to the fact table through its key, which is also part of the multipart key in the fact table.
In the past, the disadvantage of the relational approach was poor performance due to the complex queries required to mimic the functionality of multidimensional cubes. However, use of star schemas combined with the dramatic performance improvements in relational technology has transformed the way these applications perform. For many organizations, the performance is comparable to multidimen-
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