Fire, flood, earthquake and accidental deletion of data are all acts that can cause disastrous consequences on data. Such disasters can prevent the network from operating normally, which in turn can hamper the organization’s business. These disasters can be classified into man-made disasters and environmental disasters. Man-made disasters are intentionally or unintentionally caused by humans. For example, a user accidentally deletes the data, virus and malicious programs can damage data and various other events can cause data loss and downtime. Environmental disasters are non-preventive but can be reduced if appropriate precautions are taken. Environmental disasters include fire, flood, earthquake, tornado and hurricane. Disaster recovery deals with recovery of data that is damaged due to destructive activities. The time required to recover from a disaster depends on the disaster recovery plan implemented by the organization. A good disaster recovery plan can prevent an organization from any type of disruption. Disaster Recovery Continue reading
Business Information Systems
Electronic Data Interchange (EDI) Standards and Specifications
Electronic Data Interchange Standards Generally speaking, Electronic Data Interchange (EDI) is considered to be a technical representation of a business conversation between two entities, either internal or external. Note, there is a perception that “EDI” consists of the entire electronic data interchange paradigm, including the transmission, message flow, document format, and software used to interpret the documents. Electronic Data Interchange (EDI) is considered to describe the rigorously standardized format of electronic documents. The Electronic Data Interchange standards were designed to be independent of communication and software technologies. EDI can be transmitted using any methodology agreed to by the sender and recipient. This includes a variety of technologies, including modem (asynchronous, and bisynchronous), FTP, Email, HTTP, AS1, AS2, WebSphere MQ, etc. It is important to differentiate between the EDI documents and the methods for transmitting them. While comparing the bisynchronous protocol 2400 bit/s modems, CLEO devices, and value-added networks used to Continue reading
Information Systems in Various Levels of Modern Organizations
The INFORMATION SYSTEM plays a major role in the organization by satisfying the diverse needs through a variety of systems such as Query systems, Analysis systems, Modeling systems and Decision support systems. It helps the Clerical personnel in transaction processing and answers their queries on data pertaining to transaction. It helps junior management by providing operational data for planning and control, and helps them in Decision-making. It helps the Middle management in short-term planning, target setting and controlling business functions. It helps Top management in goal setting, planning and evolving business plans and their implementation. OPERATIONAL-LEVEL SYSTEMS At the operational level are transactions processing systems through which products are designed, marketed, produced, and delivered. These systems accumulate information in databases that form the foundation for higher-level systems. In today’s leading organizations, the information systems that support various functional units-marketing, finance, production, and human resources-are integrated into what is known as Continue reading
Data Processing Methods
1. Batch Processing. Batch processing is a technique in which data to be processed or programs to be executed are collected into groups to permit convenient, efficient, and serial processing. It is the simplest form of data processing. With this method, data is entered to the information flow in large volumes, or batches. That is, the processing by computer is performed periodically, at specified time intervals (weekly, monthly, etc) when large volumes are accumulated. Daily transactions in a business establishment, for example, may be batch processed on a weekly basis. Instead of being processed periodically when a sufficient volume has been accumulated. Advantages of batch processing are: Economical when a large volume of data must be processed and The most appropriate method for those applications (e.g., payroll) where the delay caused by accumulating data into batches does not reduce the value of the information. Limitations of batch processing are: It Continue reading
Case Study on MIS: Information System in Restaurant
Case Summary: A waiter takes an order at a table, and then enters it online via one of the six terminals located in the restaurant dining room. The order is routed to a printer in the appropriate preparation area: the cold item printer if it is a salad, the hot-item printer if it is a hot sandwich or the bar printer if it is a drink. A customer’s meal check-listing (bill) the items ordered and the respective prices are automatically generated. This ordering system eliminates the old three-carbon-copy guest check system as well as any problems caused by a waiter’s handwriting. When the kitchen runs out of a food item, the cooks send out an ‘out of stock’ message, which will be displayed on the dining room terminals when waiters try to order that item. This gives the waiters faster feedback, enabling them to give better service to the customers. Continue reading
Application of Big Data in Retail Industry
Information technology is evolving rapidly, and now we are living in the artificial intelligence (AI) age which is considered a smart society where Internet of Things (IoTs) connecting to intelligent devices. Thanks to the advances in technology, retail is among the industries that are most affected by digital transformation. With this transformation, the consumer is changing their shopping experience and behavior from seeking products or shops, comparing, reading reviews to in-store, online and finally writing reviews, contacting customer service. The growing demands of modern consumers for excellent shopping possibilities gives the modern retailer room to innovate and understand shopper’s behavior better in response to the need of modern consumers. Retailers are dealing with a quickly changing retail landscape and newer competitive threats due to the new and improved changes in technology. To address these changes, retailers are now using big data solutions to collect massive amounts of data to gather Continue reading