What are Information Systems


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  • This slide could be used to pick up some ethical aspects of automation, especially the possible effects on individuals and communities when jobs are automated. I would not, however, recommend the over-simplification that displacing jobs is always a bad thing. There is little evidence that IT has reduced employment overall. Rather, it has restructured the employment market, replaced some jobs with others, and changed the nature of others (some for better and others for worse).
  • Summarizing (aggregation) selects, joins and groups data in order to provide precalculated measures and trends for direct access by the end users. Packaging transforms the operational and summarized data into more useful formats, such as graphs, charts, spreadsheets, animations. Partitioning uses technical means and the user profiling to reduce the amount of data that the system needs to scan in search for the answers to queries. Like a data warehouse, a data mart is a special-purpose database dedicated to analytical processing. Unlike a data warehouse, a data mart holds only a subset of enterprise data relevant to a particular department or business function. Also, a data mart tends to hold mostly summarized historical data and leaves detailed operational data on original storage sources. A new emerging trend is towards data webhouses. A data webhouse is defined as “a distributed data warehouse that is implemented over the Web with no central data repository” (Connolly and Begg, 2005, p.1152). A data webhouse provides a natural answer to the difficulty associated with the extraction, cleansing and loading of large volumes of potentially inconsistent data from multiple data sources into single data storage. A data webhouse offers an alternative technology for analytical processing on enterprise Intranets. Its potential use on the Internet is naturally restricted by the confines of the privacy and security of data as the most guarded strategic asset of any enterprise. Outside of these confines, data webhouses can offer very useful analytical analysis of data associated with the behavior of Internet users (so called clickstreams of data).
  • Association (path analysis) – finding patterns in data where one event leads to another related event (e.g. predicting which people who rent a property are likely to move out of rental and buy a property in a near future). Classification – finding if certain facts fall into predefined interesting categories (e.g. predicting which customers are likely to be less loyal and may change to another mobile phone provider). Clustering – similar to classification, but the categories are not previously known and are discovered by a clustering algorithm, not specified by the human analyst (e.g. predicting response to a telemarketing initiative). Like with the OLAP systems, the main source of data for data mining is a data warehouse, rather than operational databases. Data mining extends the OLAP capabilities to reach to the demands of the strategic management. It offers predictive rather than retrospective models. It uses Artificial Intelligence (AI) techniques to discover trends, correlations, and patterns in data. It attempts to find hidden and unexpected knowledge rather than the knowledge that is anticipated (as the hidden and unexpected knowledge has more strategic value for decision making).
  • In this lecture you have learned about …
  • What are Information Systems

    1. 1. Information Systems Concepts What Are Information Systems? Dell Zhang Birkbeck, University of London Based on Chapter 1 of Bennett, McRobb and Farmer: Object Oriented Systems Analysis and Design Using UML, (3rd Edition), McGraw Hill, 2005.
    2. 2. Outline <ul><li>Information and Information Systems </li></ul><ul><ul><li>Section 1.4 (pp. 15 – 19) </li></ul></ul>
    3. 3. IS – Types <ul><li>Operational Systems assist or control business operations </li></ul><ul><li>Management Support Systems help managers to decide or to communicate </li></ul><ul><li>Office Systems automate or assist in the work of office workers, such as clerks, secretaries, typists and receptionists. </li></ul><ul><li>Real- T ime Control Systems typically operate physical equipment, often in safety-critical settings </li></ul>such as …….
    4. 4. IS – Management Levels <ul><li>O perational Systems </li></ul><ul><ul><li>Operational </li></ul></ul><ul><li>Management Support Systems </li></ul><ul><ul><li>T actical </li></ul></ul><ul><ul><li>S trategic </li></ul></ul>--- Maciaszek, L.A. : Requirements Analysis and System Design , ( 3 rd ed ) . Addison Wesley , 2007
    5. 5. --- Maciaszek, L.A. : Requirements Analysis and System Design , ( 3 rd ed ) . Addison Wesley , 2007 Level of decision making Focus of decision making Typical IS applications Typical IT solutions Pivotal concept Operational (operative management level) Day-to-day staff activities and production support Payroll, Invoicing, Purchasing, Accounting Database, Transactional processing, Application generators Data Tactical (line management level) Policies in support of short-term goals and resource allocation Budget analysis, Salary forecasting, Inventory scheduling, Customer service Data warehouse, Analytical processing, Spreadsheets Information Strategic (executive / senior management level) Strategies in support of organizational long-term objectives Market and sales analysis, Product planning, Performance evaluation Data mining, Knowledge management Knowledge
    6. 6. Data, Information and Knowledge <ul><li>Data </li></ul><ul><ul><li>raw facts representing values, quantities, concepts and events pertaining to business activities </li></ul></ul><ul><li>Information </li></ul><ul><ul><li>data that have been processed and summarized to produce value-added facts, revealing features and trends </li></ul></ul><ul><li>Knowledge </li></ul><ul><ul><li>understanding of information, obtained by experience or study, and resulting in the ability to do things effectively and efficiently. </li></ul></ul><ul><ul><ul><li>tacit: in a person’s mind </li></ul></ul></ul><ul><ul><ul><li>documented: in some structured form </li></ul></ul></ul>--- Maciaszek, L.A. : Requirements Analysis and System Design , ( 3 rd ed ) . Addison Wesley , 2007
    7. 7. Data, Information and Knowledge <ul><li>Data </li></ul><ul><ul><li>e.g., telephone numbers </li></ul></ul><ul><li>Information </li></ul><ul><ul><li>e.g, telephone numbers grouped by their areas, industries etc. </li></ul></ul><ul><li>Knowledge </li></ul><ul><ul><li>e.g., how the telephone numbers can be used in telemarketing to entice people to buy products </li></ul></ul>--- Maciaszek, L.A. : Requirements Analysis and System Design , ( 3 rd ed ) . Addison Wesley , 2007
    8. 8. IS – Operational <ul><li>OnLine Transaction Processing (OLTP) systems </li></ul><ul><ul><li>Transaction – a logical unit of work that accomplishes a particular business task and guarantees the integrity of the database after the task completes </li></ul></ul><ul><ul><li>Database technology </li></ul></ul><ul><ul><ul><li>Concurrency control </li></ul></ul></ul><ul><ul><ul><li>Recovery </li></ul></ul></ul><ul><ul><ul><li>Business logic (vs application/control logic) </li></ul></ul></ul><ul><ul><ul><li>Security </li></ul></ul></ul>
    9. 9. IS – Tactical <ul><li>OnLine Analytical Processing (OLAP) systems </li></ul><ul><ul><li>Analysis of pre-existing historical data to facilitate decision making </li></ul></ul><ul><ul><li>Data warehouse technology </li></ul></ul><ul><ul><ul><li>Summarizing </li></ul></ul></ul><ul><ul><ul><li>Packaging </li></ul></ul></ul><ul><ul><ul><li>Partitioning </li></ul></ul></ul><ul><ul><li>Data marts </li></ul></ul><ul><ul><li>Data webhouse </li></ul></ul>
    10. 10. IS – Strategic <ul><li>Knowledge Processing Systems </li></ul><ul><ul><li>“ Know-how” – intellectual capital accumulated through experience </li></ul></ul><ul><ul><li>Knowledge M anagement – to help organizations discover, organize, distribute and apply the knowledge encoded in information systems </li></ul></ul><ul><ul><ul><li>Data mining </li></ul></ul></ul><ul><ul><ul><ul><li>Association </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Classification </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Clustering </li></ul></ul></ul></ul><ul><ul><ul><li>AI techniques  predictive rather than retrospective models </li></ul></ul></ul>
    11. 11. Take Home Messages <ul><li>Different Types of Information Systems </li></ul><ul><ul><li>3 Management Levels </li></ul></ul><ul><li>Data, Information and Knowledge </li></ul>