Books by Stephen Few

A look at all of  Stephen Few's books on the viz and use  of data, including his latest:

The Data Loom, Stephen Few, $15.95 (U.S.), Analytics Press, May 15, 2019

Data, in and of itself, isn't valuable. It only becomes valuable when we make sense of it. Weaving data into understanding involves several distinct but complementary thinking skills. Foremost among them are critical thinking and scientific thinking. Until information professionals develop these capabilities, we will remain in the dark ages of data. If you're an information professional and have never been trained to think critically and scientifically with data, this book will set your feet on the path that will lead to an Information Age worthy of the name. ... " 

Stephen Few on Logarithms

Data viz expert Stephen Few on Logarithms.   I recall very early in my experience in the enterprise,  having to work with execs on this concept and how it could improve their understanding of visual measures, but also confuse them.  Here a considerable and interesting view.  'Sensemaking' is a good term here.

Logarithms Unmuddled  by Stephen Few

I often write about topics that I myself have struggled to understand. If I’ve struggled, I assume that many others have struggled as well. Over the years, I’ve found several mathematical concepts confusing, not because I’m mathematically disinclined or disinterested, but because my formal training in mathematics was rather limited and, in some cases, poorly taught. My formal training consisted solely of basic arithmetic in elementary school, basic algebra in middle school, basic geometry in high school, and an introductory statistics course in undergraduate school. When I was in school, I didn’t recognize the value of mathematics—at least not for my life. Later, once I became a data professional, a career that I stumbled into without much planning or preparation, I learned mathematical concepts on my own and on the run whenever the need arose. That wasn’t always easy, and it occasionally led to confusion. Like many mathematical topics, logarithms can be confusing, and they’re rarely explained in clear and accessible terms. How logarithms relate to logarithmic scales and logarithmic growth isn’t at all obvious. In this article, I’ll do my best to cut through the confusion.

Until recently, my understanding (and misunderstanding) of logarithms stemmed from limited encounters with the concept in my work. As a data professional who specialized in data visualization, my knowledge of logarithms consisted primarily of three facts:

Along logarithmic scales, each labeled value that typically appears along the scale is a consistent multiple of the previous value (e.g., multiples of 10 resulting in a scale such as 1, 10, 100, 1,000, 10,000, etc.).

Logarithmic scales make it easy to compare rates of change in line graphs because equal slopes represent equal rates of change.

Logarithmic growth exhibits a pattern that goes up by a constantly decreasing amount.
If you, like me, became involved in data sensemaking (a.k.a., data analysis, business intelligence, 
analytics, data science, so-called Big Data, etc.) with a meagre foundation in mathematics, your understanding of logarithms might be similar to mine—similarly limited and confused. For example, if you think that the sequence of values 1, 10, 100, 1,000, 10,000, and so on is a sequence of logarithms, you’re mistaken, and should definitely read on.

Before reading on, however, I invite you to take a few minutes to write a definition for each of the following concepts:   ... " 

Tell me a Story

Data visualization expert Stephen Few makes a point I have made for a long time.  And does not take it far enough.  Telling a story is great, powerful, expressive and convincing.  So use that technique if you like.  But storytelling methods can just as well lead you to a narrative that is not in the data.

Better to let someone  interact with data to tell the story they see, rather than wrap a preformed narrative around it.  The latter happens too often, have seen it many times in the well funded and staffed  enterprise.  People will actively look for data to confirm their bias, their story ... then further select data to confirm the model.  Dangerous way to model.

Mismeasure of Uncertainty

Stephen Few thoughtfully reviews Willful Ignorance:  The Mismeasure of Uncertainty, by Herbert Weisberg.

Modern science relies heavily on an approach to the assessment of uncertainty that is too narrow. Scientists rely on statistical measures of significance to establish the merits of their findings, often without fully understanding the limitations of those statistics and the original intentions for their use. P-values and even confidence intervals are cited as stamps of approval for studies that are meaningless and of no real value. Researchers strive to reach significance thresholds as if that were the goal, rather than the addition of useful knowledge. In his book Willful Ignorance: The Mismeasure of Uncertainty, Herbert I. Weisberg, PhD, describes this impediment to science and suggests solutions. ... " 

Stephen Few on Big Data Reality

A lengthy and thoughtful piece by visualization expert Stephen Few.  He concludes and makes some excellent points .  People still make most data based decisions in context:

" ... I make my living helping people understand and communicate information derived from data, so Big Data has produced a greater appreciation for my work. Here’s the rub: Big Data, as a term with no clear definition, which serves as a marketing campaign for technology vendors, encourages people to put their faith in technologies without first developing the skills that are needed to use those technologies. As a result, organizations waste their money and time chasing the latest so-called Big Data technologies—some useful, some not—to no effect because technologies can only augment the analytical abilities of humans; they cannot make up for our lack of skills or entirely replace our skills. Data is indeed a valuable resource, but only if we develop the skills to make sense of it and find within the vast and exponentially growing noise those relatively few signals that actually matter. Big Data doesn’t do this, people do—people who have taken the time to learn. ... "

Paths to Learning

Stephen Few reviews a recent book on the Science Learning.  A good topic, because we still have much to learn about learning.  Worth a look, I plan to read:  " ... Many of the strongly held and frequently espoused notions about learning practices (e.g., good study habits), which seem intuitive, are dead wrong. Scientific investigation into the learning brain has revealed a great deal, especially in recent years, but the findings seldom reach the teachers and learners who would benefit from them. Peter Brown, Henry L. Roediger III, and Mark A. McDaniel have responded to this problem in the form of a wonderful new book titled Make It Stick: The Science of Successful Learning (2014). ... ".  I do wonder if some of these methods can then be transferred over to machine learning?

Why Visualize Data?

Good piece by Stephen Few.    " .... We visualize quantitative data to perform three fundamental tasks in an effort to achieve three essential goals ... 
These three tasks are so fundamental to data visualization, I’ve long used them to define the term, as follows:  Data visualization is the use of visual representations to explore, make sense of, and communicate data. ... " .  Well worth reading the whole thing.  My view is that you should always use visualization as a first step for understanding data.  These days it is very easy to do.

Data Visualization Resources

A library site describing and linking to books, papers and white papers of data visualization expert Stephen Few.

Designing and Visualization Connected

Stephen Few on Design in the Real World.  Have discussed our experiments in this space recently.  He states " ... designer/teacher Victor Papanek, whose work I only recently discovered. You can read them yourself in Papanek’s important and thoughtful book entitled Design for the Real World (Academy Chicago Publishers). This is a true classic that all designers should read, especially those of us who design information displays. ...   "  Will take a closer look myself.

Deep Data Insights

Stephen Few reviews the book:   Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney.    Taking a look myself.  " ... Data Insights is not a “how to” book in the sense of comprehensive instruction in the principles and practices of data visualization, but more of a philosophical grounding in the thinking that a data sensemaker must have to make the journey from data to insights. ... " 

Information Dashboard Design

Newly arrived to support a project:  Information Dashboard Design: Displaying Data for At-a-Glance Monitoring  by Stephen Few. I will review in the future.  Their description:

" ... Stephen Few exposes the common problems in dashboard design and describes its best practices in great detail and with a multitude of examples in this updated second edition. According to the author, dashboards have become a popular means to present critical information at a glance, yet few do so effectively. He purports that when designed well, dashboards engage the power of visual perception to communicate a dense collection of information efficiently and with exceptional clarity and that visual design skills that address the unique challenges of dashboards are not intuitive but rather learned. The book not only teaches how to design dashboards but also gives a deep understanding of the concepts—rooted in brain science—that explain the why behind the how. This revised edition offers six new chapters with sections that focus on fundamental considerations while assessing requirements, in-depth instruction in the design of bullet graphs and sparklines, and critical steps to follow during the design process. Examples of graphics and dashboards have been updated throughout, including additional samples of well-designed dashboards. ... "

To Save Everything, Click Here

Brought to my attention by Stephen Few, a new book:  To Save Everything Click Here: The Folly of Technological Solutionism, by Evgeny Morozov   His very positive review.   " ... Technologies are important. They give us opportunities to extend our reach and improve our world, but they also give us opportunities to do the opposite. Morozov understands this. He is not a Luddite, he’s a responsible technologist. I recommend that you consider what he has to say.. "

On the stack for later reading.

The Nature of the Data Scientist

Stephen Few writes  about the nature of the term 'data scientist'.  I personally don't like the term,  I prefer the term 'analyst', which has fewer implications of complexity. The term also is more of a grouping of technical and business interests meant to solve problems.  Needing a 'scientist' may be overdoing it.  Few does a good job of addressing the nature of the term.

Information Dashboard Design

Stephen Few is coming out with a new edition of his book: Dashboard Design.  He writes about it here.  Have not read it, but may be the time to do that. From his perspective, what is a dashboard?    He concisely describes it as " ... Displaying data for at-a-glance monitoring ... ".  In my view a form of analytics that seeks to involve the decision process user of the data as tightly as possible.

Data Held Hostage

From Stephen Few.  He makes some good points about the Big Data craze:

" ... The Big Data marketing campaign distracts us from our greatest opportunities involving data. As we chase the latest Big Data technologies to increase volume, velocity, and variety (the 3 V’s), we will never resolve the fundamental roadblocks that have been plaguing us all along. I’ve written a great deal over the last few years about the fundamental skills of data sensemaking and communication that are needed to evolve from the Data Age in which we live to the Information Age of our dreams. It is essential that we develop these basic skills, but we must face many other concerns and resolve them as well before collecting more data faster and in greater variety will matter. I recently wrote about one of those concerns in a blog post titled Big Data Disaster about the problems created by credit bureaus that shroud their scoring ... " 

Big Data Disaster, Get the Data Right.

Stephen Few on the problems with too much analytics before you get the data right.  Getting the data right.  And visualizing it to make sure you understand it .... is key.

Blog Tags are Here

Starting now you can utilize blog tags here, also called labels.  They are found at the bottom of each post.  These tags classify the blog post with others that are of related topics.  You can click on these at the bottom of the post to get all blog posts classified this way.  You can also use this syntax - label: tag in the search block at the upper left to search for particular categories. (case sensitive)

For multiple tags use label:tag1 label:tag2 (case sensitive).  Many past key blog posts, but not all, have been tagged.  All new posts from now on will be tagged.  Your comments are welcome.

Team Blogging Practices

When I ran a multiple internal blog operation in the enterprise, the goals were knowledge retention, sharing and collaboration.  We did both internal and external facing work.  This new article looks at the practices involved.  In particular as it addresses team operation.  We used a wiki style architecture for permanence.   Largely obvious, but worth the review.

Show Me the Numbers

I have been fully reading Stephen Few's book: Show me the Numbers: Designing Tables and Graphs to Enlighten.  (Second Edition).  In part for preparation of some data visualization design choices, and also because the topic of linking strong, accurate and well designed data visualization to business decisions has always been an interest of mine.

First of all bravo to this book.  Beautifully written and formatted. An excellent and easy to read book on the subject.  I rarely recommend reading such a 'how to' book in its entirely.  In this case it is well worth the actual time.  Few's teaching capability comes across quickly.

Also notable is his immediate linkage of visualization to analytics.  His chapter 2 outlines the simple statistics most often encountered in such work.   It's particularly useful to those without formal stat backgrounds.  It is by no means all the analytics the user should understand, but is an essential start.  The clear exposition that will get you 90% of what you need to support visualization.

Also unusual is the book's inclusion of an examination of the comparative use of tables and graphs.  Depending on the decisions being made, one or the other form of data display can be best to deliver your point clearly.  Its all about using the simplest design possible to minimize the work needed to get results.  It is remarkably similar to user interface design.   Few does not show designs for specific computer packages, but makes the case that many of the simpler approaches can be produced by packages like MS Excel.  He does criticize Excel as introducing too many obscuring concepts.    He also shows a number of Tableau Software visualization examples.

Later in the book he discusses the role of visual perception in graphical communications.  Then he looks more broadly at variations in graphical design, including geographical information examples.   Also an excellent section on the neglected topic of component level graphical design.  Lots of illustrations too,  which makes the latter chapters easy to scan for examples.  The book does not compare business intelligence packages broadly,  but deals with the design of data visualization.

A mild criticism is that the book does not deal with the process of end-user interaction design with data visualizations. Perhaps that is another volume.  I would like to see that examined further.

Later chapters include thoughts about how to tell compelling stories with numbers.  And also the interaction of standards and innovation in the visualization space.

Read it, it is very good.    See also Stephen Few's blog which covers related topics.

Choosing a Pie Critique

Most of us who do data visualization know that the Pie Chart form is rarely a good choice for displaying comparative data to promote understanding.   Though I have seen it being used more often recently as sheer artistic relief within the 'infographic' format. Some recent papers seem to have supported the value of the Pie form..  Stephen Few critiques this research.