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Evidence Guide: BSBMKG528 - Mine data to identify industry directions

Student: __________________________________________________

Signature: _________________________________________________

Tips for gathering evidence to demonstrate your skills

The important thing to remember when gathering evidence is that the more evidence the better - that is, the more evidence you gather to demonstrate your skills, the more confident an assessor can be that you have learned the skills not just at one point in time, but are continuing to apply and develop those skills (as opposed to just learning for the test!). Furthermore, one piece of evidence that you collect will not usualy demonstrate all the required criteria for a unit of competency, whereas multiple overlapping pieces of evidence will usually do the trick!

From the Wiki University

 

BSBMKG528 - Mine data to identify industry directions

What evidence can you provide to prove your understanding of each of the following citeria?

Determine purpose of data mining

  1. Identify and review relevant client or organisational requirements for data mining
  2. Confirm potential uses of data mining outcomes and recommendations
  3. Recognise privacy and other requirements within current legislation, regulation and organisational policy that impact on data mining activities
Identify and review relevant client or organisational requirements for data mining

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Confirm potential uses of data mining outcomes and recommendations

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Recognise privacy and other requirements within current legislation, regulation and organisational policy that impact on data mining activities

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Identify data sources

  1. Identify available data sources from public, client and organisation systems
  2. Negotiate access rights and intellectual property release for relevant data sources
  3. Rank and prioritise data sources for validity, reliability and completion rates
Identify available data sources from public, client and organisation systems

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Negotiate access rights and intellectual property release for relevant data sources

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Rank and prioritise data sources for validity, reliability and completion rates

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Apply data mining techniques

  1. Select appropriate tools and techniques suitable for the type and expected degree of complexity in data analysis
  2. Classify data according to relevant factors including type, content, relationships, location, demographics and maturity
  3. Analyse data to identify patterns, clusters and relationships
  4. Use suitable graphical tools to visualise aggregated data
Select appropriate tools and techniques suitable for the type and expected degree of complexity in data analysis

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Classify data according to relevant factors including type, content, relationships, location, demographics and maturity

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Analyse data to identify patterns, clusters and relationships

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Use suitable graphical tools to visualise aggregated data

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Report and recommend on findings

  1. Assess results of data mining against requirements in order to draw relevant insights
  2. Weight insights for reliability and validity
  3. 4.3 Report data mining process and outcomes in suitable format to support the organisation's knowledge base
  4. Document lessons learned during the processes for future use
Assess results of data mining against requirements in order to draw relevant insights

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Weight insights for reliability and validity

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

4.3 Report data mining process and outcomes in suitable format to support the organisation's knowledge base

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Document lessons learned during the processes for future use

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Assessed

Teacher: ___________________________________ Date: _________

Signature: ________________________________________________

Comments:

 

 

 

 

 

 

 

 

Instructions to Assessors

Evidence Guide

ELEMENT

PERFORMANCE CRITERIA

Elements describe the essential outcomes.

Performance criteria describe the performance needed to demonstrate achievement of the element.

1. Determine purpose of data mining

1.1 Identify and review relevant client or organisational requirements for data mining

1.2 Confirm potential uses of data mining outcomes and recommendations

1.3 Recognise privacy and other requirements within current legislation, regulation and organisational policy that impact on data mining activities

2. Identify data sources

2.1 Identify available data sources from public, client and organisation systems

2.2 Negotiate access rights and intellectual property release for relevant data sources

2.3 Rank and prioritise data sources for validity, reliability and completion rates

3. Apply data mining techniques

3.1 Select appropriate tools and techniques suitable for the type and expected degree of complexity in data analysis

3.2 Classify data according to relevant factors including type, content, relationships, location, demographics and maturity

3.3 Analyse data to identify patterns, clusters and relationships

3.4 Use suitable graphical tools to visualise aggregated data

4. Report and recommend on findings

4.1 Assess results of data mining against requirements in order to draw relevant insights

4.2 Weight insights for reliability and validity

4.3 Report data mining process and outcomes in suitable format to support the organisation's knowledge base

4.4 Document lessons learned during the processes for future use

Required Skills and Knowledge

ELEMENT

PERFORMANCE CRITERIA

Elements describe the essential outcomes.

Performance criteria describe the performance needed to demonstrate achievement of the element.

1. Determine purpose of data mining

1.1 Identify and review relevant client or organisational requirements for data mining

1.2 Confirm potential uses of data mining outcomes and recommendations

1.3 Recognise privacy and other requirements within current legislation, regulation and organisational policy that impact on data mining activities

2. Identify data sources

2.1 Identify available data sources from public, client and organisation systems

2.2 Negotiate access rights and intellectual property release for relevant data sources

2.3 Rank and prioritise data sources for validity, reliability and completion rates

3. Apply data mining techniques

3.1 Select appropriate tools and techniques suitable for the type and expected degree of complexity in data analysis

3.2 Classify data according to relevant factors including type, content, relationships, location, demographics and maturity

3.3 Analyse data to identify patterns, clusters and relationships

3.4 Use suitable graphical tools to visualise aggregated data

4. Report and recommend on findings

4.1 Assess results of data mining against requirements in order to draw relevant insights

4.2 Weight insights for reliability and validity

4.3 Report data mining process and outcomes in suitable format to support the organisation's knowledge base

4.4 Document lessons learned during the processes for future use

Evidence of the ability to:

determine data mining requirements from client and organisational sources

negotiate intellectual property rights release

apply current industry tools and techniques to a current customer data set to identify patterns and cluster trends

prepare graphical representation of data patterns

make recommendations based on an analysis of data mining results.

Note: If a specific volume or frequency is not stated, then evidence must be provided at least once.

To complete the unit requirements safely and effectively, the individual must:

list the various uses of data mining in the context of marketing communications

identify privacy and other relevant legislation related to public and private data

explain the terms 'data validity', 'reliability' and 'completion'

compare the characteristics of public, client and organisational data sets

identify and list the uses of current industry tools and techniques used in data mining.