1
Q
Why need data visualisations
A
- Humans easily recognise patterns
- faster understanding than a mass of numbers or text
2
Q
information is meaningless without context - expand
A
Without context information nor charts cannot be evaluated.
It’s important to answer these questions:
3
Q
The Good Chart Matrix
A
Contextual Awareness: What am I trying to Say, To Whom and where?
Design Execution: How well is this chart constructed?
- Requires principles that help you understand why you’d choose a bar chart or a line chart or no chart at all.
- A perfectly relevant visualization that breaks a few presentation rules is far more valuable than a perfectly executed, beautiful chart that contains the wrong data, communicates the wrong message, or fails to engage its audience.
- The more relevant a data visualization is, the more forgiving, to a point, we can be about its execution.
4
Q
Three interrelated trends are driving the need to learn and practice visual thinking.
A
1. Increasing volume of visualizations.
- Raised expectations due to constant exposure to better dataviz.
2. Increasing data volume and velocity.
- So much information hitting us so fast that it demands a abstract and simplified way of communication - dataviz.
3. Increasing participation.
- Everybody’s doing it because it has become cheap and easy.
5
Q
Conventions and Metaphors
A
We rely on conventions and metaphors.
We are hardwired and shaped by upbringing to see and expect the world like this.
6
Q
5 things we do when a chart hits our eyes
A
1. We don’t go in order.
- Visuals aren’t read in a predictable, linear way, as text is.
- Instead, we look first at the visual and then scan the chart for contextual clues about what is important.
- What this means:
- “write” charts spatially, from the visual outward
2. We see first what stands out.
- Our eyes go directly to change and difference, such as unique colors, steep curves, clusters, or outliers.
- What this means:
- Whatever stands out should match or support the idea being conveyed. If it doesn’t, it will distract from and fight for attention with the main idea.
3. We see only a few things at once.
- A visual that contains tens, hundreds, or thousands of plotted data points shows us a forest instead of individual trees. The more data that’s plotted in a chart, the more singular the idea it conveys.
- What this means:
- To focus on individual data points, plot as few as possible
4. We seek meaning and make connections.
- What this means:
- If visual elements are presented together, they should be related in a meaningful way; otherwise viewer creates false relationships.
5. We rely on conventions and metaphors.
- We use learned shortcuts to assign meaning to visual cues on the basis of common expectations (green is good, red is bad).
- What this means:
- Embrace conventions and metaphors when creating visuals, otherwise creates confusion.
7
Q
2 questions about the nature and purpose of your visualization
A
1. Is the information conceptual or data-driven?
- Either you’re visualizing concepts and qualitative information or you’re plotting data and information.
- But notice that the question is about the information itself, not the forms that might ultimately be used to show it.
2. Am I declaring something or exploring something?
- Declarative: well-designed, finished products;
- e.g. presenting sales data
- Confirmatory: looking to understand something, less formal
- Exploratory: unsure what you are looking for, rough design; good for brainstorming and discoveries
8
Q
Concepts & Models
A
Concepts arise as abstractions or generalisations from experience; from the result of a transformation of existing ideas; or from innateproperties.
All models are wrong, some are useful.
9
Q
4 types of visualisations (matrix)
A
Helps make more appropriate decisions about the needed forms, time and skills.
10
Q
Idea illustration quadrant in detail
A
At their best, declarative, conceptual visualizations simplify complex ideas by drawing on people’s ability to understand metaphors (trees, bridges) and simple conventions (circles, hierarchies).
Examples:
- organisational charts
- decision trees
- cycle diagrams
11
Q
Idea generation quadrant in detail
A
Explore ideas using non-data visuals
- whiteboard
- back of a napkin
12
Q
Visual discovery quadrant in detail
A
Actually two categories - 3 possible types of tasks: declarative, confirmatory, and exploratory.
Confirmatory applies only to data-driven charts.
- A hypothesis can’t be confirmed or disproved without data.
- Soft distinction. Confirmation is a kind of focused exploration, whereas true exploration is more open-ended. The bigger and more complex the data, and the less you know going in, the more exploratory the work.
Visual confirmation. Answer two questions:
- Is what I suspect is true actually true?
- What are some other ways of looking at this idea?
- The scope of the data here is manageable, and the chart types you’re likely to use are common (not necessarily).
- Confirmation usually doesn’t happen in a formal setting; it’s the work you do to and the charts you want to create for presentations. That means your time will shift away from design and toward prototyping that allows you to iterate on the data and rapidly visualize and re-visualize.
Visual exploration.
- usually data scientists and business intelligence analysts
13
Q
Everyday dataviz quadrant in detail
A
The data sets tend to be small and simple.
Goal: give people factual information based on data
14
Q
What are BI applications
A
Deliverables by the BI team
- For business people to use in their analysis
- Usually, build several BI applications at a time
Deliverables can be
- Report
- Dashboard
- Scorecard
- Data visualization
- Ad hoc query
- OLAP cube
- Predictive model
- Data model
15
Q
Role of Requirements in BI Applications + 2 types of requirements
A
- Foundation for a successful BI solution
- Development team uses these requirements to:
- design
- develop
- deploy BI systems
- Functional requirements: what the users will get; use cases
- Non-functional requirements: define qualities and constraints
- qualities define the properties and characteristics which the delivered system should demonstrate
- constraints are the limitations, standards and environmental factors which must be taken into account in the solution
- e.g. availability, performance, security, operability
16
Q
Draw stepwise refinement of requirements
A
Stepwise refinement is the process of taking business requirements and going deeper into the details to define data, functional, technical, and regulatory requirements.
Stepwise refinement is also called functional decomposition.
17
Q
Defining requirements workflow
A
Data Profiling - structure and content of input data
Visualize Functions - use storyboards, mockups, use cases, wireframes, prototypes
Replacement Requirements - Data Shadow Systems - stuff the business unit has already built for its own reporting and analysis (Excel, etc.) The project’s aim is to get that into a properly developed and maintained BI solution, to protect against a number of potential problems, e.g. loss of the person who built the system, obsolescence of the technology used to build it, difficulty in keeping up with changes in underlying data
18
Q
Next step after requirements have been gathered
A
- Now need to develop BI content specifications
- Starting point for design, development of BI applications
- BI lends itself to an iterative and incremental methodology
- Mix of waterfall and agile
19
Q
Waterfall Approach
A
Sequential (non-iterative) software design process
- Conception
- Initiation
- Analysis
- Design
- Construction
- Testing
- Production/implementation
- Maintenance
20
Q
5 steps in application specification
A
- Build up an application specification (initiation)
- Specify the data
- Manage business transformation for the application
- Clarify business users
- Estimate, costs needed to deliver
21
Q
- Build up an application specification
A
- Name and identifier
- Description – short - business purpose
- evaluates usefulness, clarifies who pays
- Category,
- this is a functional grouping, expense tracking
- e.g. sales performance
- Business processes – supported by the application
22
Q
- Application specification: Data Specifications
A
- Data sources, e.g. EDW, OLAP cube, SoR
- Data structure - XML, OLAP cube, NoSQL
- Table names
- Column names
- Column data type
- Status in the data source
- Exists, ready for use
- Exists, but needs to be transformed
- Does not exist, needs to be obtained from a SoR
23
Q
- Business transformations for this application
A
Filters, business rules, algorithms used on input data
- Specific to this BI application
- Over and above data integration transformation
Business transformations for analysis
- Calculate measures and KPIs
BI Analytical style to be used
- Reports, dashboards etc.
24
Q
- Business users of the application
A
- Business owner
- Business contact
- The owner usually delegates to a contact
- BI owner – responsible for project delivery
- BI developer
- Business priority, relative to other BI applications
25
5. Estimates, costs needed to deliver
* Estimates of resources needed by project
* People, technology, licenses etc.
* Include business people as well as BI project team
* Assumptions, dependencies, risks, issues
* Comments
* From business and technical staff
_People need for_:
* Validate and adjust requirements and prototypes
* Do use testing, including user accepting testing
* Sign off requirements and user acceptance testing
26
Reviewing your BI applications
* Review scope of applications
* Categorise, prioritise, estimate costs
* Consolidate
* can we address several requirements with one application
* self service or dashboards may offer opportunities
* Review Specifications
* —with business stakeholders and SMEs
* —project dependencies and risks
* Create an agreed list of BI deliverables
27
BI *Personas* (definition + examples)
Fictional characters created to represent the different user types that might use a site, brand, or product in a similar way.
BI examples, with _different needs_:
* **Casual consumers**
* Many/most people in an enterprise; will use reports and dashboards to do their job, and move on.
* **Analysts**
* Use BI applications to gather and explore data they need to perform their jobs.
* **Power users**
* Departmental experts at using BI tools; will be the “go to” people in the department.
* **Data scientists**
* Maths/statistics guys; understand the business and industry
28
Role of Consistency in Data Visualisations
**Consistency beats elegance**
* Use a design template for each BI style
* Templates improve productivity, speed of BI team
* Users should feel each BI application is familiar
* In both layout and navigation
29
Design Guidelines of Dashboards
*Dashboard – number of different views on the same screen.*
* **Filters** (on the left) - selective viewing
* Capable to **drill down** by clicking
* **Keep it simple**; don’t cram
* Important data on the top, especially top left; go top to bottom and left to right
* Use **colours** judiciously - limited number of colours; colour gradients effective in showing differences
* Use contrasting colours for easy distinguishing
* Use colours conventionally (red = bad)
30
Match these **analysis types** to visualizations
* Comparative analysis
* Time-series or trending
* Contribution
* Correlation
* Geographic data
* Comparative analysis - **bar chart**
* Time-series or trending - **line graph, area chart, line chart**
* Contribution analysis - **heatmap**
* Correlation analysis - **scatter plot**
* Geographic data - **mapping**
31
Comparison analysis – bar chart
* Bar charts are very effective because the lengths of the bars represent quantitiesthat can be compared quite easily
* Often best to use one colourbecause it is easier to estimate the different sizes
* Horizontal bars work well for any number of items
* Vertical bars work well with fewer than twelve items
Bar charts are one of the most common ways to visualize data. Why? It’s quickto compare information, revealing highs and lows at a glance. Bar charts areespecially effective when you have numerical data that splits nicely into differentcategories so you can quickly see trends within your data.
When to use bar charts:
* •Comparing data across categories. Examples: Volume of shirts in differentsizes, website traffic by origination site, percent of spending by department.
Also consider:
* •Include multiple bar charts on a dashboard. Helps the viewer quicklycompare related information instead of flipping through a bunch of spreadsheets or slides to answer a question.
* •Add colourto bars for more impact. Showing revenue performance withbars is informative, but overlaying color to reveal profitability providesimmediate insight.
* •Use stacked bars or side-by-side bars. Displaying related data on top ofor next to each other gives depth to your analysis and addresses multiplequestions at once.
* •Combine bar charts with maps. Set the map to act as a “filter” so when youclick on different regions the corresponding bar chart is displayed.
* •Put bars on both sides of an axis. Plotting both positive and negative datapoints along a continuous axis is an effective way to spot trends.
32
Trend analysis - line graph
* Line charts very effective at displaying segmented data which changesover time
* Better than bar charts if the user wants to examine the trends and their totals
* Here we have the value of bike sales by type over time
Line charts are right up there with bars and pies as one of the most frequently usedchart types. Line charts connect individual numeric data points. The result is a simple,straightforward way to visualize a sequence of values. Their primary use is to displaytrends over a period of time.
When to use line charts:
* •Viewing trends in data over time. Examples: stock price change over a five yearperiod, website page views during a month, revenue growth by quarter.
Also consider:
* •Combine a line graph with bar charts. A bar chart indicating the volume soldper day of a given stock combined with the line graph of the corresponding stockprice can provide visual queues for further investigation.
* •Shade the area under lines. When you have two or more line charts, fill thespace under the respective lines to create an area chart –see next slide. This informs a viewerabout the relative contribution that line contributes to the whole.
33
Trend analysis – area chart
Area chartsare good at showing the individual contributions and the totals over time
Here we have the value of bike sales by type over time but we can also see the total value of bike sales
When you have two or more line charts, fill thespace under the respective lines to create an area chart. This informs a viewerabout the relative contribution that line contributes to the whole.
34
Trend analysis – bar chart
35
Contribution analysis – heat map
The contribution that something makes is usually represented by a percentage. People usually use a pie chart.
When there are too many slices in a pie chart (more than four or five) it gets hard to compare the slices
A heat map can accommodate a much larger number of slices, especially when you use a colourgradient to make it more useful
Notice we’ve selected Australia in the filter on the right!
Heat maps are a great way to compare data across two categories using color. Theeffectis to quickly see where the intersection of the categories is strongest and weakest.
When to use heat maps:
* •Showing the relationship between two factors. Examples: segmentationanalysis of target market, product adoption across regions, sales leads by individual rep.
Also consider:
* •Vary the size of squares. By adding a size variation for your squares, heatmaps let you know the concentration of two intersecting factors, but add a thirdelement. For example, a heat map could reveal a survey respondent’s sportsactivity preference and the frequency with which they attend the event based oncolour, and the size of the square could reflect the number of respondents in thatcategory.
* •Using something other than squares. There are times when other types ofmarks help convey your data in a more impactful way
36
Correlation analysis – scatter plot
Scatter plotswork well for correlation analysis, especially when size and colourare used to show variation in the measure’s quantitative values
Using scatter plots is a quick, effective way to spot outliers that might warrant furtherinvestigation.
By creating this scatter plot, an insurance investigator canquickly evaluate where they might have fraudulent activity.
When to use scatter plots:
* •Investigating the relationship between different variables. Examples: Maleversus female likelihood of having lung cancer at different ages, technology earlyadopters’ and laggards’ purchase patterns of smart phones, shipping costs of different product categories to different regions.
Also consider:
* •Add a trend line/line of best fit. By adding a trend line the correlation amongyour data becomes more clearly defined. Incorporate filters. By adding filters to your scatter plots, you can drill down intodifferent views and details quickly to identify patterns in your data.
* •Use informative mark types. The story behind some data can be enhanced witha relevant shape
37
Geography mapping
To visualize data by location you can use a map
You can see where your sales are –a lot in London, fair bit in Berlin and Glasgow
You can also see how a pie chart with lots of slices doesn’t really work well –they would have been better to limit the number of wedges to twoor three
In any case, when making pie charts usethemto show relative proportions – or percentages – ofinformation. That’s it. Despite this narrow recommendation for when to use pies, they
are made with abandon. As a result, they are the most commonly mis-used chart type.If you are trying to compare data, leave it to bars or stacked bars. Don’t ask yourviewer to translate pie wedges into relevant data or compare one pie to another. Keypoints from your data will be missed and the viewer has to work too hard.
When to use pie charts:
* •Showing proportions. Examples: percentage of budget spent on differentdepartments, response categories from a survey, breakdown of how Americansspend their leisure time.
* •Limit pie wedges to six. If you have more than six proportions to communicate,consider a bar chart. It becomes too hard to meaningfully interpret the pie pieceswhen the number of wedges gets too high
Pairing the map with an additional chart, such as a line chart showingtrends or a data table providing the underlying detail, is often very useful in enhancing the information conveyed
You could put all three –map, line chart, data table together on a dashboard
38
Tableau Paper:
## Footnote
*Which chart or graph is right for you?*
13 different types of charts, and where they are most appropriate
1. Bar chart
2. Line chart
3. Pie chart
4. Map
5. Scatter plot
6. Gantt chart
7. Bubble chart
8. Histogram chart
9. Bullet chart
10. Heat map
11. Highlight table
12. Treemap
13. Box-and-whisker plot
39
BI User Interface standards
*The important thing is to have standards...*
* Layout standards specific for each BI style
* Dashboards, scorecards, reports, etc.
* Common layout standards
* Layout, text, colours, filters
* Delivery platform standards
* Browser, tablet, smartphone, print, pdf
* Chart type standards
* Revise, as technology and possibilities change
40
Benefit of Standards
* Developer productivity
* Familiarity, reuse
* User productivity
* Less confusion
* Better quality
* templates can be checked
41
How to develop Standards
Develop your standards as you go along
* no months of up-front effort
* however, remember that you need to stop and settle on standards
42
4 BI visual design methods
* Increasing visual detail as we go left to right
* Should be fast –clarify the design
43
BI storyboard
lays out the major actions that happen as the user uses the application
44
BI prototyping - 7 steps
This is where you build a mockup or, better, an early version of the application.
You **show it to users** (all personas), get feedback, and iterate
45
BI application development lifecycle
46
BI application testing
Rule of thumb: testing should be 30% of a project’s effort.
* Best done agile... test as they build
_User Acceptance test_ – does the application meet the criteria agreed with the users, and is it usable?
_System and performance testing_:
* Smoke test – preliminary testing, ensure critical functions work
* Stress test – heavy load (say 150% of the expected load) to see where it stops being responsive; you may do performance analysis and tuning as you hit problems in stress test
* Soak test - run the system for a long time to ensure long-term functionaly (e.g. memory leaks)
Business Intelligence
flashcards
Decks in class (14)
# Cards
