Differences between OLTP vs DSS systems

Information systems are classified into two major categories, according to international developments:

A. Online transactional processing systems (also called operational systems)

B. Decision support systems (DSS)

Online transactional processing systems

On-line transaction processing (OLTP) systems are those that handle transactions with suppliers, partners, and customers, as well as transactions within the company. They provide assistance for operations across the whole value chain of the organisation, including:

  • Supply Chain Management (SCM)
  • Production support (e.g. MRP, Advanced Planning & Scheduling)
  • Customer interface management (e.g. sales, order management and billing) (CRM)
  • Finance and Accounting (ERP)
  • Salesforce automation
  • Web channel operations (eCRM)
  • Internal workflow support systems

Decision support systems

Managers at all levels of the organisation may rely on DSS to give them information that will assist them in comprehending the current business situation and making educated choices (fact-based management).

OLTP systems against DSS systems It is clear that the purposes of OLTP (online transactional processing) and DSS (decision support systems) differ, despite the fact that some functionalities of the two categories overlap (for example, an OLTP system may provide some operational reporting functionality used for decision support), given that they serve different functions and different User groups in the Business.

Therefore the development philosophy of the two categories differs radically.

Specifically, differences are identified on the following criteria (1 for OLTP, 2 for DSS):

System functional requirements:

  1. Clearly specified given that the system serves specific functional needs – the predetermined transactions
  2. the determination of a complete requirement set is a challenge, given that there are dynamically changing informational requirements.

The capture of current and historical information:

  1. Current state information is captured (some historical data may exist only to serve potential future transactions)
  2. Recent and historical information is captured (current may not be captured, given that data from the OLTP are retrieved at regular intervals)

Data models used:

  1. Complex focused on business entities (in terms of relational databases it is called normalised data structure (e.g. 3NF))
  2. Different approaches exist. The simplified denormalised dimensional structure gains momentum since it allows easier understanding by business users and optimised execution of complex queries.

Information level of detail:

  1. Detailed data per transaction are kept
  2. Detailed data are kept in a different structure and are enriched by ‘dimensional’ information which allows analytical processing. Moreover, aggregated data, like KPIs (key performance indicators), are calculated and stored in persistent storage.

The volume of data:

  1. The volume of data is relevant to the size of the Business and the penetration of IT in it.
  2. The data volume handled by a DSS is multiple of that of the OLTP systems on which it is based, given that it maintains multiple historical snapshots.
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