Accounting Information Systems Flashcards - Exam 1

Access restricted

Able to limit access to authorized parties

Available

Available to users when needed in a format that can easily and quickly used

Complete

Does not omit aspects of events or activities.

Concise

Clear and to the point; free of unnecessary detail.

Consistent

Presented in the same format over time

Current

Includes event and activity data ip to the present

Objective

Unbiased

Relevant

Reduces uncertainty and improves decision making

Reputable

Perceived as true and credible to to a trusted source

Timely

Provided in time for decision makers to make informed choices.

Understandable

Presented in a useful and intelligible format

Usable

Easy to use for different tasks by humans or machines

Verifiable

Same information produced by two independent knowledgable people

AIS components

1. People

2. Procedures

3. Data

4. Software

5. IT

6. Internal controls

Value of an AIS

Improves…

1. Quality

2. Efficiency

3. Internal controls

4. Decision making

5. Sharing Knowledge

Factors influencing an AIS

Organizational structure

Business strategy

IT

Value Chain

Inbound Logistics

Operations

Outbound Logistics

1. Firm Infrastructure (Administrative Roles)

2. HR

3. Technology

4. Purchasing

Marketing and Sales

Service

Data input

Data input → Data Processing → Data Storage → Information Output

Turnaround Documents

output documents generated by the company & sent to an external party

General Ledger

summary level data for every asset, liability, and equity

Subsidiary Ledger

detailed data for any general ledger account with many individual sub-accounts

Control account

General Ledger account corresponding to a subsidiary ledger

sequence codes

items are numbered consecutively for accounts of all items

block code

blocks of numbers are reserved for categories of data

group codes

subgroups of digits used to code items

mnemonic coding

letter and numbers are interspersed to identify an item

Chart of accounts

list of numbers assigned to each general ledger account

General journal

used to record infrequent or nonroutine transactions

Specialized journal

records repetitive transactions

Audit trail

traceable path of a transaction through a data processing system

Entity

something about which information is stored

Attributes

characteristics of interest that are stored

Fields

data about entity attributes

Record

input within a field

Data Value

content within a record

File

group of related records

Master file

stores cumulative information about an organization

Transaction file

records of individual business interactions that occur during a specific time

Database

set of interrelated, centrally coordinated files

CRUD acronym for data

1. Create

2. Read

3. Update

4. Delete

Batch processing

Updating done periodically

Real time processing

Updates each transaction as it occurs

Documents

records of transaction or other company data

ERP

integrates all aspects of a company’s operations with a traditional AIS

Database: several entities about which the company wants to store data about

several entities about which the company wants to store data about

Database management system

software program that manages the data.

Database system

combo of the data, database, dbms, and applications

Online transaction processing database

used to process normal business transactions.

Data warehouse

one or more very large databases containing detailed and summarized data for analytical processing.

Advantages of Database systems

• Data integration

• Data sharing

• Minimal data redundancy and data inconsistencies

• Data independence

• Cross functional analysis

Logical View

How people conceptually organize and understand among data items

Physical View

The way data is physically arranged and stored in the computer system

Record layout

documents the structure of the data items stored in a file

Schema View

description of the data elements in a database, relationships among them, and the logical model used to organize them

external level

user’s logical view of their portion

subschema

each user’s logical view of their database

conceptual-level schema

org-wide view of the entire database

internal level schema

low level view of the database

access rights

portions of the database the user needs to perform their duties

data dictionary

information about the structure of the database

normalization

assuming everything is stored in one large table and then decomposed into smaller ones

semantic data modeling

designer uses the knowledge of business processes and information needs to create a diagram that shows what to include in the database

Relational Database Management System Requirements

• Every column must be single valued

• Entity Integrity Rule: Primary keys cannot be null

• Foreign keys if not null must have values the respond to the value of a primary key in another table

• All non-key attributes in a table must describe a characteristic of the object identified by the primary key

relational data model

conceptual and external-level schemas as if data are stored in 2-D tables

primary key

database attribute that uniquely identifies a specific row

foreign key

an attribute in one table that is a primary key in another table and used to link 2 tables.

Four V’s of Data

• volume: amount of data created and stored

• variety: different forms data can take

• veracity: quality of data

• velocity speed at which data is created and stored

SMART questions

1. Specific

2. Measurable

3. Achievable

4. Relevant

5. Timely

Data Extraction Requirements

• understand the data needs and available data

• performing the data extraction

• verify the data extraction quality and what was done

Structured data

highly organized and fit into fixed fields

Unstructured data

no uniform structure and include items as images, vid, etc.

Semi-structured data

organized to some extent but not enough to be inserted into a RDMS

Data marts

smaller data repositories that hold structured data

Data lake

collection of structured, semi-structured, and unstructured data in a single place

Dark data

data that an organization stores but isn’t analyzed and is ignored

Data swamp

data lakes that aren’t accurately documented and cannot be used

data about the data dictionary

data about the data dictionary

Flat file

text file that consolidates data from multiple tables into a single row

Delimiter

a character that separates one field from another

Text qualifier

two characters that indicate the start and end of a field

Transforming Data Requirements

• Understand the data and the desired outcome

• Standardize, structure, and clean the data

• Validate data quality and verify that data meets the data requirements

• Document the transformation process

Load the data

in the name

Descriptive Analytics

asks what happened.

Diagnostic Analytics

asks why did this happen.

  Prescriptive Analytics

asks what should be done.

Predictive Analytics

answer the question “why might this happen in the future”

Data storytelling

process of translating complex data into simpler terms

Data visualization

use of graphs to show data

Data dashboard

collection of key metrics and data points

What graph is used for Comparison?

Bar Chart or Bullet Chart

What graph is used for Correlation?

Scatter Plot or Heatmaps

What graph is used for Distribution?

Histogram or Box Plot

What graph is used for Trend Evaluation?

Line Chart or Area Chart

What graph is used for Part-to-Whole?

Pie Chart or Treemap