Data. Entities. Accounting Information Systems. Chapter 4: Data Management

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1 Accounting Information Systems Chapter 4: Data Management Data Data may be defined broadly to include two interrelated components: Data Models that provide structure to data File Orientation Data-base Orientation Data values A firm s data resource involves four major functions: Record & Repository Creation Repository Maintenance through additions and updates Data Retrieval Data Archival and Removal Entities An Entity is an object, person, or event about which a firm wants to collect and maintain data Characteristics of Entities are Attributes Each attribute stored in the system is a Data Element There is usually a one-to-one correspondence between attributes and data elements A broadly defined attribute may have several specific attributes and therefore data elements. e.g., Shipping Address Street Address City State Zip Code Country 1

2 Data Models Files File Record Data Element Data-base Data-bases Data-sets (or Tables) Record Data Element Figure 4-1 Data Elements Every recorded attribute of an entity is a data element Field Length: This is the number of contiguous positions required to store a data element Data Type: Character Numeric Date Raw Data Value Some Specifics File{ Field 1 Field 2 Field 3 Field 1 Field 2 Field 3 Records Field 1 Field 2 Field 3 2

3 File Classifications (Master Files) Master files: These contain (semi) permanent data (records) pertaining to entities (people, places, and things). Accounting related examples include: General and Subsidiary ledgers General ledger master file Customer/Accounts Receivable master file Vendor/Accounts Payable master file Inventory master file Employee/Payroll master file Open WIP master file Standard cost master file File Classification (Transaction Files - I) Transaction files: These contain records pertaining to events currently being processed, such as sales, receipts of goods, etc. Transaction files capture detailed transaction data. They are counterparts to general and special journals in manual systems Transaction data are periodically posted to related master file(s) and are then either purged or archived File Classification (Transaction Files - II) Accounting related transaction files include: General/Special journal file (General ledger) Sales/Cash receipts file (Accounts receivable) Receiving/Purchases file, Cash disbursements file (Inventory, Accounts payable) Inventory issuance file/shipment file/sales file/adjustments file (Inventory) Payroll/Cash disbursements (Payroll) 3

4 Other File Classifications - I Reference files: These contain tables or lists of data needed for making calculations or for checking the accuracy of input data. e.g., product price tables, customer lists, etc. History files: These are also called archive files since they contain records pertaining to completed transactions such as past sales Open files: These record incomplete transactions. Whereas transaction files are purged or archived at the end of a given period, open files remain indefinitely open. Only individual records from Open files get purged as the transaction actually occurs or does not. e.g., Open sales order file Sales transaction file. Other File Classifications - II Report files: These are derived from records within master or transaction files. e.g., data may be periodically extracted from the Accounts Receivable master file to construct an aging schedule. Backup file: This is a copy of a current file generated so that the original file can be recreated from it. Suspense file: This is a collection of those records of a transaction file that appear to contain erroneous or questionable data Record-Key Record keys: These are data elements within records that serve as sort keys. e.g., customeraccount number. Two types of keys often used in master and transaction file records are a primary key and one or more secondary keys. A Primary key (also called a record key) is the attribute that uniquely identifies a specific record. They are usually of numeric or alphanumeric modes, e.g., customer number. A Secondary key is an attribute other than the primary key and represents an alternative way to sort or access records in a file, e.g., customer last name. 4

5 Logical View Versus Physical Storage of Records - I File structure pertains either to logical file structures or to physical file structures The logical file structure defines the user s perspective of a file. For example, each logical record in a computerized customer master file pertains to a particular customer Data contained in logical records must necessarily be physically mapped onto storage media File organization refers to the methods by which data in logical records are stored on physical storage media Logical View Versus Physical Storage of Records - II Files stored on physical media are seen as a collection of physical records Sometimes there may be a one-to-one correspondence between logical and physical records (unusual) Sometimes a logical record may occupy more than one physical record - typically on magnetic disks Sometimes two or more logical records may occupy one physical record - typically on magnetic tape Design Considerations for Records & Files - I Managing files and records requires the answering of questions such as: How should the records be structured What type of file organization and access method (e.g., sequential, indexed sequential, random) should be used How long should records be retained 5

6 Design Considerations for Records & Files - II The answers to the above questions also depend in part on the requirements of the application being designed These requirements, in turn, are affected by issues such as: storage requirements efficiency in file maintenance accessibility of stored data Establishing Record Structures - I The structure of a record is defined by its: content arrangement modes of data fields lengths of data fields keys Generally the primary keys are placed to occupy the first fields of the records Generally balance amounts or amounts of transactions are placed in the last fields Establishing Record Structures - II Transaction records are usually arranged somewhat in accordance with the placement of the elements on the source documents (e.g., sales invoices) The modes and lengths of the fields depend on the nature of data placed therein, while the keys are expressed as codes An important design issue is the extent to which records should be consolidated. This issue is especially important in relational database normalizations and table designs 6

7 File-Oriented Approach to Data Storage In the file-oriented approach to data storage computer applications maintain their own set of files This traditional approach focuses on individual applications, each of which have a limited number of users, who view the data as being owned by them Deficiencies of the File- Oriented Approach Files and data elements used in more than one application must be duplicated, which results in data redundancy As a result of redundancy, the characteristics of data elements and their values are likely to be inconsistent Outputs usually consist of preprogrammed reports instead of ad-hoc queries provided upon request. This results in inaccessibility of data Changes to current file-oriented applications cannot be made easily, nor can new developments be quickly realized, which results in inflexibility It is difficult to represent complex objects using file processing systems. The Database Approach to Data Storage A database is a set of computer files that minimizes data redundancy and is accessed by one or more application programs for data processing The database approach to data storage applies whenever a database is established to serve two or more applications, organizational units, or types of users A database management system (DBMS) is a computer program that enables users to create, modify, and utilize database information efficiently 7

8 Documenting Data in Data- Base Systems The Conceptual Data Model is the logical grouping of data on entities Two common Conceptual Data Modeling techniques are: The Data Dictionary Entity-Relationship Diagrams Data Dictionary A data dictionary is a computer file that maintains descriptive information about the items in a database Each computer record of the data dictionary contains information about a single data item used in an AIS Examples of information that might be stored in a data dictionary are source document(s) used to create the data item, programs that update the data item and classification information about the item s length and data type Data Modeling Via the Entity- Relationship Diagram - I The Entity-Relationship Model is a high level conceptual data model that specifies the data base structure independent of any specific DBMS (hierarchical, network, relational, objectoriented) It is only after completing the E-R model that a particular DBMS is selected. Then the high level model is mapped into schemas using the DDL provided by a given DBMS 8

Entity-Relationship Diagram - II In order to arrive at a specific E-R model, one must select the entities first, and then define the relationship between them (cardinalities: one-to-one, one-to-many,

9 Entity-Relationship Diagram - II In order to arrive at a specific E-R model, one must select the entities first, and then define the relationship between them (cardinalities: one-to-one, one-to-many, many-to-many) Rectangle=Entity Diamond=Relationship Line=Links: attribute to entity entity to relationship attribute to relationship Sometimes we use ellipses to represent specific attributes of entities, e.g., customer_#, student_last_name, etc. To go from the ER model to a specific conceptual data model (hierarchical, network, relational, object-oriented), we typically assign attributes to the entities and relationships so as to obtain fully specified pointers (hierarchical & network), and normalized tables (relational) Advantages of the Database Approach Efficient use of computerized storage space Each subsystem has access to the other s information All application programs utilize the same computer file, thereby simplifying operations Fewer backup files for security purposes Relieves some users from data-gathering responsibilities in situations where these users previously gathered their own data Disadvantages of the Database Approach Databases can be expensive to implement because of hardware and software costs. Additional software, storage, and network resources must be used A DBMS can only run in certain operating environments,which makes some unsuitable for certain alternate hardware/operating system configurations Because it is radically different from the fileoriented approach, the database approach may cause initial inertia, or complications and resistance 9

10 Data-Flow Diagrams A data-flow diagram shows the physical and logical flows of data through a transaction processing system without regard to the time period when each occurs Physical devices that transform data are not used in the logical diagrams Because of the simplified focus, only four symbols are needed Symbols used in Data Flow Diagrams A square represents an external data source or data destination. The latter is also called a sink A circle (or bubble) indicates an entity or a process that changes or transforms data A bubble can either be an internal entity in a physical DFD or a process in a logical DFD An open-ended rectangle or a set of parallel lines represents a store or repository of data The file may represent a view or a portion of a larger entity-wide data base A line with an arrow indicates the direction of the flow of data Physical DFDs A Physical DFD documents the physical structure of an existing system. It answers questions such as Where an entity works, How an entity works, the work is done by Whom, etc. Given the very physical focus of a physical DFD, it changes whenever the entities, technology used to implement the system, etc. changes Physical DFDs have no lower levels This limitation makes physical DFDs cumbersome to work with, and usually of limited value 10

Logical DFDs - I Logical Data flow diagrams are usually drawn in levels that include increasing amounts of detail A top level (or high-level) DFD that provides an overall picture of an application or

11 Logical DFDs - I Logical Data flow diagrams are usually drawn in levels that include increasing amounts of detail A top level (or high-level) DFD that provides an overall picture of an application or system is called a context diagram A context diagram is then decomposed, or broken down, into successively lower levels of detail Logical DFDs - II Logical Data flow diagrams document the processes in an existing or proposed system (What tasks) Because the logic of a system changes infrequently, relative to its physical nature, a logical DFD will remain relatively constant over time Logical Data flow diagrams typically have levels below the level-0 diagram The Hierarchy of Data-Flow Diagrams The Hierarchy of Data Flow Diagrams Context Diagram Physical DFD No lower levels Level-0 logical DFD Lower levels possible Level 1 diagram(s) Level 2 diagrams(s), etc. 11

12 A Context Diagram Customer Payment Process bubble Cash Receipts Process Relevant Environment comprised of External Entities }Boundary (border between a system and its environment) Dataflows (Interfaces) This is a flow connecting a system with its environment Deposit Bank A Physical DFD Customer Cash Sales information Blue sales book Sales Clerk 1.0 Form 66W Book- Keeper 3.0 Order & register tape Verified register tape 1. Bubbles are labeled with nouns 2. Data flows & files have physical descriptions Cashier 2.0 Deposit slip & cash Bank A Logical DFD 1.0 CustomerPayment Receive Payment Sales data Sales Journal Sales record 4.0 Record Sale Receipts & descriptions receipts summary 2.0 Compare Cash & Tape Verified receipts summary 1) Bubbles are labeled with verbs that describe the activity taking place 2) Data flows & files have logical Verified receipts 3.0 Prepare Deposit Deposit Bank 12

13 Thanks. 13

Chapter 4: Data Management

Accounting Information Systems: Essential Concepts and Applications Fourth Edition by Wilkinson, Cerullo, Raval, and Wong-On-Wing Chapter 4: Data Management Slides Authored by Somnath Bhattacharya, Ph.D.