IT management system

IT management system
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Methods and techniques

There are currently many different IT based management systems. You can distinguish here: Management Information Systems (MIS), Decision Support Systems (DSS), Executive Information Systems (EIS), Executive Support Systems (ESS) or expert systems (Expert Systems - ES), and knowledge-based systems. In addition, you can also distinguish office information systems (Office Information Systems - OIS, or Office Automation Systems - OAS). Characterizing the IT systems described above, one should refer to their features of importance to the management of various levels. One can distinguish here the following special features:

  • type - form - information resources,
  • used models and procedures,
  • technical measures,
  • types of supported decisions.


Classification

The following types of IT management systems can be distinguished: Due to the area of ​​application:

  • production management systems,
  • material resources management systems,
  • logistics management and distribution systems,
  • systems for managing intangible assets,
  • financial management systems

Due to the generation of the system:

  • transaction systems (registration and reporting),
  • information and decision systems,
  • decision support systems (consultancy, expert).

Due to the management level:

  • operational management systems,
  • tactical management systems,
  • strategic management systems.

Due to the level of complexity:

  • simple systems (single-row and / or single-functional),
  • multi-bank and / or multifunctional systems,
  • comprehensive systems.

Due to the degree of integration:

  • autonomous systems,
  • partially integrated systems,
  • integrated systems.

Due to the universality:

  • systems based on computer tool packages,
  • individual systems,
  • post-payment systems,
  • typical and standard systems

Data Warehouse

The term introduced in the eighties, which can be defined as a collection of all enterprise-wide databases remaining invariably in the data warehouse. It aims to support decisions and obtain the necessary information. Split data is cleaned up and shared according to specific criteria. Data warehouses in the organization are created to:

  • Quick acquisition of information by data search (data-drilling), you can distinguish data-down deep operations, data-merging, cross-sectional analysis of slicing & dicing
  • obtaining knowledge of data-mining data, in this case uses neural network models, genetic algorithms, statistical techniques

Information stored in the warehouse can be divided into:

  • factual information (facts) - these are specific events in the enterprise regarding business operations inside or outside. Facts are analyzed, therefore they are presented by numbers, e.g. number of returns, value of sales
  • reference information (descriptions) - these are descriptions of categories according to which the actual data is analyzed, express aspects of the company's operations, such as the client, geographical region or human resources, and show where the actual data should be added
  • aggregate information (fact aggregates) - data for a longer period of time are stored here, eg number of failures per quarter
  • metadata - they are used to describe data, their meaning, indicate their location, method of obtaining, processing and using

Types of data warehouse:

  • integrated - satisfying all decision makers
  • thematic mini-stores - satisfying specific information needs (in the field of, for example, finance, logistics)
  • central data warehouse (EDW - Entreprise Data Warehouse) - used to supplement mini warehouses, does not perform direct analyzes
  • departmental warehouses (DDW - Departamental Data Warehouse) - each branch has its own multi-thematic data warehouse
  • central warehouse (CDW - Corporate Data Warehouse) - data warehouse, which has data from the entire enterprise, is the main source of archival data

References