List the assessment methods to be used and the context and resources required for assessment. Copy and paste the relevant sections from the evidence guide below and then re-write these in plain English.
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.
Assessment must be conducted in a safe environment where evidence gathered demonstrates consistent performance of typical activities experienced in the marketing communications field of work and include access to:
relevant legislation and regulations
communications equipment and technology
relevant workplace documentation and resources
case studies or, where possible, real situations
industry software packages and apps (where applicable).
Assessors of this unit must satisfy the requirements for assessors in applicable vocational education and training legislation, frameworks and/or standards.