ect production of reports, this approach does not assure:

• a sufficient level of detail in the facts captured
• the ability to perform time series analysis (no historical data available)
• the option to evaluate data in a differentiated view, by ‘highlighting’ certain dimensions of the business process and hiding others
• the integrity, clarity and accuracy of facts and the single version of truth
• the ability to produce complex reports, without overloading the operational system.

The set of KPIs and reports, is expected to be enriched as new informational needs are identified and new reporting capability is devA simple analysis of the functionality of a decision support system, is presented. In an effort to analyze the performance of a Business, and develop strategy for the future positioning in a competitive environment, the dimension of time is crucial (this is why all data warehouse systems have a time dimension and maintain historical data). In this article series, we describe three functionality categories of decision support systems:part 1. Detailed capture of performancepart 2. Performance analysis part 3. Modeling and prediction Categories are presented in an increasing complexity and business value sequence. They do not necessarily represent maturity levels, since each category is developed and matured as it is enriched with new capabilities and adopted to the dynamically changing informational and analytical Business needs. DSS systems may be developed for other purposes, apart from performance management. A very common example is the development of a customer intelligence system, in which the aim is to build customer insight in order to adopt products and services accordingly.

1. Detailed capture of business performance A basic management principle is the following: ‘you cannot manage what you cannot quantify and measure’. Within this framework, each Business aims to develop infrastructures which shall enable it, to capture detailed measurements of the current business process performance and gradually build detailed historical performance data. ‘How successful the Business was’ for a given time interval, is captured. Business performance is captured in a detailed mode for all ‘dimensions’ involved (indicatively): Business processes and activities, Products marketed, Customer touch points employed, Geography, Customer segments, Business Units involved. Data analysis that takes place at the ‘business performance capture’ level, is usually predefined. Predefined Key performance indicators (KPIs) are regularly calculated and presented in business intelligence dashboards. Detailed facts are produced in a multidimensional data structure (also called data mart), like:

• Detailed financial facts (revenue, expenses)
• Detailed production facts (e.g. production activity performance)

• Customer facts (e.g. number of contacts, number of complaints)
• Customer churn data
• Product quality facts
• New product introduction facts,

and presented in reports. A static performance capture takes place.

Even though many on-line transaction processing (OLTP) systems enable the direct production of reports, this approach does not assure:

• a sufficient level of detail in the facts captured
• the ability to perform time series analysis (no historical data available)
• the option to evaluate data in a differentiated view, by ‘highlighting’ certain dimensions of the business process and hiding others
• the integrity, clarity and accuracy of facts and the single version of truth
• the ability to produce complex reports, without overloading the operational system.

The set of KPIs and reports, is expected to be enriched as new informational needs are identified and new reporting capability is deve. They do not necessarily represent maturity levels, since each category is developed and matured as it is enriched with new capabilities and adopted to the dynamically changing informational and analytical Business needs. DSS systems may be developed for other purposes, apart from performance management. A very common example is the development of a customer intelligence system, in which the aim is to build customer insight in order to adopt products and services accordingly.

1. Detailed capture of business performance A basic management principle is the following: ‘you cannot manage what you cannot quantify and measure’. Within this framework, each Business aims to develop infrastructures which shall enable it, to capture detailed measurements of the current business process performance and gradually build detailed historical performance data. ‘How successful the Business was’ for a given time interval, is captured. Business performance is captured in a detailed mode for all ‘dimensions’ involved (indicatively): Business processes and activities, Products marketed, Customer touch points employed, Geography, Customer segments, Business Units involved. Data analysis that takes place at the ‘business performance capture’ level, is usually predefined. Predefined Key performance indicators (KPIs) are regularly calculated and presented in business intelligence dashboards. Detailed facts are produced in a multidimensional data structure (also called data mart), like:

• Detailed financial facts (revenue, expenses)
• Detailed production facts (e.g. production activity performance)

• Customer facts (e.g. number of contacts, number of complaints)
• Customer churn data
• Product quality facts
• New product introduction facts,

and presented in reports. A static performance capture takes place.

Even though many on-line transaction processing (OLTP) systems enable the direct production of reports, this approach does not assure:

• a sufficient level of detail in the facts captured
• the ability to perform time series analysis (no historical data available)
• the option to evaluate data in a differentiated view, by ‘highlighting’ certain dimensions of the business process and hiding others
• the integrity, clarity and accuracy of facts and the single version of truth
• the ability to produce complex reports, without overloading the operational system.

The set of KPIs and reports, is expected to be enriched as new informational needs are identified and new reporting capability is dev’. Within this framework, each Business aims to develop infrastructures which shall enable it, to capture detailed measurements of the current business process performance and gradually build detailed historical performance data. ‘How successful the Business was’ for a given time interval, is captured. Business performance is captured in a detailed mode for all ‘dimensions’ involved (indicatively): Business processes and activities, Products marketed, Customer touch points employed, Geography, Customer segments, Business Units involved. Data analysis that takes place at the ‘business performance capture’ level, is usually predefined. Predefined Key performance indicators (KPIs) are regularly calculated and presented in business intelligence dashboards. Detailed facts are produced in a multidimensional data structure (also called data mart), like:

• Detailed financial facts (revenue, expenses)
• Detailed production facts (e.g. production activity performance)

• Customer facts (e.g. number of contacts, number of complaints)
• Customer churn data
• Product quality facts
• New product introduction facts,

and presented in reports. A static performance capture takes place.

Even though many on-line transaction processing (OLTP) systems enable the direct production of reports, this approach does not assure:

• a sufficient level of detail in the facts captured
• the ability to perform time series analysis (no historical data available)
• the option to evaluate data in a differentiated view, by ‘highlighting’ certain dimensions of the business process and hiding others
• the integrity, clarity and accuracy of facts and the single version of truth
• the ability to produce complex reports, without overloading the operational system.

The set of KPIs and reports, is expected to be enriched as new informational needs are identified and new reporting capability is devformance indicators (KPIs) are regularly calculated and presented in business intelligence dashboards. Detailed facts are produced in a multidimensional data structure (also called data mart), like:

• Detailed financial facts (revenue, expenses)
• Detailed production facts (e.g. production activity performance)

• Customer facts (e.g. number of contacts, number of complaints)
• Customer churn data
• Product quality facts
• New product introduction facts,

and presented in reports. A static performance capture takes place.

Even though many on-line transaction processing (OLTP) systems enable the direct production of reports, this approach does not assure:

• a sufficient level of detail in the facts captured
• the ability to perform time series analysis (no historical data available)
• the option to evaluate data in a differentiated view, by ‘highlighting’ certain dimensions of the business process and hiding others
• the integrity, clarity and accuracy of facts and the single version of truth
• the ability to produce complex reports, without overloading the operational system.

The set of KPIs and reports, is expected to be enriched as new informational needs are identified and new reporting capability is devect production of reports, this approach does not assure:

• a sufficient level of detail in the facts captured
• the ability to perform time series analysis (no historical data available)
• the option to evaluate data in a differentiated view, by ‘highlighting’ certain dimensions of the business process and hiding others
• the integrity, clarity and accuracy of facts and the single version of truth
• the ability to produce complex reports, without overloading the operational system.

The set of KPIs and reports, is expected to be enriched as new informational needs are identified and new reporting capability is developed (either based on the operational systems, or on external data sources).

The development of a DSS system should follow an iterative method. The data model implemented should allow the gradual expansion of the DSS.

Copyright 2006 – Kostis Panayotakis