Develop and communicate data visualisations
Overview
Performance criteria
You must be able to:
- develop requirements specifications for data visualisations that accurately describe the problem and scope
- locate and prepare required datasets across multiple sources ready for visualisation
- create a data pipeline to deliver up to date data for visualisation
develop plans to show how the requirements specifications will be met through the data visualisation outputs
identify suitable data visualisation techniques based on the data context and the problem to be solved, to make the data understandable and usable
- create data visualisations that deliver the outputs required
interpret datasets using storytelling and visualisation to aid the audience appreciation of the data
apply data modelling approaches to aid the visual representation of data
define and document the Key Performance Indicators (KPIs) required for data visualisation assignments
select, compose and construct data visualisation graphics that are accessible to all users and follow organisational and industry standard accessibility guidelines
produce and disseminate data visualisations in the form of reports, dashboards, presentations and other communications
- develop, validate and deploy visualisation tools into production environments within the organisation
- create effective contextual awareness communications, guides and training for end users to get the most out of data visualisation tools developed
Knowledge and Understanding
You need to know and understand:
- the foundations of data visualisation including perception, pre-attentive attributes and the use of best practice data visualisation techniques
- the data context and the who the target users / audience for data visualisation are
the business problems to be addressed using data visualisation
the detailed stages of a data lifecycle, including create, store, use, archive and destroy
what is meant by structured/unstructured data and numerical/categorical data
- how to develop an initial understanding of the shape of the data
- how to prepare datasets for data visualisation
- the structure and the need to restructure and reshape datasets to support different visualisations
- the best practice visualisation techniques for different types of data
- the difference between continuous or discrete variables as it impacts visualisation
- the appropriate data to display for a given visualisation solution
- the methods for visualisation of multi-faceted data
- how to select and use analytical and data visualisation software and platforms for large, complex and interlinked data presentation
- the data visualisation tools available and how to apply them in context
- how to manage the reproducibility, security and source control of the data visualisation techniques applied
- how to define, capture and document Key Performance Indicators (KPIs) in context
- how to undertake data-driven storytelling to communicate data, trends, patterns and derived analytical insights
- how to use data modelling to help in the visual representation of data
- how to build a scalable data analytics pipeline