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!
Predictive Conversion Modeling - Lifting Web Analytics to the next levelPetri Mertanen
Annalect presentation at Superweek 2017: Predictive Conversion Modeling - Lifting Web Analytics to the next level. Presented by Petri Mertanen, Director of Digital Analytics and Ron Luhtanen, Data Science Analyst. #SPWK
Predictive Conversion Modeling - Lifting Web Analytics to the next levelPetri Mertanen
Annalect presentation at Superweek 2017: Predictive Conversion Modeling - Lifting Web Analytics to the next level. Presented by Petri Mertanen, Director of Digital Analytics and Ron Luhtanen, Data Science Analyst. #SPWK
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.
UX Conversion Camp: Aldermore Bank, Making Corporate UX WorkLisa Duddington MSc
UX Conversion Camp is the UK's only brand-only conversion event, organised by Keep It Usable. 2017 was the best yet! Here are the slides from our fantastic presenters, Aldermore Bank.
For more information on UXCC, please visit www.uxconversioncamp.com
UX Conversion Camp is the UK's only brand-only conversion event, organised by Keep It Usable. 2017 was the best yet! Here are the slides from our fantastic presenter, Karl Rowlands of Matalan. For more information on UXCC, please visit www.uxconversioncamp.com
UX Conversion Camp is the UK's only brand-only conversion event, organised by Keep It Usable. 2017 was the best yet! Here are the slides from our fantastic presenter, Kate Rylance, Shop Direct. For more information on UXCC, please visit www.uxconversioncamp.com
The five essential steps to building a data productBirst
Building a data-driven product is scary business. You need to get the right platform both for today’s needs and for tomorrow’s possibilities – and then, you need to go beyond the technical to build a go-to-market plan that will set you up for success. Learn the five keys to building a great analytical product from someone who has done it before — and failed! Hear Kevin Smith speak about the mistakes he’s made building data products and how you can benefit from his lessons learned.
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.
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.
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.
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.
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.
UX Conversion Camp: Aldermore Bank, Making Corporate UX WorkLisa Duddington MSc
UX Conversion Camp is the UK's only brand-only conversion event, organised by Keep It Usable. 2017 was the best yet! Here are the slides from our fantastic presenters, Aldermore Bank.
For more information on UXCC, please visit www.uxconversioncamp.com
UX Conversion Camp is the UK's only brand-only conversion event, organised by Keep It Usable. 2017 was the best yet! Here are the slides from our fantastic presenter, Karl Rowlands of Matalan. For more information on UXCC, please visit www.uxconversioncamp.com
UX Conversion Camp is the UK's only brand-only conversion event, organised by Keep It Usable. 2017 was the best yet! Here are the slides from our fantastic presenter, Kate Rylance, Shop Direct. For more information on UXCC, please visit www.uxconversioncamp.com
The five essential steps to building a data productBirst
Building a data-driven product is scary business. You need to get the right platform both for today’s needs and for tomorrow’s possibilities – and then, you need to go beyond the technical to build a go-to-market plan that will set you up for success. Learn the five keys to building a great analytical product from someone who has done it before — and failed! Hear Kevin Smith speak about the mistakes he’s made building data products and how you can benefit from his lessons learned.
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.
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.
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.
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.
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.
Presented on May 7, 2015 to the TechChange Technology for M&E course. The aim of the presentation was to highlight key considerations in designing visualizations as part of international development programs, and includes both challenges of visualization in development programs and six things to consider when designing visualizations.
eMetrics London 2015: Getting data visualisation to workSean Burton
This talk (35mins) was presented at the eMetrics summit in London 2015 and covers the core principles for creating successful data visualisation - specifically dashboards.
Learn how to craft the perfect KPIs & how best to show them
Understanding how we perceive & process information helps us to build better dashboards
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.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
3. Intros…
Sean Burton
sean@analyt.co.uk | @sean_d_burton | 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
13. 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
15. 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
33. 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 0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analy tsdcaatales and measurement, Science)
36. 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
42. not simpler. “
Everything should be made
as simple as possible but
”
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
43. 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
44. 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
45. 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
47. 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
48. 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;
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
49. 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 smal ler
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.
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
50. 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.
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
51. 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);
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
53. …but as Albert said…
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
54. Good KPIs are “Übermetrics”…
Good KPI
Strategic
measures of
success
Actionable
Easy to
understand
Based on
valid data
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
55. 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
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
56. 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
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
58. 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
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
61. 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!
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
63. 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
0191 704 2045 | analyt.co.uk | info@analyt.co.uk | @analytdata
65. 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
66. info@analyt.co.uk email
twitter @analytdata
analyt.co.uk web
Training Feedback
http://www.surveygizmo.com/s3/1800143/MeasureCamp-V-Training