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Elements and Performance Criteria

1. Determine scope of big data capture
   • Identify business need for big data capture
   • Determine timelines for capturing big data
   • Identify volume and variety of big data to be captured
   • Identify data storage and archive requirements
   • Develop a plan for big data capture according to organisational policies and procedures
   • Identify sources of big data relevant to business need
   • Obtain big data from identified sources according to legislative requirements and organisational policies and procedures
   • Apply big data cleansing methodologies
   • Re-create and recover big data lost during capture process according to organisational policies and procedures
   • Conduct data offload and archiving on big data and create report
2. Store captured big data
   • Organise obtained big data sets in a retrievable format
   • Confirm that big data is accurate, up-to-date, and comprehensive
   • Securely store big data and data capture report according to organisational procedures, legislative requirements, and industry practices

Performance Evidence

The candidate must demonstrate the ability to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including evidence of the ability to:

capture, apply data cleansing, and store one set of transactional and one set of non-transactional big data relating to specific workplace needs

create a report relating to data capture for a specific workplace need for at least one set of transactional and one set of non-transactional big data.

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Knowledge Evidence

The candidate must be able to demonstrate knowledge to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including knowledge of:

legislative requirements relating to big data capture and storage, including data protection, security and privacy laws and regulations

organisational policies and procedures relating to big data capture and storage, including for:

capturing, storing and reporting transactional and non-transactional sources of big data

cleansing methodologies when capturing big data

business intent of big data capture

re-creating and recovering big data lost during capture process

privacy concerns of predictive analytics organising security and retrieval of big data

quality assuring captured big data

data capture techniques to produce analytic ready data and datasets

big data features and their impact on data capture and storage, including velocity, volume, variety, value, and veracity

variability and complexity of big data:

structured and unstructured

transactional and non-transactional

framework for distributed storage of big data

platform solutions for capturing and storing big data

key features of organisational products and services related to big data capture

technologies, techniques and protocols for capturing, storing and retrieving big data

secure data access and control mechanisms of captured and stored big data

high availability and scalability challenges with big data.

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