MIS Chapter 6

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Author first name, author last name, author address, agent name and address, title of book, book ISBN, date of contract, amount of money, payment schedule, date contract ends. Other databases might be an author database (author names, address, and agent details), a book title database (title and ISBN of book), and financial database (payments made).

Data redundancy occurs when different divisions, functional areas, and groups in an organization independently collect the same piece of information. Because it is collected and maintained in so many different places, the same data item may have: 1. different meanings in different parts of the organization, 2. different names may be used for the same item, and 3. different descriptions for the same condition. In addition, the fields into which the data is gathered may have different field names, different attributes, or different constraints.

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Data accuracy when the new data is input, establishing a good data model, determining which data is important and anticipating what the possible uses for the data will be, beyond looking up contract information, technical difficulties linking this system to existing systems, new business processes for data input and handling, and contracts management, determining how end users will use the data, making data definitions consistent with other databases, what methods to use to cleanse the data.

A DBMS includes capabilities and tools for organizing, managing, and accessing the data in the database. Its most important capabilities and tools are data definition, data dictionary, and data manipulation language. The data definition capability enables a user to be able to specify the structure of the content of the database. This capability is used to create database tables and to define the characteristics of the fields in each table. The data dictionary is used to store definitions of data elements and their characteristics in the database. In large corporate databases, the data dictionary may capture additional information, such as usage; ownership; authorization; security; and the individuals, business functions, programs, and reports that use each data element. A data manipulation language, such as SQL, that is used to add, change, delete, and retrieve the data in the database. This language contains commands that permit end users and programming specialists to extract data from the database to satisfy information requests and develop applications.

The select operation creates a subset consisting of all records (rows) in the table that meets stated criteria. The join operation combines relational tables to provide the user with more information than is available in individual tables. The project operation creates a subset consisting of columns in a table, permitting the user to create new tables that contain only the information required.

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All types of data can be used for their business intelligence systems, including operational, historical, machine-generated, Web/social data, audio and video data, and external data. The large datasets can be collected in a Hadoop cluster and used by an analytic platform to support power user queries, data mining, OLAP, etc. A data warehouse can be used to house all data, including smaller data sets and operational data, and be used to support casual use, for queries, reports, and digital dashboards, as well as support the analytic platforms. Smaller data marts can be created from the data warehouse to enable faster querying and typical queries from casual users.

Sales databases could be used to make the supply chain more efficient and minimize warehousing and transportation costs. You can also use sales databases, as well as text mining and sentiment analysis, to determine what supplies are in demand by which customers and whether needs are different in different geographical areas. Business intelligence databases could be used to predict future trends in office supply needs, to help anticipate demand, and to determine the most efficient methods of transportation and delivery.

Product databases could be made available to customers for greater convenience and ordering online. Databases could be used to track customer preferences and to help anticipate customer desires. Sales databases could also help clients such as toy stores anticipate when they would need to re-supply, providing an additional service. Data mining, Web mining, and sentiment analysis of big data could help anticipate trends in sales or other factors to help determine new services and products to sell to clients.

Data mining is one of the data analysis tools that helps users make better business decisions and is one of the key tools of business intelligence. Data mining allows users to analyze large amounts of data and find hidden relationships between data that otherwise would not be discovered. For example, data mining might find that a customer that buys product X is ten times more likely to buy product Y than other customers. Data mining finds information such as: · Associations or occurrences that are linked to a single event. · Sequences, events that are linked over time. · Classification, patterns that describe the group to which an item belongs, found by examining existing items that have been classified and by inferring a set of rules. · Clusters, unclassified but related groups. I would advise a company to use data mining when they are looking for new products and services, or when they are looking for new marketing techniques or new markets. Data mining might also be helpful when trying to analyze unanticipated problems with sales whose causes are difficult to identify.

Data mining uncovers hidden relationships and is used when you are trying to discover data and new relationships. It is used to answer questions such as: Are there any product sales that are related in time to other product sales? In contrast, OLAP is used to analyze multiple dimensions of data and is used to find answers to complex, but known, questions, such as: What were sales of a product—broken down by month and geographical region, and how did those sales compare to sales forecasts?

A data warehouse stores current and historical data of potential interest throughout a company. Data warehouses gather data from multiple operational systems inside the organization. Data warehouses make data available, but do not allow that information to be altered. Data marts are subsets of data warehouses, in which a highly focused portion of an organization's data is placed in a separate database for specific users. Data marts are decentralized, whereas data warehouses are enterprise-wide, central locations for data.

The term big data is used to describe datasets with volumes so huge that they are beyond the ability of typical DBMS to capture, store, and analyze. Big data is created by the explosion of data coming from the Web, such as Web traffic, e-mail, Twitter, and Facebook, as well as information from other electronic and networked devices such as sensors and meters. Businesses are interested in big data because it contains more patterns and interesting anomalies than smaller data sets, with the potential to provide new insights into customer behavior, weather patterns, financial market activity, or other phenomena. However, to derive business value from big data, organizations need new technologies and tools capable of managing and analyzing non-traditional data along with their traditional enterprise data. They also need to know what questions to ask of the data and the limitations of big data. Capturing, storing, and analyzing big data can be expensive, and information from big data may not necessarily help decision-makers.

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Data redundancy and inconsistency can occur because of (1) employing different names and descriptions for the same entities or attributes; (2) multiple systems feeding a data warehouse; (3) incorrect data entry

The first step is to perform a data quality audit, a survey of the accuracy and level of completeness in all the firm's major databases. Once issues are identified, initiate a program for data cleansing to correct data that is incomplete, improperly formatted, redundant, or just plain wrong.

An information policy specifies the organization's rules for sharing, disseminating, acquiring, standardizing, classifying, and inventorying information. Information policy lays out specific procedures and accountabilities, identifying which users and organizational units can share information, where information can be distributed, and who is responsible for updating and maintaining the information. An information policy is needed in firms because data are an important resource, and you don't want people doing whatever they want with them. You need to have rules on how the data are to be organized and maintained and who is allowed to view the data or change them.