AIS EXAM 1

Internal-level schema

-a low-level view of the database. It describes how the data are actually stored and accessed (record layouts, definitions, addresses, and indexes)

Control account

is general ledger account which corresponds to a subsidiary ledger

Posting Process

•Journals record all transaction details •Journals are used to update subsidiary ledgers •Summaries of journals are used to update the general ledger

Timing of Processing

-Batch processing -Real-time processing -Online batch processing

Business processes/Transaction cycle

-Revenue -Expenditure -Payroll -Production -Financing

Cloud options

- IAAS ( tAKE AND BAKE) -PAAS (Pizza delivered) -SAAS (Dine Out)

Batch processing

-Accumulating source documents into groups or batches for processing at a regular internal such as daily or weekly.

Blockchain Advantages

-Accuracy •Transparency •Data consistency •Trust •No need for third parties •Single set of books •Cost •Decentralization •Efficiency •Privacy •Security •Provenance

Data Query Language (DQL)

-Contains powerful, easy-to-use commands that enable users to retrieve, sort, order, and display data. Example - HR manager runs a report showing employees who are retiring within 5 yrs.

Journals

General journal Specialized Journals

Real-time processing

-Processing individual transactions as they occur and immediately updating all related files

Ledgers

General ledger Subsidiary ledger Control account

Online batch processing

-Transactions/source documents are entered as they occur but processed together at a later time

Group Code

-Used to represent items involving two or more pieces of related data using zones or fields that possess specific meaning •Example: -UH class codes -ACCT 3371-Vehicle id number -VIN #

Block code

-blocks of numbers within a numerical sequence are reserved for categories having meaning to the user Example:1000000-1999999 Electric range 2000000-2999999 Refrigerator 3000000-3999999 Washer 4000000-4999999 Dryer

Schema

-description of the data elements; the relationship among them; logical model used to organize and describe the data Three levels of schema -External -Conceptual -Internal

Foreign key

-field in a table that is a primary key in another table.

Primary key

-field, or combination of fields, that uniquely identifiesa specific row in a table.

Support Activities in the Value Chain

-firm infrastructure -human resource management -technology development -purchasing

Conceptual-level schema

-illustrates the different files and relationships between the files. Representation is independent of the actual physical implementation.

General journal

-infrequent non-routine trxs (loan pmts, adjusting and closing entries)

sequence code

-items are numbered consecutively to ensure that there will be no gaps in the sequence -check numbers, any prenumbered document

Database Approach

-programs and users share data -reduced data redundancy -improved data integrity -shared data -easier access -reduced development time

Specialized Journals

-records large number of repetitive trxs (many per cycle) Example: Revenue journals -sales order journal, shipping journal, accounts receivable (billing), cash receipts

External-level schema

-set of individual user views of portions of the database, each is a subschema. These become roles →Segregation of duties.

Advantages of Databases

-share data -constancy -integrity -control redundancy -processing time -single container

Steps in processing input

1. Capture transaction 2. Verify captured data are accurate and complete 3. Ensure company policies are followed (transaction approval)

Components of an AIS

1. PEOPLE who use the system. 2. PROCEDURES and instructions used to collect, process, and store data. 3. DATA about the organization and its business activities. 4. SOFTWARE used to process the data. 5. The information technology INFRASTRUCTURE including computers, peripheral devices, and network communication devices used to collect, store, process, and transmit data and information. 6. The internal CONTROLS and security measures that safeguard the data in the AIS.

Primary activities in the value chain

1. inbound logistics 2.operations 3.outbound logistics 4.marketing and sales 5.service

Steps in a Blockchain Transaction

1.Initiate transaction 2.Validate transaction 3.Create a block 4.Calculate and insert a hash 5.Complete transaction

A good data analytic question is

SMART Specific measurable achievable relevant timely

data dashboard

A set of visual displays that organizes and presents information that is used to monitor the performance of a company or organization in a manner that is easy to read, understand, and interpret.

Comparison

Bar Chart Bullet Chart

Data Definition Language (DDL)

Builds the data dictionary, creates the database, describes logical views, and specifies record or field security constraints. -example -Database administrator defines the logical structure of the database by adding a new table.

Data Manipulation Language (DML)

Changes database content, including data creation, update, insertion, and deletion example -Sales manager runs a program that updates all product prices by 10%.

Emphasis

Consider for Emphasis •Highlighting •Weighting •Ordering -color

Simplification

Consider for Simplification •Quantity •Distance •Orientation

DBMS Languages

Data Definition Language (DDL) Data Manipulation Language (DML) Data Query Language (DQL)

Understand the Data -Location

Data can be found throughout various systems. •Accounting •Operational •Customer relationship management •Sales

Ethical Data Presentation

Data deception -graphical depiction, designed with or without an intent to deceive, that creates a belief about the message which varies from the actual message. Avoid data deception by •Show representations of numbers proportional to the reported number •In viz designed to depict trends, time should progress from left to right on the x-axis •Present complete data given the context

data processing cycle

Data input ----> Data processing or data storage ----> Information output

Predictive Data Analytics

Definition: information that results from analyses that focus on predicting the future, answers the question, "what might happen in the future?" Examples: all forecasting analytics are examples of predictive analytics (sales forecasts, EPS forecasts, stock price forecasts, etc.).

Prescriptive Data Analytics

Definition: information that results from analyses to provide a recommendation of what should happen, answers the question "what should be done?" Examples: these are often models that provide a recommendation

Diagnostic Data Analytics

Definition: information that results from the examination of data to determine causal relationships, answers the question "why did this happen?" Examples: often statistical analyses such as using regression to see if one thing causes (or more often is associated with) another.

Descriptive Data Analytics

Definition: information that results from the examination of data to understand the past, answers the question "what happened?" Examples: common financial ratios such as earnings per share, inventory turnover ratios, profitability ratios, etc.

Rules for Relational Databases

Every column in a row must be single valued •Primary keys cannot be null .•Foreign keys, if not null, must have values that correspond to the value of a primary key in another table. (i.e.referential integrity) •All nonkey fields in a table should describe the primary key

ETL

Extract, Transform, Load

File approach

Files are created and only certain programs have access to their respective files

Diagnostic Analytics

Formal diagnostic analytics often employ confirmatory data analysis technique Testing involves 4 steps: 1.State a null and alternative hypothesis. 2.Select a level of significance for refuting the null hypothesis. 3.Collect a sample of data and compute the probability value. 4.Compare the computed probability against the level of significance and determine if the evidence refutes the null hypothesis. Failing to refute the hypothesis is seen as support for the alternative hypothesis.

Common Problems with Data Analytics

Garbage in, garbage out Data overfitting Extrapolation beyond the range of data Failing to consider the variation Relationships between variables change over time No human judgement

Distribution

Histogram Boxplot

Trend evalutation

Line chart Area Chart

Value Chain

Linking together of all the primary and support activities in a business. Value is added as a product passes through the chain.

Accounting Information Systems

Overall goal -produce information for decision makers Add value by -Improving the quality and reducing the costs of products or services -Improving efficiency -Sharing knowledge -Improving efficiency and effectiveness of its supply chain Improving the internal control structure -Improving decision making

Part to a whole

Pie chart Tree map

Type I error

Rejecting null hypothesis when it is true

Virtualization

Running multiple systems simultaneously on one physical computer (~high rise condo building)

Correlation

Scatterplot heatmap

input Documents

Source document -document used to capture transaction data at its source when the transaction takes place Turnaround document -document sent to an external party and then returned by the external party

Relational Database

Storing all data in one large table causes redundant data or the same piece of data being stored more than once.

Understand the Data -Structure

Structured-data is highly organized and fits into fixed fields -Data in most accounting systems, relational databases •Unstructured-data with no uniform structure -Images, social media posts, videos •Semi-structured-data organized in some way but not fully organized-CSV, logs, xml

Access Rights

There are four basic access rights that can be granted in a subschema: -Create (C), Read (R), Update (U), and Delete (D)

transforming Data

There are four steps in the data transformation process: •Understand the data and the desired outcome. •Standardize, structure, and clean the data.(most time-consuming) •Validate data quality and verify data meets data requirements. •Document the transformation process.

Extracting Data Steps

Three steps in the data extraction process •Understand data needs and the data available.-Location, accessibility and structure of the data •Perform the data extraction .•Verify the data extraction quality and document what you have done.

Descriptive Analytics

Uses exploratory data analysis techniques, an approach that explores data without testing formal models or hypotheses -Find mistakes in the data-Understand the structure of data -Check assumptions required by more formal statistical modeling techniques -Determine the size, direction, and strength of relationships between variables

Cloud computing

Using a browser to remotely access software, data storage, hardware and applications

Four V's of Big Data

Volume, Velocity, Variety, Veracity

Loading Data

When data has been extracted and transformed well, the data loading process is a relatively simple process. However, data loading often reveals errors in the transformation steps as the data is not accepted correctly.

Enterprise Resource Planning (ERP) Systems

a software system that integrates information from across the entire company, including finance, order fulfillment, manufacturing, and transportation, and then facilitates sharing of the data throughout the firm

Data

acts that are collected, recorded, stored, and processed by an information system (input) -Most useful when it is in machine-readable form

transaction

an agreement between two entities to exchange goods or services; any other event that can be measured in economic terms by an organization.

Coding techniques

are used to organize data in journals and ledgers -Sequence codes -Block code -Group codes -Mnemonic codes

Analytics Improve Decision Making

basic-Identify a problem or issue Intermediate-Collect data needed to solve the problem and make recommendations to management Advanced-Actionable insights can be integrated into the systems used to make decisions

Blockchain

chains blocks of data together using cryptography algorithms that create hashes. The hash of a prior block is included in the latest block, creating the chain.

Five main purposes for visualization are:

comparison, correlation, distribution, trend evaluation, part-to-whole

General Ledger

contains summary-level data for every asset, liability, equity, revenue, and expense account of the organization.

Information

data that have been organized and processed to provide meaning and improve the decision-making process (output)

Internet of things (IoT)

embedding sensors in a multitude of devices so they can connect to the Internet

Type II error

failing to reject a false null hypothesis

DBMS (Database Management System)

handles the link between the way data are physically stored and each user's logical view of the data.

logical view

how people conceptually organize, view, and understand the relationships among data items

data dictionary

is a "blueprint" of the structure of the database and includes data elements, field types, programs that use the data element, outputs, and so on.

Data Warehouses

is one or more very large databases containing detailed and summarized data for a number of years used for analysis rather than transaction processing. However, they are not the books and records of the company!

data Analytics

is the use of software and algorithms to find and solve problems and improve business performance

Database Keys

primary and foreign

Data mining

process of analyzing data repositories for new knowledge about the company's data and business processes.

OLAP(online analytical processing)

queries to find expected relationships in data

Subsidiary ledger

records all the detailed data for any general ledger account that has many individual subaccounts. (Generally, one per cycle)

Value of information

the benefit provided by information less the cost of producing it

physical view

the way data are physically arranged and stored in the computer system

Designing High-Quality Visualizations

three important design principles: -Simplification-refers to making a visualization easy to interpret and understand. -Emphasis-assuring the most important message is easily identifiable. -Ethical presentation -refers to avoiding the intentional or unintentional use of deceptive practices that can alter the user's understanding of the data being present

predictive analytics tools

three steps to creating a predictive analytic model -Select the target outcome. -Find and prepare the appropriate data .-Create and validate a model.

Mnemonic code

—letters and numbers interspersed to identify an item Example: Dry300W05 is low end (300), white (W) dryer (D R Y) made by Sears (05)

Blockchain Disadvantages

•Cost Loss of privacy and confidentiality •Susceptibility

Disadvantages of ERP Systems

•Costly •Significant amount of time to implement •Customizing or standardizing a business process •Complexity •User resistance (learning new things is sometimes hard for employees)

Data Processing Four types of processing (C R U D):

•Creating new records •Reading existing data •Updating previous record or data •Deleting data

Data Input

•Financial data(taxes, amount) •Nonfinancial data (date, location of sale [online, in-store], salesperson, register) Data collected about each business activity 1.Type of activity (sale of inventory) 2.Resources affected (cash and inventory) 3.People/entities participating (salesperson and customer)

Advantages of ERP Systems

•Integrated enterprise-wide single view of the organization's data •Data captured once •Greater visibility and monitoring capabilities for management •Improved data access control through security settings •Standardization of procedures and reports •Improved customer service •Increased productivity through automation

Coding Guidelines

•Need to allow for growth (four digits versus 3 digits) •Be simple to reduce costs •Be consistent with organization's structure •Be consistent with intended use

Prescriptive Analytics

•Recommended course of action •Programmed actions a system can take based on predictive analytics results.