click below
click below
Normal Size Small Size show me how
DA - S14
Data Visualisation
| Term | Definition |
|---|---|
| Heatmaps | Best used for: Identifying "hot spots," trends, density, or areas of high/low concentration, regardless of arbitrary borders. |
| Temporal charts | Line charts, area charts, and candlestick graphs. Used to show trends and cycles. |
| Comparison charts | Bar charts (horizontal/vertical), grouped bars, and butterfly charts. Used to show differences between categories. |
| Colour theory | Use colour to higlight intentional versus distracting (unintentional) content. It also helps with preparing material which is colour blindness friendly. |
| Edward Tufts - Data to Ink ratio | Good graphics should include only data-ink. This is to avoid distracting the viewer's attention to irrelevant elements. |
| Static Reports | Show a point in time. They are useful for documentation and where the narrative needs to be controlled |
| Interactive (Dashboards / BI Tools) | Aid understanding through drill downs and providing current data for quickly actionable decisions. |
| A failed visualisation is one where. | A Stakeholder has to ask, "What am I looking at?". |
| What is the goal of a visualisation? | Visualisation helps to move from raw data to information to Insight, providing the opportunity for actionable knowledge. |
| What are Heatmaps more useful for? | If the data is about concentration or volume regardless of borders (e.g., "Where are our customers geographically clustered?"), use a Heatmap |
| What is a Choropeth map? | If the data is tied to a specific jurisdiction or territory (e.g., "What are our sales by state?"), use a Choropleth Map. |
| The impact of Natural Language Query (NLQ) | Instead of dragging and dropping fields, analysts type, "Show me the correlation between regional rainfall and supply chain delays," and the tool generates the optimal chart instantly. |
| The new impact of ScrollyTelling | A web-based technique where visualizations change or animate as the user scrolls down a page, guiding them through a logical narrative sequence. |
| Adaptive Design | Visuals that automatically change their level of detail based on who is viewing them—showing high-level KPIs for an executive but expanding into granular tables for a technical manager. |
| Dashboards are becoming "living" documents. | A document that is continuously updated and revised to reflect current information, processes, or requirements rather than remaining static after creation |
| Data Provenance | The visual "audit trail" showing where the data came from and how it was cleaned. |
| Cognitive Load | The amount of mental effort required to understand a chart (Lower is better). |
| What does Data Visualisation mean? | Means to communicate your analysis with stakeholders |
| One Benefit of Visualistion | Simplifying and displaying complex data in an understandable format to aid stakeholders with key decision making |
| A second benefit of Visualisation | Highlighting key insights quickly so stakeholders who are short for time do not need to review all the data they can see they key information |
| A third benefit of Visualisation | Well designed and displayed visuals will ensure that stakeholders do not misunderstand or misinterpret the data |
| Some of the software tools available | Excel, Power BI, Python |
| The benefits of Excel | Widely used and understood, stakeholder familiarity; good for quick analysis |
| Limitations of Excel | Very limited within visuals; Weak for very large data set handling; Simple visuals can be less engaging. |
| Key uses of Excel | ideal for quick and ad-hoc reporting; Non-technical stakeholders will be familiar and likely more comfortable with it. |
| The benefits of Power BI | Strong interactive dashboards with features such as slives; Real time unpdateable dashboards. |
| Limitations of Power BI | More time consuming to set and maintain than excel; Visuals take a higher skill level to produce key insights. |
| Key uses of Power BI | Creating dashboards; Useful for stakeholders who require high level insights with interactive features. |
| The benefits of Python | Highly flexible and limitless visuals; Strong handling of data sets |
| Limitations of Python | Requires strong coding skills to create visuals; Less accessible for non-technical stakeholders. |
| Key uses of Python | High level analysis and visuals with large data sets. |
| Some of the approaches to visualisations | Scatter plot charts, Pie charts, Data Tables, Bar charts, Line charts |