The document outlines an agenda for a data visualization workshop presented by Sean Burton. The workshop will cover topics such as perception and visual processing, examples of different visualization techniques, best practices for design, and exercises for participants. It provides background on Sean Burton and lists 23 attendees.
MeasureCamp V London training: Data VisualisationSean Burton
Data are beautiful… True of your spreadsheets?
This session looked at how to present data in an informative and insightful way – beautiful without the gimmicks!
Measurecamp 6 Workshop: Data IntegrationSean Burton
This document summarizes a presentation on data integration. It discusses collecting data from different sources like web analytics, surveys, CRM and combining it to understand the customer experience. It provides examples of integrating web analytics and survey data in Google Analytics using custom dimensions. Integrating these data sources can provide insights into who customers are, their needs, and how to improve processes based on where customers drop off or leave feedback.
This document profiles Sean Burton, the Director of Measurement at Analytic. It provides details about Sean's background including 15 years of experience in eLearning, content management, interaction design, product management, web analytics, and data visualization. It also lists some of the brands Sean has worked with, which come from financial, telecommunications, gaming, and retail sectors. The document advocates for measuring the customer experience using a blend of data, technology, and psychology to improve business value.
The document discusses measuring and improving customer experience. It provides examples of how three different organizations - an electricity company, life assurance company, and train franchise - used a simple tool to map customer touchpoints, measure customer experience scores internally and externally, identify areas for improvement, and implement quick wins to improve the customer experience and retention. The tool helped reveal gaps between staff and customer perceptions, and showed that improving communications and information provision could have a significant impact on experience scores at a lower cost than other options like solely focusing on punctuality. Measuring customer experience provides a better way to improve retention than only measuring customer satisfaction.
Analytics for Customer Acquisition - Presentation at Nasscom Product Conclave...Arun Agrawal
Don't jump into Google Analytics without defining your KPIs first. Set your targets and analyse with this guide.
Includes strategies and tactics to solve the low traffic and low web site conversion problems. Apply these ideas to improve your sales and leads by a huge margin at a low cost.
MeasureCamp V London training: Data VisualisationSean Burton
Data are beautiful… True of your spreadsheets?
This session looked at how to present data in an informative and insightful way – beautiful without the gimmicks!
Measurecamp 6 Workshop: Data IntegrationSean Burton
This document summarizes a presentation on data integration. It discusses collecting data from different sources like web analytics, surveys, CRM and combining it to understand the customer experience. It provides examples of integrating web analytics and survey data in Google Analytics using custom dimensions. Integrating these data sources can provide insights into who customers are, their needs, and how to improve processes based on where customers drop off or leave feedback.
This document profiles Sean Burton, the Director of Measurement at Analytic. It provides details about Sean's background including 15 years of experience in eLearning, content management, interaction design, product management, web analytics, and data visualization. It also lists some of the brands Sean has worked with, which come from financial, telecommunications, gaming, and retail sectors. The document advocates for measuring the customer experience using a blend of data, technology, and psychology to improve business value.
The document discusses measuring and improving customer experience. It provides examples of how three different organizations - an electricity company, life assurance company, and train franchise - used a simple tool to map customer touchpoints, measure customer experience scores internally and externally, identify areas for improvement, and implement quick wins to improve the customer experience and retention. The tool helped reveal gaps between staff and customer perceptions, and showed that improving communications and information provision could have a significant impact on experience scores at a lower cost than other options like solely focusing on punctuality. Measuring customer experience provides a better way to improve retention than only measuring customer satisfaction.
Analytics for Customer Acquisition - Presentation at Nasscom Product Conclave...Arun Agrawal
Don't jump into Google Analytics without defining your KPIs first. Set your targets and analyse with this guide.
Includes strategies and tactics to solve the low traffic and low web site conversion problems. Apply these ideas to improve your sales and leads by a huge margin at a low cost.
Measurecamp 7 Workshop: Data VisualisationSean Burton
This document summarizes a presentation on data visualization and dashboard design. It includes an introduction to the presenter and overview of topics to be covered. Examples of effective and ineffective visualizations are provided to demonstrate best practices. Guidance is given on using appropriate scales and chunking information. Interactive exercises engage attendees in visualization design. Overall the presentation aims to teach best practices for designing visualizations and dashboards that clearly and meaningfully communicate data through simple, interactive, and contextual designs.
This presentation was for Social Media Week Berlin on Tuesday, 24th September. It was targeted at NGOs, NPOs, activist organisations and charities who have important key messages to share with the community. The event will combine elements of a presentation and workshop. We will examine case studies of campaigns that have successfully used data visualisation in tandem with social media and content marketing techniques to spread information and ideas, and to counteract prevailing myths about climate change and renewable energy technology. We will then allow time for participants to split up into small working groups. Structured discussion tasks and group feedback will allow participants to investigate how these strategies can apply to their own organisation or issue. Participants will learn practical steps for identifying important messages, researching and developing content, incorporating data visualisation in a powerful and meaningful way, and promoting their data visualisation campaigns through social media and email outreach. In particular, the event will focus on developing powerful stories that will attract the support of influential sharers and thought leaders from a range of backgrounds, from activism through to industry, so as to maximise the campaign's reach and impact.
This document discusses data visualization techniques. It begins by defining data visualization and its importance for analyzing large datasets. It then discusses the advantages of data visualization, including how visuals help people quickly understand trends and outliers. The document also covers the importance of data visualization for business decision making. It lists several benefits, such as enabling better analysis, identifying patterns, and exploring insights. Finally, it categorizes and provides examples of different types of charts for visualizing data, including charts for showing change over time, comparing categories, ranking items, part-to-whole relationships, distributions, flows, and relationships.
Introduction to information visualisation for humanities PhDsMia
Training workshop for the CHASE Arts and Humanities in the Digital Age programme. (
This session will give you an overview of a variety of techniques and tools available for data visualisation and analysis in the humanities. You will learn about common types of visualisations and the role of exploratory and explanatory visualisations, explore examples of scholarly visualisations, try some visualisation tools, and know where to find further information about analysing and building data visualisations.
Guidelines for data visualisation: eye vegetables and eye candyJen Stirrup
What's your data visualization vegetables? What's your candy? This session will look at data visualization theory and practice of hot data visualization topics such as: how can you choose which chart to choose and when?
How can you best structure your dashboard?
What about pie charts? What is the fuss about, and when are they best used?
Color blindness - how can you cater for the 1 out of 12 color blind males (and not forgetting the 1 out of 100 color blind females?)
To 3D or not to 3D? Why is it missing in Power View? And any other data visualization topics you care to mention! Come along for dataviz fun, and to learn the "why" along with practical advice.
Startupfest 2016: NOAH ILIINSKY (Amazon Web Services) - How to Startupfest
How To design effective visualizations (and other communications) -
This talk discusses the broad design considerations necessary for effective visualizations (as well as other types of communication). Attendees will learn what’s required for a visualization to be successful, gain insight for critically evaluating visualizations they encounter, and come away with new ways to think about the visualization design process.
4 pillars of visualization & communication by Noah Iliinskyiliinsky
A version of my standard "how to do visualization" talk from summer 2016. This version points out that the same process works for most modes of communication as well.
Digital analytics: Wrap-up (Lecture 12)Joni Salminen
The document provides information about a 3-week digital analytics program at Aalto University taught by Dr. Joni Salminen. The first week introduces basics of analytics using Google Analytics and covers metrics and dashboards. The second and third weeks focus on optimization, A/B testing, cohort analysis, visualization, and algorithm-based marketing. Students will learn to choose relevant metrics, manage analytics projects, perform website audits, and make better business decisions using data. The document emphasizes learning tools like Google Analytics, Tableau, and R, and continuing education after the program.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
This document outlines an assignment to create an infographic for a client. Students will write a 1-page brief discussing the purpose and goals of the infographic. They will then create the infographic, focusing on statistics, processes, timelines, ideas, or geography related to the client. The infographic must include visuals, charts, and citations to research. A grading rubric evaluates the brief, infographic, and research components. Tips are provided on determining the purpose, targeting audiences, sourcing data, citing references, and finding design inspiration.
This document provides an overview of the origins and early development of graphs and charts. It discusses how many common chart types, such as bar charts, line charts and pie charts, were first invented in the late 18th century by William Playfair to visualize large amounts of economic and social data. It also describes some other key contributors in the following decades who helped advance different chart types, such as Joseph Priestley inventing the timeline, Charles Dupin creating the first choropleth map, and John Snow using a map to trace the source of a cholera outbreak in London. Overall, the document traces how charts have evolved from their early inventions to become ubiquitous tools for visualizing data today.
The document provides an overview of digital analytics and Google Analytics. It discusses why analytics is useful for solving problems like Wanamaker's dilemma of not knowing which advertising channels are effective. It explains how analytics works by collecting anonymous data on user behavior and interactions with a website and turning that raw data into useful reports and metrics. Key aspects of Google Analytics that are covered include its data model of users, sessions, and interactions. The document also discusses segmentation, internal vs external analytics, and how analytics can be used for reporting, optimization, and strategic decision making.
This document discusses visual analytics and big data visualization. It defines big data and explains the need for big data analytics to uncover patterns. Data visualization helps make sense of large datasets and facilitates predictive analysis. Different visualization techniques are described, including charts, graphs, and diagrams suited to simple and big data. Visualization acts as an interface between data storage and users. Characteristics of good visualization and tools for big data visualization are also outlined.
BigData Visualization and Usecase@TDGA-Stelligence-11july2019-sharestelligence
This document provides an overview of data visualization and big data. It begins with the speaker's contact information and experience in data science. It then discusses data visualization techniques and history. Common data sources for big data are described along with an analytic framework. Examples of using big data in Thailand and for the public sector are given. Finally, the document proposes a workshop on a big data use case to reduce electricity billing costs and discusses analyzing water pipe leaks.
eMetrics London 2015: Getting data visualisation to workSean Burton
The document profiles Sean Burton, an expert in customer experience and data analytics. It provides information on Sean's background and experience in fields like web analytics, data visualization, and user experience design. It also describes the services offered by Sean's company, Analyt, which provides consulting services to optimize the customer experience through a blended approach of data, technology, and psychology.
eMetrics London 2015 - Getting data visualisation to workSean Burton
This talk (35 mins) looks at how to get data visualisations, and particularly dashboards, to be successful.
Learn how to craft the perfect KPI & how best to display them based on how we, as humans, perceive & process information.
The document provides an overview of data science and what it entails. It discusses the hype around big data and data science, and how data science has evolved due to improvements in technology that allow for large-scale data processing. It defines data science as a process that involves collecting, cleaning, analyzing and extracting meaningful insights from data. Data scientists come from a variety of academic backgrounds and work in both industry and academia developing solutions to real-world problems using data-driven approaches.
Understanding big data and data analytics-Business IntelligenceSeta Wicaksana
The document provides an overview of understanding big data and data analytics, including business intelligence, analytics, and visualization. It discusses the evolution of business intelligence and analytics, from descriptive analytics describing what has occurred to predictive analytics predicting what will occur and prescriptive analytics determining what should occur. It also covers topics like data mining, market basket analysis, cluster analysis, and the importance of visualization for extracting insights from data.
Data Visualization dataviz superpower! Guidelines on using best practice data visualization principles for Power BI, Excel, SSRS, Tableau and other great tools!
Become a Better Data Analyst with Tableau - Charlotte TUGSarah Bartlett
This document provides tips for becoming a better analyst using Tableau. It outlines 9 key skills: 1) Master the basics by utilizing free training resources. 2) Practice regularly by choosing learning projects. 3) Ask the right questions of data to understand its limits. 4) Study design fundamentals. 5) Publish work to gain feedback. 6) Engage with the Tableau community. 7) Get certified to prove skills. 8) Leverage community resources like blogs. 9) Teach others to share knowledge. Regular practice, publishing work, and engaging with the community are emphasized as important ways to refine skills.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Measurecamp 7 Workshop: Data VisualisationSean Burton
This document summarizes a presentation on data visualization and dashboard design. It includes an introduction to the presenter and overview of topics to be covered. Examples of effective and ineffective visualizations are provided to demonstrate best practices. Guidance is given on using appropriate scales and chunking information. Interactive exercises engage attendees in visualization design. Overall the presentation aims to teach best practices for designing visualizations and dashboards that clearly and meaningfully communicate data through simple, interactive, and contextual designs.
This presentation was for Social Media Week Berlin on Tuesday, 24th September. It was targeted at NGOs, NPOs, activist organisations and charities who have important key messages to share with the community. The event will combine elements of a presentation and workshop. We will examine case studies of campaigns that have successfully used data visualisation in tandem with social media and content marketing techniques to spread information and ideas, and to counteract prevailing myths about climate change and renewable energy technology. We will then allow time for participants to split up into small working groups. Structured discussion tasks and group feedback will allow participants to investigate how these strategies can apply to their own organisation or issue. Participants will learn practical steps for identifying important messages, researching and developing content, incorporating data visualisation in a powerful and meaningful way, and promoting their data visualisation campaigns through social media and email outreach. In particular, the event will focus on developing powerful stories that will attract the support of influential sharers and thought leaders from a range of backgrounds, from activism through to industry, so as to maximise the campaign's reach and impact.
This document discusses data visualization techniques. It begins by defining data visualization and its importance for analyzing large datasets. It then discusses the advantages of data visualization, including how visuals help people quickly understand trends and outliers. The document also covers the importance of data visualization for business decision making. It lists several benefits, such as enabling better analysis, identifying patterns, and exploring insights. Finally, it categorizes and provides examples of different types of charts for visualizing data, including charts for showing change over time, comparing categories, ranking items, part-to-whole relationships, distributions, flows, and relationships.
Introduction to information visualisation for humanities PhDsMia
Training workshop for the CHASE Arts and Humanities in the Digital Age programme. (
This session will give you an overview of a variety of techniques and tools available for data visualisation and analysis in the humanities. You will learn about common types of visualisations and the role of exploratory and explanatory visualisations, explore examples of scholarly visualisations, try some visualisation tools, and know where to find further information about analysing and building data visualisations.
Guidelines for data visualisation: eye vegetables and eye candyJen Stirrup
What's your data visualization vegetables? What's your candy? This session will look at data visualization theory and practice of hot data visualization topics such as: how can you choose which chart to choose and when?
How can you best structure your dashboard?
What about pie charts? What is the fuss about, and when are they best used?
Color blindness - how can you cater for the 1 out of 12 color blind males (and not forgetting the 1 out of 100 color blind females?)
To 3D or not to 3D? Why is it missing in Power View? And any other data visualization topics you care to mention! Come along for dataviz fun, and to learn the "why" along with practical advice.
Startupfest 2016: NOAH ILIINSKY (Amazon Web Services) - How to Startupfest
How To design effective visualizations (and other communications) -
This talk discusses the broad design considerations necessary for effective visualizations (as well as other types of communication). Attendees will learn what’s required for a visualization to be successful, gain insight for critically evaluating visualizations they encounter, and come away with new ways to think about the visualization design process.
4 pillars of visualization & communication by Noah Iliinskyiliinsky
A version of my standard "how to do visualization" talk from summer 2016. This version points out that the same process works for most modes of communication as well.
Digital analytics: Wrap-up (Lecture 12)Joni Salminen
The document provides information about a 3-week digital analytics program at Aalto University taught by Dr. Joni Salminen. The first week introduces basics of analytics using Google Analytics and covers metrics and dashboards. The second and third weeks focus on optimization, A/B testing, cohort analysis, visualization, and algorithm-based marketing. Students will learn to choose relevant metrics, manage analytics projects, perform website audits, and make better business decisions using data. The document emphasizes learning tools like Google Analytics, Tableau, and R, and continuing education after the program.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
This document outlines an assignment to create an infographic for a client. Students will write a 1-page brief discussing the purpose and goals of the infographic. They will then create the infographic, focusing on statistics, processes, timelines, ideas, or geography related to the client. The infographic must include visuals, charts, and citations to research. A grading rubric evaluates the brief, infographic, and research components. Tips are provided on determining the purpose, targeting audiences, sourcing data, citing references, and finding design inspiration.
This document provides an overview of the origins and early development of graphs and charts. It discusses how many common chart types, such as bar charts, line charts and pie charts, were first invented in the late 18th century by William Playfair to visualize large amounts of economic and social data. It also describes some other key contributors in the following decades who helped advance different chart types, such as Joseph Priestley inventing the timeline, Charles Dupin creating the first choropleth map, and John Snow using a map to trace the source of a cholera outbreak in London. Overall, the document traces how charts have evolved from their early inventions to become ubiquitous tools for visualizing data today.
The document provides an overview of digital analytics and Google Analytics. It discusses why analytics is useful for solving problems like Wanamaker's dilemma of not knowing which advertising channels are effective. It explains how analytics works by collecting anonymous data on user behavior and interactions with a website and turning that raw data into useful reports and metrics. Key aspects of Google Analytics that are covered include its data model of users, sessions, and interactions. The document also discusses segmentation, internal vs external analytics, and how analytics can be used for reporting, optimization, and strategic decision making.
This document discusses visual analytics and big data visualization. It defines big data and explains the need for big data analytics to uncover patterns. Data visualization helps make sense of large datasets and facilitates predictive analysis. Different visualization techniques are described, including charts, graphs, and diagrams suited to simple and big data. Visualization acts as an interface between data storage and users. Characteristics of good visualization and tools for big data visualization are also outlined.
BigData Visualization and Usecase@TDGA-Stelligence-11july2019-sharestelligence
This document provides an overview of data visualization and big data. It begins with the speaker's contact information and experience in data science. It then discusses data visualization techniques and history. Common data sources for big data are described along with an analytic framework. Examples of using big data in Thailand and for the public sector are given. Finally, the document proposes a workshop on a big data use case to reduce electricity billing costs and discusses analyzing water pipe leaks.
eMetrics London 2015: Getting data visualisation to workSean Burton
The document profiles Sean Burton, an expert in customer experience and data analytics. It provides information on Sean's background and experience in fields like web analytics, data visualization, and user experience design. It also describes the services offered by Sean's company, Analyt, which provides consulting services to optimize the customer experience through a blended approach of data, technology, and psychology.
eMetrics London 2015 - Getting data visualisation to workSean Burton
This talk (35 mins) looks at how to get data visualisations, and particularly dashboards, to be successful.
Learn how to craft the perfect KPI & how best to display them based on how we, as humans, perceive & process information.
The document provides an overview of data science and what it entails. It discusses the hype around big data and data science, and how data science has evolved due to improvements in technology that allow for large-scale data processing. It defines data science as a process that involves collecting, cleaning, analyzing and extracting meaningful insights from data. Data scientists come from a variety of academic backgrounds and work in both industry and academia developing solutions to real-world problems using data-driven approaches.
Understanding big data and data analytics-Business IntelligenceSeta Wicaksana
The document provides an overview of understanding big data and data analytics, including business intelligence, analytics, and visualization. It discusses the evolution of business intelligence and analytics, from descriptive analytics describing what has occurred to predictive analytics predicting what will occur and prescriptive analytics determining what should occur. It also covers topics like data mining, market basket analysis, cluster analysis, and the importance of visualization for extracting insights from data.
Data Visualization dataviz superpower! Guidelines on using best practice data visualization principles for Power BI, Excel, SSRS, Tableau and other great tools!
Become a Better Data Analyst with Tableau - Charlotte TUGSarah Bartlett
This document provides tips for becoming a better analyst using Tableau. It outlines 9 key skills: 1) Master the basics by utilizing free training resources. 2) Practice regularly by choosing learning projects. 3) Ask the right questions of data to understand its limits. 4) Study design fundamentals. 5) Publish work to gain feedback. 6) Engage with the Tableau community. 7) Get certified to prove skills. 8) Leverage community resources like blogs. 9) Teach others to share knowledge. Regular practice, publishing work, and engaging with the community are emphasized as important ways to refine skills.
Similar to Measurecamp 6 Workshop: Data Visualisation (20)
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We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
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3. Overview
• A bit about me…
• Who’s in the room…
• Some background…
• Getting started…
• Exercise!
• Bringing it all together…
• Next steps…
• The wrap up.
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
4. Intros…
Sean Burton
sean@analyt.co.uk | @sean_d_burton & @analytdata | analyt.co.uk
I'm passionate about improving customer experience and business value by using a
blend of data, technology and psychology.
About me:
• Formerly the Director of Measurement at Seren Design Ltd.
• A 15 year career covering: eLearning, Content Management Systems, Interaction
Design, Product Management, Web Analytics, and Data Visualisation.
• Extensive experience with FTSE 100 companies across financial,
telecommunication, gaming, and retail sectors.
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
16. Perception: Beauty – Fibonacci & the Golden Ratio
• Finonacci
• 0 0 1 1 2 3 5 8 13 21 …
• Each number is the sum of the preceding two numbers
• Equates to a ratio of 1:1.618033987
• The Golden Ratio (Divine proportion, Golden Mean, or Phi) refers to the fact
that this ratio appears repeatedly in nature as well as works of art
• Constructal Law (Bejan, 1996 (http://constructal.org/)):
• “The eye scans an image the fastest when it is shaped as a golden ratio rectangle.”
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
18. Perception: Working Memory: 7 ±2
• Theory that “the number of objects an average human can hold
in working memory is 7 ± 2”
• From the paper “The Magical Number Seven, Plus or Minus Two: Some
Limits on Our Capacity for Processing Information” by George Miller
1956.
• ‘Chunking’ allows for people to apply meaning to individual objects to
group them together making them easier to remember.
• Cowan (2001) has proposed that working memory has a capacity of
about four chunks in young adults.
• Allowing audience to get the gist will significantly aid retension
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
36. Data Types and how to use them
• Nominal Scale
• Clustering or grouping
• Ordinal Scale
• Ranked
• Interval Scale
• Allows for the degree of
difference between items
• Ratio Scale
• Referenced against a non-
arbitrary zero, e.g. absolute
zero. Basically means ‘how
much’ or ‘how many’.
*Theory of typology – Stevens 1946 (On the theory of scales and measurement, Science)0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
39. Exercise
• Get into groups of 3 or 4…
• Plan out a visualisation of the other groups in terms of: name, age,
gender, job role, etc. (5 mins)
• Draw appropriate charts to tell the story of the group (5 mins)
• Present back (5 mins each group)
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
45. Everything should be made
as simple as possible but
not simpler.
“
”
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
46. Ethos of Design
• Simple but not simplistic
• Visualisations should be sophisticated without being complex.
• Less is often more!
• Interactive and meaningful
• Goal is to make data tangible/tactile so that the end user can relate to it easily,
view it from a different perspective, and gleam insight.
• Context, Context, Context!
• Balance of form and function
• Every element of the visualisation must have purpose, however the aesthetic
must also be maintained to retain emotional connection.
• it’s all about visual patterns
• Tell a story
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
47. Ethos of Design
• Audience.
• Who are you writing for? The general public will have a different level of expertise to statistical specialists, just as
a school textbook will have different requirements to a scientific journal. If you are unsure, aim your work at a less
specialist audience.
• Purpose.
• What will the data be used for? If they are intended for reference and further calculation you might present them
differently to if you are demonstrating a particular fact. In practice it is usually only tables that are effective for
presenting reference material.
• Clarity.
• Will people understand what you're showing? A specialist audience may allow you to use more complex and
unusual presentation techniques, but you should still aim to present the data clearly and correctly.
• Medium.
• Will the data appear in a book or on a website? A large table or graphic might work fine on paper but be less
suitable online if it forces users to scroll around. On the other hand, online technology might allow you to make
the data interactive in a way that would be impossible on paper. Note that although many aspects of good
practice apply to all media, these guidance notes are primarily targeted at static information suitable for print.
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
48. Ethos of Design
• Relevance.
• Avoid unnecessary data. Don't put extra variables in a table, or extra features on a map just because you think they're
interesting. Will they be useful to the reader? If not, you probably don't need them.
• Ink to data ratio.
• If there's ink on the page which doesn't add to the description or interpretation of data you should ask yourself whether it's
necessary. Whilst some lines and annotations can make things clearer and add visual appeal, too many add clutter. Things to
avoid include drawing horizontal lines between every row or column in a table, or drawing too many gridlines on a chart.
• Colour association.
• This applies to charts and particularly maps. Most people associate red with Labour and blue with Conservative, for example, so
producing a chart where the colours of the bars are reversed would be confusing. Similarly, on a health map, areas with high
levels of a particular disease should normally be coloured darker.
• Colour recognition.
• Consider too the suitability of your colour choice for colour-blind people - http://www.vischeck.com is an interesting way of
checking. Also think of the implications if people are likely to photocopy your work, or if they use a black and white printer.
• Format.
• Remember that for demonstration (explanatory) purposes, a combination of presentation methods is often best. Specifically, your
tables, charts and maps should be accompanied by text.
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
50. Simplicity
• Drop background as it delivers nothing of value
• Remove pointless decimals from vertical scale
• Place data labels with data series, and remove legend
• Retain gridlines but reduce their prominence
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
51. NSO Tips: (Excel) chart formatting
• Apply sound design principles;
• Use colour strategically: mute axis and grid lines by greying them out; grey out some contextual data also; use
soft colours; use saturated colours sparingly and with a clear purpose of emphasis;
• What the users see is not what you see in your monitor: if needed, test for other monitors and output
formats (b&w print, colour print, PDF, overhead projector);
• There is no rational justification to use pseudo-3D charts and other dubious effects(gradients, glow…), so never
use them if you what to be rational;
• Use a clear font;
• Don’t emphasize everything (for obvious reasons);
• The y axis scale should start at zero; this is particularly important if you are using bar charts; make sure you
have a good reason to break this rule;
• A chart is not a table: by labelling every single data point you make it harder for the user to search for trends or
patterns; if you have to, place the labels where they can do no harm;
• Annotate: Add labels for the last, the lowest, the highest or any other relevant data point; add data or comments
where appropriate;
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52. NSO Tips: (Excel) column and bar charts
• A column chart is not a skyline: if you can’t see the individual patterns, consider removing some series or create several smaller
charts;
• If you are charting categorical data sort the columns; if there is more than one series, allow the user to sort the data;
• If you are displaying time series, column charts are not interchangeable with line charts: column charts allow you to compare individual
data points, while a line chart shows the trend; be sure to select what your audience wants to see;
• For target/actual series (like budget/actual) overlap them but make sure they can’t be taken for stacked bars; you can do it by using a
different column width for each series or by setting filling to none (usually the target series);
• Use horizontal bar charts when x labels are too large to be correctly displayed;
• The y axis scale should start at zero; this is particularly important if you are using bar charts; make sure you have a (very) good reason
to break this rule;
• If you really need to label each column try to minimize its impact; in Excel 2003, select Format Data Labels / Alignment / Label
Position: Inside Base;
• Don’t use multiple colours for a single data series;
• Avoid stacked bar charts;
• Use category/subcategory to label the x axis. For example, instead of having Mar-2008, Apr-2008… use Mar, Apr and place 2008 in
the second line.
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53. NSO Tips: (Excel) Line charts
• Don’t use line markers unless you really need them to identify b&w printed charts;
• Don’t use a legend; directly label the series, instead;
• If you can’t easily see the pattern of each series you may have too many;
• In a time series, the spacing between markers in the x-axis should be proportional. For example, if
you have data for years 1980, 1990, 2000 and 2008, the spacing between 2000 and 2008 should be
smaller than between other dates; if you can’t do it with line charts use a scatter plot;
• If you are comparing two series like imports/exports or profit/expenses, chart the differences, not the
actual series (or at least add a small chart with the differences, below the main chart;
• If you are comparing two time series with very different units of measurement, consider using a
logarithmic scale;
• You don’t have to start the Y-axis scale at zero; break the scale if you need;
• If you are using different line styles you may be emphasizing some series more than the others;
make sure that’s consistent with your users needs (emphasize what is important);
• Add a trend line (make sure the trend is plausible…);
• Don’t use line charts for categorical data; if you need a profile chart use a scatter plot and switch
axis.
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54. NSO Tips: (Excel) pie charts
• Do you really need a pie chart?
• Pie charts shouldn’t be compared (comparing market shares in two regions, for example);
• Don’t use the “exploded” option;
• Five is in general the maximum number of slices you can use in a pie chart, but two is
better…;
• If there is no other meaningful order, order the slices from maximum to minimum;
• Put “other” in a grey slice;
• Don’t use a legend, just label the slices;
• Use a very small pie chart in a supporting role for a more complex chart;
• Use the appropriate colour codes to identify groups of slices;
• Start the first slice at 0º (noon);
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56. …but as Albert said…
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57. Good KPIs are “Übermetrics”…
Good KPI
Strategic
measures of
success
Actionable
Easy to
understand
Based on
valid data
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58. Components of a good dashboard
Appropriate real-time information
Warning lights
and graphics
Capacity and
current levels
Relevant historic data
Key information displayed clearly
Ability to adjust metrics through action
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59. Dashboard development process
Requirements
analysis
•Interviews with
stakeholders
Data and systems
review
•Review data sources
•Review current
reports
•Review reporting
systems
Design
•Conceptual reporting
model
•Data model
•Dashboard wireframes
•Mock ups
Prototype
•Dashboard design and
prototyping
•Reporting technology
selection
Automation
•Production systems
•Dissemination
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61. Dashboards customised to desired
reporting periods.
Commentary section to allow additional context for
known events or insight.
KPIs requiring
attention are clearly
highlighted.
Sparklines are used to give trended
view of relevant metric.
Each metric is shown in context to
the last reporting period and to the
average over last year.
Example Dashboard
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64. Dashboards: 5 key elements
• Relevance
• Make sure you’re showing the right stuff to the right
person at the right time!
• Context
• Try to ‘ground’ each metric, by showing: the metric, it’s
trend; and a comparator
• Also think about other associated metrics
• Colour
• Use sparingly, e.g. only red for alerts
• Don’t depend on the colour to convey meaning – couple
with an icon, e.g. green up-arrow vs red down-arrow.
• Story
• Try to configure your dashboard to tell a story. Most
people read top-left to bottom-right – try to layout metrics
accordingly
• Aesthetic
• Be driven by the function and not the form. Tailor your
design to your audience, you don’t want an exec to be
put off your dashboard simply because it’s ugly!
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66. Dashboards: Excel, PowerPoint and the web
• PowerPoint is great for mocking up dashboards and testing navigation
designs.
• VBA within PowerPoint can result in dynamically built slides, pulling new
data directly from Google Analytics and other sources
• Excel is massively powerful and doesn’t have to boring!
• (show examples
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68. A few helpful links…
• Data vis tools
• Datawrapper
• Infogr.am
• PiktoChart
• Google Fusion Tables
• Visumap & Ggobi (High-dimensionality data
visualisation)
• http://supermetrics.com/
• Web libraries
• Chartjs (http://www.chartjs.org/)
• D3 (http://d3js.org/) and DC (http://dc-js.github.io/dc.js/)
• Examples for inspiration
• http://dadaviz.com/i/851
• Golden Ration
• http://www.hongkiat.com/blog/golden-ratio-in-moden-
designs/
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
A couple of great books:
• The Visual Display of Quantitative Information (Edward
Tufte)http://www.amazon.co.uk/gp/product/0961392142/r
ef=oh_aui_detailpage_o06_s00?ie=UTF8&psc=1
• Information Dashboard Design (Stephen
Few)http://www.amazon.co.uk/gp/product/1938377001/re
f=oh_aui_detailpage_o06_s00?ie=UTF8&psc=1