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BTF > Data Analysis > Flashcards

Data Analysis Flashcards

1
Q

Data definition

A

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

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2
Q

Information

A

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

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3
Q

Information ‘formula’

A

Data + Meaning

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4
Q

Uses of information

A
  1. Planning
  2. Decision making
  3. Controlling
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5
Q

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

A

Appropriate sources of information

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6
Q

Types of data

A
  1. Quantitative
  2. Qualitative
  3. Discrete
  4. Continuous
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7
Q

Quantitative data

A

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

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8
Q

Qualitative data

A

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

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9
Q

Discrete data

A

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

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10
Q

Continuous data

A

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

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11
Q

Types of sources of data

A

Internal

External

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12
Q

Internal data sources

A

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

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13
Q

Two types of external information

A

Formally gathered

Informally gathered

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14
Q

Formally gathered data examples

A

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

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15
Q

Informally gathered data

A

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

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16
Q

Qualities of good information

A
  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
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17
Q

Data analysis steps

A
  1. Identify information needs
  2. Collect the data
  3. Analyse the data
  4. Present the information
  5. Use the information
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18
Q

Three types of data analysis

A
  1. Inferential statistics
  2. Exploratory data analysis
  3. Confirmatory data analysis
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19
Q

Inferential statistics

A

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

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20
Q

Exploratory data analysis

A

Pattern identified in a set of data

May be:
Regression analysis
Correlation analysis

21
Q

Confirmatory data analysis

A

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

22
Q

When is sampling appropriate

A

When possible to select units from the population

23
Q

Three reasons sampling is necessary

A
  1. Whole population may not be known
  2. Testing whole population costly (time+money)
  3. Items may be destroyed in testing
24
Q

2 rules of sampling

A
  1. Must be a certain size
  2. Must be representative
25
Q

Compatibility bias

A

Comparing data from different sources

26
Q

Data bias

A

Sample not representative

27
Q

Selection bias

A

Not selected randomly

28
Q

Observer bias

A

Observer assumptions inadvertently influence observations

29
Q

Cognitive bias

A

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

30
Q

Hypothesis testing

A

Confirming whether a hypothesis is true

31
Q

Statistical significance

A

Results occurred due to hypothesis not chance

32
Q

Type 1 error

A

False positive

Null hypothesis falsely rejected

33
Q

Type 2 error

A

False negative

Null hypothesis falsely accepted

34
Q

Big data

A

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

35
Q

Four Vs of big data

A

Volume

Variety

Velocity

Veracity

36
Q

Big data Volume

A

Amount of data der into organisation

Do they have resources to store and manage this data?
Or have the money to upgrade IT?

37
Q

Big data variety

A

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?

38
Q

Big data velocity

A

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?

39
Q

Big data veracity

A

The reliability of the data received

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

40
Q

Importance of big data

A

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

41
Q

Data science

A

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

42
Q

Data analytics

A

Value extracted from big data

Converting data into useful information

43
Q

Benefits of big data, data science and data analytics

A

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
44
Q

Risks of big data, data science and data analysis

A
  1. Storage
  2. Skills
  3. Data dependency
  4. Overload
  5. Data privacy
  6. Data security
45
Q

Data storage challenge

A

Systems Must be reviewed and upgraded to cope with data

46
Q

Big data skills challenge

A

Data scientists and analysts rare
Hard to recruit and retain

47
Q

Data dependency risk

A

If decisions made on weak, erroneous, corrupted data.

48
Q

Data privacy risk

A

May break legislation

BTF (14 decks)

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