Lecture 7&8 Flashcards

(14 cards)

1
Q

Describe what data analytics is

A

Data analytics is defined as the science of examining raw data, removing excess noise and organizing the data with the purpose of drawing conclusions for decision-making, it often involves the technologies, databases, and applications used to analyze diverse business data to help organizations make sound and timely business decisions.

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2
Q

Challenges of data analytics

A
  • Storage and processing
  • Extraneous data and noise
  • Cost and time of cleaning data
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3
Q

Benefits of data analytics

A
  • Business value creation
  • Investigate anomalies
  • Forecast future behaviour
  • Identify future opportunities and risk
  • Affects internal process
  • Improving productivity
  • growth
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4
Q

Application of data analytics in accounting

A

An analytics mindset is a way of thinking that centres on the correct use of data and analysis for decision-making. It enables the ability to ask the right questions, extract transform and load relevant data, apply appropriate data analytic techniques, interpret and share the results with stakeholders.

The ETL process (extract transform and load relevant data)
This is the most time-consuming part of the process however this can be done fully automated.

Data analytics can be integrated into both auditing and financial reporting

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5
Q

Extracting Data

A

3 Steps
- Understand the datas needs and the data available
- Perform the data extraction
- Verify the data extraction quality and document what you have done.

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6
Q

Transforming Data

A

4 Steps
- Understand the data and the desired outcome
- Standardise, structure and clean the data
- Validate data quality and verify data meets data requirements
- Document the transformation process

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7
Q

Loading Data

A
  1. The transformed data must be stored in a format and structure acceptable to the receiving software
  2. Programs may treat some data formats differently than expected. It is important to understand how the new program will interpret data formats.
    - Once the data is successfully loaded into the new program, it is important to update or create a new data dictionary.
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8
Q

AMPS model for data analytics

A
  • Ask the question
    - Use a SMART question
  • Master the data
    - Data accessibility
    - Data reliability and validity
    - Data integrity
    - Data type
  • Perform the analysis
    - What happened? – descriptive analysis
    - Why did it happen? – diagnostic analysis
    - Will it still happen in the future? – predictive analysis
    - What should we do, based on what we expect to happen? – prescriptive analysis
  • Share the history
    - Data storytelling is the process of translating often complex data analyses into more.
    easy-to-understand terms to enable better decision making
    - Remember the question that initiated the analytics process
    - Consider the audience
    - Use data visualizations
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9
Q

Data analytics software

A
  • Excel
  • Python
  • +ableau
  • Alteryx
  • SAS
  • Microsoft Power BI
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10
Q

What is big data?

A

Big data is everything that leaves a trace e.g., all the data that is captured when we use digital technology, everything we do leaves a digital trace behind.

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11
Q

What are the V’s of big data?

A
  • Velocity speed of data
  • Volume scale of data
  • Variety diversity of data
  • Veracity certainty of data
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12
Q

Big data - Internal sources

A

Accounting Information systems
EFTPOS
Survey data

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13
Q

Big data - External sources

A

Social Media
IoT
Google Analytics

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14
Q

Application of big data in accounting

A
  • Video data
  • Audio data
  • Images
  • Textual data
  • Application of big data in financial accounting
  • Application of big data in management accounting
  • Application of big data in auditing and internal controls
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