Data Analysis

Question 1

Data definition

Answer 1

Numbers, letters, symbols, raw facts, events and transactions
Recorded but not yet processed into a form suitable for management use

Question 2

Information

Answer 2

Data which has been processed
So it is meaningful to the person receiving it

Question 3

Information ‘formula’

Answer 3

Data + Meaning

Question 4

Uses of information

Answer 4

1. Planning
2. Decision making
3. Controlling

Question 5

When preparing for a budgeting exercise, management accountants must identify what?

Answer 5

Appropriate sources of information

Question 6

Types of data

Answer 6

1. Quantitative
2. Qualitative
3. Discrete
4. Continuous

Question 7

Quantitative data

Answer 7

Numerical data
Measurements or quantities
Can be analysed using statistical methods (risks management)

Question 8

Qualitative data

Answer 8

Cannot be expressed as numbers/values
Harder to analyse
E.g. nationality, hair colour

Question 9

Discrete data

Answer 9

Non-continuous data
Can only take certain values e.g. integers
Discrete data is counted

Question 10

Continuous data

Answer 10

No gaps
Can take on any value
(within a range)
E.g. time/distance
Continuous data is measured

Question 11

Types of sources of data

Answer 11

Internal

External

Question 12

Internal data sources

Answer 12

E.g.
Accounting records
HR records
Payroll records
Machine logs
Computer systems
Procurement data system
Timesheets
Communication with staff

Question 13

Two types of external information

Answer 13

Formally gathered

Informally gathered

Question 14

Formally gathered data examples

Answer 14

Marketing research
E.g. new trends, customer tastes, competitor products

R&D

Tax and accounting specialists
E.g. new legislation/standards

Legal specialists info
E.g. changes in health and safety at work

Question 15

Informally gathered data

Answer 15

Data gathered on an ongoing basis
E.g. newspapers, internet, meetings with external colleagues

Question 16

Qualities of good information

Answer 16

1. Accurate
   No typos. roundings, categorised, assumptions
2. Complete
   All information provided for the purpose
3. Cost beneficial
   Benefit > cost of producing info
4. User-targeted
   Understandable and useful to recipient
5. Relevant
   For purpose intended
6. Authoritative
   Genuine, highest quality for purpose, source should be knows and reliable
7. Timely
   Produced in advance when needed
8. Easy to use
   Clear, concise, constructive, communicated appropriately

Question 17

Data analysis steps

Answer 17

1. Identify information needs
2. Collect the data
3. Analyse the data
4. Present the information
5. Use the information

Question 18

Three types of data analysis

Answer 18

1. Inferential statistics
2. Exploratory data analysis
3. Confirmatory data analysis

Question 19

Inferential statistics

Answer 19

Uses random sample of data from pop
To describe and make inferences about it

Question 20

Exploratory data analysis

Answer 20

Pattern identified in a set of data

May be:
Regression analysis
Correlation analysis

Question 21

Confirmatory data analysis

Answer 21

Confirms(/disconfirms) hypothesis
Using statistical methods
E.g. price increase of 3% will reduce demand by 5%

Question 22

When is sampling appropriate

Answer 22

When possible to select units from the population

Question 23

Three reasons sampling is necessary

Answer 23

1. Whole population may not be known
2. Testing whole population costly (time+money)
3. Items may be destroyed in testing

Question 24

2 rules of sampling

Answer 24

1. Must be a certain size
2. Must be representative

Question 25

Compatibility bias

Answer 25

Comparing data from different sources

Question 26

Data bias

Answer 26

Sample not representative

Question 27

Selection bias

Answer 27

Not selected randomly

Question 28

Observer bias

Answer 28

Observer assumptions inadvertently influence observations

Question 29

Cognitive bias

Answer 29

(Subconscious) Perception of data by user that leads to misinterpretation of results.

Question 30

Hypothesis testing

Answer 30

Confirming whether a hypothesis is true

Question 31

Statistical significance

Answer 31

Results occurred due to hypothesis not chance

Question 32

Type 1 error

Answer 32

False positive Null hypothesis falsely rejected

Question 33

Type 2 error

Answer 33

False negative Null hypothesis falsely accepted

Question 34

Big data

Answer 34

Datasets whose size is beyond the ability of the typical database software to capture, store, manage, analyse

Question 35

Four Vs of big data

Answer 35

Volume Variety Velocity Veracity

Question 36

Big data Volume

Answer 36

Amount of data der into organisation Do they have resources to store and manage this data? Or have the money to upgrade IT?

Question 37

Big data variety

Answer 37

Various formats of data received Are their systems compatible for and capable of accessing the various forms of data? Legally, is the data owned by the organisation or the 3P?

Question 38

Big data velocity

Answer 38

Speed data fed into organisation Are the systems able to capture and process real time data? Do they have skills to analyse the data in a timely manner?

Question 39

Big data veracity

Answer 39

The reliability of the data received Can they challenge data received data from 3P? Is the data received representative?

Question 40

Importance of big data

Answer 40

Potential to achieve competitive advantage More data sources E.g. social media, internet of things Exponential growth in computing power and storage capacity New avenues of knowledge creation E.g. crowd sourcing, open source software

Question 41

Data science

Answer 41

Collecting, preparing, managing, analysing, interpreting and visualising large and complex datasets Scientific approach applying mathematics, statistics and computing. Increased demand for employees with data science skills

Question 42

Data analytics

Answer 42

Value extracted from big data Converting data into useful information

Question 43

Benefits of big data, data science and data analytics

Answer 43

Significant opportunities Abundance of data, potential to capture and harness data 1. Decision making Speed of analysis 2. Customer analysis Market segmentation and customisation 3. Innovation 4. Risk management

Question 44

Risks of big data, data science and data analysis

Answer 44

1. Storage 2. Skills 3. Data dependency 4. Overload 5. Data privacy 6. Data security

Question 45

Data storage challenge

Answer 45

Systems Must be reviewed and upgraded to cope with data

Question 46

Big data skills challenge

Answer 46

Data scientists and analysts rare Hard to recruit and retain

Question 47

Data dependency risk

Answer 47

If decisions made on weak, erroneous, corrupted data.

Question 48

Data privacy risk

Answer 48

May break legislation