Data Visualisation

1. Data Analysis and
Visualisation

2. Techniques and Best Practices
• Understanding of data available internally and externally
• Access, Structured, Quantitative/Qualitative, Metadata Repository (data dictionary), Data Cleaning
• Identifying outliers through scattered plots. Point falls away from the data trend, it is worth investigating
• Ridden with confirmation bias – don’t go into analysis with desired results.
• Comments and documents that explains the steps taken throughout the analysis – tables/ Formula etc.
• Use of data should focus towards mitigation of issues rather than telling the story what happened historically
• Data Management Tips
• Move all of your data to a centralized database to create a standardized data architecture.
• Ensure your employees are up to date on all aspects of data best practices, including data entry,
management, compliance and safety.
• Create data management hierarchies if you have multiple teams to keep it all organized and reduce the
odds of a breach occurring.
• Designate certain team members to handle core data management.
• Shareable Dashboard for streamlined communication, Fully Mobile with third party integration
• Data Silos – Silos, Duplicate Information, lost opportunity of improvement
• there should be someone who interacts with each of the leaders within each department to distill the
information and prioritize data integrity while transforming the data into valuable insights
• Adoption of standard datasets, models, schemas, and codes significantly decreases complexity within your data
supply chain
• establishing a central repository and setting standardized rules for capturing, storing, and analyzing data while
encouraging organization-wide transparency and collaboration without sacrificing security or data integrity.

3. Introduction to Visualization