INFS1602

1489 WordsSep 29, 20136 Pages
Strategic Business Objectives Operational Excellence New products, services and business models Customer and Supplier Intimacy Improved Decision Making Competitive Advantage Survival Value Chain Model Primary Activities Inbound Logistics (warehousing systems) Operations (machining systems) Sales and Marketing (electronic ordering) Service (equipment maintenance) Outbound Logistics (automated shipment scheduling) Support Activities Admin/Management (messaging/scheduling) Infrastructure (hardware/software used by primaries) HR (workforce planning) Technology development Procurement (electronic ordering from suppliers) IS can be used for Synergising companies together for lower operating costs (tying together…show more content…
Master data - data most important in the operation of the business Data can be stored in data warehouses. However, they need data cleansing to standardise the data. Data mart - categorisation of data from data warehouses customised for a specific group. Stages in decision making: Intelligence->design->choice->implementation 2 types of business intelligence system: Data mining: find for more hidden relationship through without any ideas about the existence of those relationship and data that they do not collect it before. OLNP: multidimensional analysing, summarising data to get information from existing data they can collect Trend of BI: Social media: new platform for intelligence, help business identify customer’s view about performance and customer relationship of business in the market. Cloud service: minimize cost of storage of data, minimize time of processing and summarizing data. Visualization(important): (real time updating) easier for business to do and understand the data, easier to identify potential and most influential customers. Business Intelligence (BI) components Information and knowledge discovery (Collects current data) Ad hoc queries and reports Online Analytical Processing (OLAP) (multidimensional) (hidden relationships, complements OLAP) Association discovery Clustering (attribute based) and classification (pre-knowledge segmentation) Unstructured data analysis Web content mining (web crawler

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