Accounting Information Systems

1. Data and Databases Accounting Information Systems

2. Study Objectives • The need for data collection and storage • Methods of storing data and the interrelationship between storage and processing • The differences between batch processing and real-time processing • The importance of databases and the historical progression from flat-file databases to relational databases • The need for normalization of data in a relational database • Data warehouse and the use of a data warehouse to analyze data • The use of OLAP and data mining as analysis tools • Distributed databases and advantages of the use of distributed data • Controls for Data and Databases • Ethical issues related to data collection and storage, and their use in IT systems

3. The Need for Data Collection and Storage Dataare the set of facts collected from transactions, whereas informationis the interpretation of data that have been processed. • Main reasons to store transaction data: • To complete transactions from beginning to end. • To follow up with customers or vendors and to expedite future transactions. • To create accounting reports and financial statements. • To provide feedback to management. SO 1 The need for data collection and storage

4. The Need for Data Collection and Storage • Typical storage and processing techniques: • The storage media types for data: sequential and random access • Methods of processing data: batch and real time • Databases and relational databases • Data warehouses, data mining, and OLAP • Distributed data processing and distributed databases SO 1 The need for data collection and storage

5. The Need for Data Collection and Storage Concept Check Which of the following best describes the relationship between data and information? • a. Data is interpreted information. • b. Information is interpreted data. • c. Data is more useful than information in decision making. • d. Data and information are not related. SO 1 The need for data collection and storage

6. Storing and Accessing Data Data Storage Terminology Exhibit 13-1 Data Hierarchy • Character • Field • Record • File • Database SO 2 Methods of storing data and the interrelationship between storage and processing

7. Storing and Accessing Data Data Storage Media • Magnetic tape • Sequential access • Early Days of Mainframe Computers • Modern IT Systems • Random Access SO 2 Methods of storing data and the interrelationship between storage and processing

8. Storing and Accessing Data Concept Check A character is to a field as • a. Water is to a pool. • b. A pool is to a swimmer. • c. A pool is to water. • d. A glass is to water. SO 2 Methods of storing data and the interrelationship between storage and processing

9. Storing and Accessing Data Concept Check Magnetic tape is a form of • a. Direct access media. • b. Random access media. • c. Sequential access media. • d. Alphabetical access media. SO 2 Methods of storing data and the interrelationship between storage and processing

10. Data Processing Techniques • Real-time Processing • Batch Processing Exhibit 13-2 Comparison of Batch and Real-Time Processing SO 3 The differences between batch processing and real-time processing

11. Data Processing Techniques Concept Check Which of the following is not an advantage of using real-time data processing? • Quick response time to support timely record keeping and customer satisfaction • b. Efficiency for use with large volumes of data. • c. Provides for random access of data. • d. Improved accuracy due to the immediate recording of transactions. SO 3 The differences between batch processing and real-time processing

12. Databases Data stored in a form that allows the data to be easily accessed, retrieved, manipulated, and stored. Exhibit 13-3 Traditional File-Oriented Approach • Data redundancy • Concurrency SO 4 The importance of databases and the historical progression from flat-file databases to relational databases

13. Databases Exhibit 13-3 Database Approach Relationships • One-to-One • One-to-Many • Many-to-Many Database Management System (DBMS) is software that manages the database and controls the access and use of data by individual users and applications. SO 4 The importance of databases and the historical progression from flat-file databases to relational databases

14. The History of Databases Exhibit 13-4 Database Table Flat File Database Model • 1950s and 1960s • Text format, sequential order • Sequential processing • Large volumes of similar transactions • Single record not easily retrieved or stored SO 4 The importance of databases and the historical progression from flat-file databases to relational databases

15. The History of Databases Hierarchical Database Model • Inverted tree structure • Parent–child, represent one-to-many relationships • Record pointer Exhibit 13-5 Linkages in a Hierarchical Database SO 4 The importance of databases and the historical progression from flat-file databases to relational databases

16. The History of Databases Network Database Model • Inverted tree structure • More complex relationship linkages by use of shared branches • Not very popular, rarely used SO 4 The importance of databases and the historical progression from flat-file databases to relational databases

17. The History of Databases Relational Database Model • Developed in 1969 • Stores data in two-dimensional tables • Most widely used database structure today • Examples include; IBM DB2, Oracle Database, and Microsoft Access SO 4 The importance of databases and the historical progression from flat-file databases to relational databases

18. Databases Concept Check If a company stores data in separate files in its different departmental locations and is able to update all files simultaneously, it would not have problems with • a. attributes. • b. data redundancy. • c. industrial espionage. • d. concurrency. SO 4 The importance of databases and the historical progression from flat-file databases to relational databases

19. Databases Concept Check When the data contained in a database are stored in large, two-dimensional tables, the database is referred to as a • a. flat file database. • b. hierarchical database. • c. network database. • d. relational database. SO 4 The importance of databases and the historical progression from flat-file databases to relational databases

20. Databases Concept Check Database management systems are categorized by the data structures they support. In which type of database management system is the data arranged in a series of tables? • a. Network. • b. Hierarchical. • c. Relational. • d. Sequential. SO 4 The importance of databases and the historical progression from flat-file databases to relational databases

21. The Need for Normalized Data Relational databases consist of several small tables. Small tables can be joined in ways that represent relationships among the data. Exhibit 13-6 Relational Database in Microsoft Access Bolded field is the primary key. SO 5 The need for normalization of data in a relational database

22. The Need for Normalized Data Relational database has flexibility in retrieving data. Structured query language (SQL) has become the industry standard. Exhibit 13-7 Relational Database in Microsoft Access SELECT Customers.CustomerID, Customers.CompanyName, Orders.OrderID, Orders.ShippedDate FROM Customers INNER JOIN Orders ON Customers.CustomerID Orders.CustomerID; SO 5 The need for normalization of data in a relational database

23. The Need for Normalized Data • The process of converting data into tables that meet the definition of a relational database is called data normalization. • Seven rules of data normalization, additive. • Most relational databases are in third normal form. • First three rules of data normalization are: • Eliminate repeating groups • Eliminate redundant data • Eliminate columns not dependent on primary key. SO 5 The need for normalization of data in a relational database

24. The Need for Normalized Data Trade-offs in Database Storage Relational database • Not most efficient way to store data that will be used in other ways. • Most organizations are willing to accept less transaction processing efficiency for better query opportunities. SO 5 The need for normalization of data in a relational database

25. The Need for Normalized Data Concept Check Which of the following statements is not true with regard to a relational database? • a. It is flexible and useful for unplanned, ad hoc queries. • b. It stores data in tables. • c. It stores data in a tree formation. • d. It is maintained on direct access devices. SO 5 The need for normalization of data in a relational database

26. Use of a Data Warehouse to Analyze Data Management often needs data from several fiscal periods from across the whole organization. Exhibit 13-8 The Data Warehouse and Operational Databases SO 6 Data warehouse and the use of a data warehouse to analyze data

27. Use of a Data Warehouse to Analyze Data • Management often needs data from several fiscal periods from across the whole organization. • Build the data warehouse • Identify the data • Standardize the data • Cleanse, or scrub, the data • Upload the data SO 6 Data warehouse and the use of a data warehouse to analyze data

28. Use of a Data Warehouse to Analyze Data Concept Check A collection of several years’ nonvolatile data used to support strategic decision-making is a(n) • a. operational database. • b. data warehouse. • c. data mine. • d. what-if simulation. SO 6 Data warehouse and the use of a data warehouse to analyze data

29. Data Analysis Tools Data miningis the process of searching for identifiable patterns in data that can be used to predict future behavior. OLAPis a set of software tools that allow online analysis of the data within a data warehouse. Analytical methods in OLAP usually include: • Time series analysis • Exception reports • What-if simulations • Drill down • Consolidation • Pivoting SO 7 The use of OLAP and data mining as analysis tools

30. Data Analysis Tools Concept Check Data mining would be useful in all of the following situations except • a. identifying hidden patterns in customers’ buying habits. • b. assessing customer reactions to new products. • c. determining customers’ behavior patterns. • d. determining customers’ behavior patterns. SO 7 The use of OLAP and data mining as analysis tools

31. Distributed Data Processing • Early days • Centralized processing • Centralized databases • Today’s IT Environment • Distributed data processing (DDP) • Distributed databases (DDB) SO 8 Distributed databases and advantages of the use of distributed data

32. Distributed Data Processing • Distributing the processing and data offers the following advantages: • Reduced hardware cost • Improved responsiveness • Easier incremental growth • Increased user control and user involvement • Automatic integrated backup The most popular type of distributed system is a client/server system. SO 8 Distributed databases and advantages of the use of distributed data

33. Distributed Data Processing Concept Check A set of small databases where data are collected, processed, and stored on multiple computers within a network is a • a. Centralized database. • b. Distributed database. • c. Flat file database. • d. High-impact process. SO 8 Distributed databases and advantages of the use of distributed data

34. IT Controls for Data and Databases • To ensure integrity (completeness and accuracy) of data in the database, IT application controls should be used. These controls are • input, • processing, and • output controls such as • data validation, • control totals and reconciliation, and • reports that are analyzed by managers. SO 9 Controls for data and databases

35. Ethical Issues Related to Data Collection Ethical Responsibilities of the Company Data collected and stored in databases in many instances consist of information that is private between the company and its customer. Ten privacy practices for online companies: • Management • Notice • Choice and consent • Collection • Use and retention • Access • Disclosure to third parties • Security for privacy • Quality • Monitoring and enforcement SO 10 Ethical issues related to data collection and storage, and their use in IT systems

36. Ethical Issues Related to Data Collection Ethical Responsibilities of Employees Employees have an ethical obligation to avoid misuse of any private or personal data about customers. There are no specific IT controls that would always prevent authorized employees from disclosing private information. SO 10 Ethical issues related to data collection and storage, and their use in IT systems

37. Ethical Issues Related to Data Collection Ethical Responsibilities of Customers • Customers have an obligation to • provide accurate and complete information. • keep any known company information confidential. • avoid improper use of data that they gain from accessing a database as a customer. SO 10 Ethical issues related to data collection and storage, and their use in IT systems

38. Ethical Issues Related to Data Collection Concept Check Each of the following is an online privacy practice recommended by the AICPA Trust Principles Privacy Framework except: • a. Redundant data should be eliminated from the database. • b. Notification of privacy policies should be given to customers. • Private information should not be given to third parties without the customer’s consent • d. All of the above. SO 10 Ethical issues related to data collection and storage, and their use in IT systems