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BUS 319 > Midterm > Flashcards

Midterm Flashcards

(80 cards)

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1
Q

data processing cycle

A

4 operations performed on data to generate meaningful and relevant information: data input, data storage, data processing, and information output

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2
Q

source documents

A

documents used to capture transaction data

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3
Q

data capture

A

the first step in data input

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4
Q

general (specialized) ledger

A

summary level data on accounts

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5
Q

subsidiary ledger

A

records detailed data for general ledger accounts with individual subaccounts

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6
Q

control account

A

title given to the general ledger account that summarizes the amounts recorded in subsidiary ledger

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7
Q

audit trail

A

traceable path of a transaction through data processing system

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8
Q

sequence codes

A

items numbered consecutively to account for all items

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9
Q

block codes

A

blocks of numbers reserved specific categories of data

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10
Q

group codes

A

2+ subgroups of numbers used to code items (VINs)

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11
Q

mnemonic codes

A

letters/numbers used to identify items

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12
Q

chart of accounts

A

list of numbers assigned to financial statement accounts

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13
Q

batch processing

A

accumulating transactions into groups for processing at a regular interval (daily/weekly)

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14
Q

real time processing

A

processes transactions immediately after they occur

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15
Q

3 forms of info output

A
  1. document
  2. report
  3. query
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16
Q

smart contracts

A

regular contract with terms built into blockchain that can be automated and executed

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17
Q

ERP system

A

Enterprise Resource Planning - integrates all aspects of an organization’s activities in one system

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18
Q

ERP system advantages

A

enterprise-wide view of data
data captured once
standardization of procedures
improved customer service
increased productivity

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19
Q

ERP system disadvantages

A

costly
time to implement
standardizing business processes
complexity
user resistance

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20
Q

documentation

A

narratives, flowcharts, diagrams, written materials to explain how a system works

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21
Q

5 reasons for documentation systems/processes

A
  1. systems development + changes
  2. efficient knowledge transfer
  3. effective audits
  4. comply with laws + regulations
  5. evaluate + improve internal controls
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22
Q

Business Process Diagram (BPD)

A

visualizes steps/activities in a business process at a high level overview

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23
Q

BPMN Flow objects:

A

Events, Activities, Gateways

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24
Q

BPMN Connecting Objects

A

Sequence flow, Message flow, Association

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25
BPMN Swim Lanes
Pool or Lanes
26
BPMN Artifacts:
Data objects, Annotations, Group, Control, Risk
27
program flowchart
illustrates logical operations in system flowchart
28
system flowchart
shows data processing cycle for a process in a system
29
Data Flow Diagram (DFD)
shows flow of data within organization (data sources, destinations, flows, storage)
30
context diagram
highest level data flow diagram that provides summary-level view
30
Why subdivide DFD into lower levels?
variety of levels satisfied different needs increasing amounts of detail hard to fully diagram on one sheet of paper
30
big data
large, complex datasets too big for existing systems
31
4 V's of big data
Volume, veracity, variety, velocity
31
Big Data: Volume
massive amount of data
32
Big Data: Variety
unstructured + unprocessed data
33
Big Data: Velocity
data comes in at quick speeds
34
Big Data: Veracity
quality/trustworthiness of data
35
data analytics
examining, removing excess noise, and organizing big data for decision making
36
analytics mindset
centers on correct use of data for decision making
37
4 steps of analytics mindset:
1. ask the right questions 2. extract, transform, load data 3. apply appropriate data analytics techniques 4. interpret + share results with stakeholders
38
SMART Questions:
Specific Measurable Achievable Relevant Timely
39
ETL =
extracting, transforming, loading data
40
5 attributes of high-quality data
Accurate, Complete, Consistent, Timely, Valid
41
4 Step process - transforming data to improve quality
1. Data Structuring 2. ...Standardization 3. ...Cleaning 4. ...Validation
42
Data structuring
changing organization and relationship among data to prepare for analysis
43
3 functions of data structuring
1. data joining 2. aggregate data 3. data pivoting
44
data joining
(data structuring) - process of combining data sources
45
aggregate data
(data structuring) - presentation of data in summarized form
46
data pivoting
(data structuring) - rotating data from rows to columns
47
Data standardization
standardizing structure/meaning of data
48
5 functions of data standardization
1. data parsing 2. data concatenation 3. cryptic data values 4. misfielded data values 5. data consistency
49
Data parsing
(data standardization) - separating data from a single field to multiple fields
50
Data concatenation
(data standardization) - combining data from 2+ fields to single field
51
cryptic data values
(data standardization) - data that has no meaning without understanding coding scheme
52
Misfielded data values
(data standardization) - data formatted correctly but listed in wrong field
53
data consistency
(data standardization) - every value in a field should be stored the same way
54
Data cleaning
process of updating data to be consistent, accurate, complete
55
3 Functions of data cleaning
1. Data deduplicatoin 2. data filtering 3. data imputation
56
Data filtering
(data cleaning) removing records/fields of information
57
Data imputatoin
(data cleaning) replacing null/missing value with substituted value
58
4 types of data cleaning errors:
1. data contradiction errors 2. data threshold violations 3. violated attribute dependencies 4. data entry errors
59
Data validation
process of analyzing data to make sure it is high quality
60
4 functions of data validation
1. visual inspection 2. basic statistical tests 3. audit a sample 4. advanced testing techniques
61
descriptive analytics
what happened?
62
diagnostic analytics
why did it happen?
63
predictive analytics
what will happen in the future?
64
prescriptive analytics
what should be done about it?
65
3 principles of designing high quality visualizations
1. simplification 2. emphasis 3. ethical
66
robot process automation (RPA)
computer software programmed to do what humans do
67
Dynamic input
used to facilitate inputting multiple sheets
68
Union tool
vertically expands data by stacking incoming data streams on top of each other based on matching column names, positions or manual alignment
69
join tool
combines 2 data streams based on common fields/record positions
70
find/replace tool
string data - finds string value and replaces it or appends additional fields to table
71
does find/replace have embedded select
no
72
does join have embedded select
no
73
Append fields tool
appends fields from source input to every record in output
74
does append fields have embedded select
yes
75
data cleansing tool
removes nulls, removes punctuation, modifies capitalization
76
auto field tool
sets field type for string types to smallest possible size and type
77

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