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Follow the links below to find material targeted to the unit's elements, performance criteria, required skills and knowledge

Elements and Performance Criteria

  1. Confirm database design
  2. Identify required data and sources
  3. Determine data warehouse operational steps and processes
  4. Design and develop data warehouse features
  5. Test and implement data warehouse
  6. Finalise work processes

Required Skills

Required skills

analytical skills to

analyse business requirements

gather and analyse user requirements

communication skills to liaise with business and technical staff

literacy skills to prepare reports and technical documentation

numeracy skills to complete costbenefit analyses

planning and organisational skills to manage data warehouse implementation

technical skills to

convert and validate data

perform data modelling

work with databases including programming languages

Required knowledge

business operating systems relating to data sources

database management system DBMS fundamentals to facilitate extraction of data

decision support systems relating to knowledge management strategies

encryption and authentication as they apply to database security features

functions and features of dimension tables and fact tables

installation and use of proprietary software

logical database model knowledge to facilitate data extraction

Evidence Required

The evidence guide provides advice on assessment and must be read in conjunction with the performance criteria required skills and knowledge range statement and the Assessment Guidelines for the Training Package

Overview of assessment

Critical aspects for assessment and evidence required to demonstrate competency in this unit

Evidence of the ability to

undertake activities from proposal to implementation stage for a data warehouse model that reflects current and future business requirements and the business knowledge management strategy and demonstrates

costbenefit analysis of a data warehouse implementation for a defined enterprise

user guide for use by an implemented data warehouse

technical documentation for an implementation of a data warehouse

Context of and specific resources for assessment

Assessment must ensure

data in a DBMS

datawarehousing tools

appropriate learning and assessment support when required

modified equipment for people with special needs

Method of assessment

A range of assessment methods should be used to assess practical skills and knowledge The following examples are appropriate for this unit

review of candidates written report

questioning to determine knowledge of relationship between databases and data warehouses

project to implement a data warehouse

Guidance information for assessment

Holistic assessment with other units relevant to the industry sector workplace and job role is recommended where appropriate

Assessment processes and techniques must be culturally appropriate and suitable to the communication skill level language literacy and numeracy capacity of the candidate and the work being performed

Indigenous people and other people from a nonEnglish speaking background may need additional support

In cases where practical assessment is used it should be combined with targeted questioning to assess required knowledge


Range Statement

The range statement relates to the unit of competency as a whole. It allows for different work environments and situations that may affect performance. Bold italicised wording, if used in the performance criteria, is detailed below. Essential operating conditions that may be present with training and assessment (depending on the work situation, needs of the candidate, accessibility of the item, and local industry and regional contexts) may also be included.

Database may include:

DB2

Informix

Ingres

Microsoft SQL (MS SQL) server

Mini SQL (mSQL)

MySQL

Oracle

Postgre Structured Query Language (Postgre SQL)

Sybase.

Document may relate to:

audit trails

client training

International Organization for Standardization (ISO) standards

maintaining equipment inventory

naming standards

project management templates and report writing

satisfaction reports

version control.

Big data may include:

data access that incorporates high volume, high velocity and a high variety of information with fast in-depth processing

data managed by large information management specialist companies using big data technologies, such as Software AG, Oracle, IBM, Microsoft, SAP, EMC, and HP

data that is distributed within the cloud across a wide number of database servers.

System may include:

application service provider (ASP)

applications

databases

gateways

internet service provider (ISP)

operating systems

servers.

Users may include:

department within an enterprise

person within a department

third party.