Mid-December 2014 Statistics Switzerland launched its first digital publication for tablets (iOs. Android) and (and!) browser, in French and German. The name for this publishing category is ‘DigiPub‘.


In App Store and Google Play

DigiPubs are provided via the SwissStats App available on Apple Store and Google Play (Windows to come later ).



In the browser 2015-07-17_webviewer.

The challenge: Storytelling in times of tablets

Storytelling in the time of tablets and mobile people performs in a new field.
The idea and the message are old – let‘s call it the book paradigm.
But it‘s a book in new clothes. New aspects must be taken into account: new possibilities, skills, tools and processes.



After evaluation, the choice for a performant and sustainable publishing instrument fell on: .folio, an open format, part of Adobe‘s Digital Publishing Suite DPS.

.folio provides:

  • Standardised navigation
  • Wide range of presentation possibilities
  • Integration of internet content
  • Runs on most platforms, also browsers
  • Publication in the major stores
  • Production based on layout programs, editing systems or web content management systems
  • Open format (ZIP archive with PDF, HTML, XML inside).


 Rethink publication !

Electronic publication offers everything needed to make a story appealing. But this means: Rethink publication!

Authors and also publication specialists (publishers, visualisers, layout designers) are challenged

  • in terms of concept with regard to the content that is to be communicated
  • in terms of the presentation due to the possibilities that the medium is opening up
  • in terms of collaboration with specialists.

New ways of working, processes and also changing job descriptions are the result and necessary.


The Concept

The whole story about choosing and developing DigiPubs is in the following presentation:

2015-02-19_dotfolioDownload presentation (format: Powerpoint): Dotfolio.pptx

Download presentation (format: PDF): dotfolio.pdf



Basic Needs and Delighters

How to find out user needs? Which method to choose?

These questions find an innovative answer in an article from Ilka Willand (of Destatis, the German Statistical Office) published in number 31 of IAOS’ Statistical Journal

Beyond traditional customer surveys: The reputation analysis
Authors: Willand, Ilka
DOI: 10.3233/sji-150866
Journal: Statistical Journal of the IAOS, vol. 31, no. 2, 2015

Here a short version with pieces taken from this article:


‘An important strategic goal of Destatis is to continuously collect information about the customer satisfaction and the perception of important stakeholders and target groups. We conduct frequent customer surveys since 2007. But not all important stakeholders and target groups are necessarily registered customers. To learn more about their demands a reputation analysis was conducted in 2013 in cooperation with a market researcher. To determine a manageable frame for the study, we focused on three target groups: Respondents (households and enterprises), fast multipliers (online and data journalists) and young multipliers (young academics). The analysis was mainly based on the “Kano-Model”, a methodological approach, which is often used in quality management and product development. In the following article the survey design and the main results will be presented.’

Basic needs and Delighters

‘The most important category is the basic needs. Basic needs are taken for granted and they are typically unspoken. If they are fulfilled, they do not increase satisfaction. If they are not fulfilled, they will cause dissatisfaction.
Delighters are unexpected features that make customers happy. They do not necessarily cause dissatisfaction when not fulfilled, because they are not expected.’

 Three Target Groups in Focus

‘To determine a manageable frame we focused on three target groups who became increasingly important for the work of the Federal Statistical Office in the past years:
a) Respondents (households, enterprises)
b) Fast multipliers (online and datajournalists)
c) Young multipliers (young graduates and PhD students of social and economic sciences).

‘Target groups were asked for their basic needs and delighters concerning data search, data use and the reporting process.
On a scale from 0 (very bad) to 7 (very good) the reputation values are 5.3 for the fast and the young multipliers, 4.7 for the households and 4.6 for the enterprises.’




‘Most important basic needs and delighters: Especially for the responding enterprises it is a basic need important to get survey results after the survey is completed. A telephone service is a basic need especially for the bigger companies and the households to support the reporting process.
It is a delighter for enterprises to respond only online. This is currently being implemented in Germany, regardless of the results of the survey.’


Fast Multipliers

Most important basic needs and delighters: Fast multipliers expect more than databases and datasets. For almost every second a telephone-support is a basic need. This is quite interesting because there are many internal discussions at Destatis to give up that service for the journalists. Also they expect to find data they are looking for as fast as possible and for free on the internet. After an average of 14 minutes of searching on the Destatis website they will contact the information service if they are not able to find what they are looking for. To satisfy their basic need to find data as quick as possible we have to improve the search engine.
Most of our data is already available for free. Interactive charts would delight most of the journalists. Application programming interfaces (APIs) to grab huge amounts of primary data are the delighter especially for the data journalists.’


Young Multipliers

Most important basic needs and delighters: There are intersections between the young and the fast multipliers. Young multipliers also want data as fast as possible and for free on the internet. Most of the PhD students expect detailed methodological descriptions related to the datasets. What are the delighters? Surprisingly one half of the young academics mentioned examples on how to read tables and charts as a delighter. Similar to the fast multipliers we have overestimated their statistical knowledge in the past. Already more than one third of them see the opportunity to search for data via smartphone or tablet as a delighter. That means we have to offer more appropriate publication formats in the future.’


Results at a Glance


 See also

Ilka Willand got the award at IMAODBC 2013 for presenting this reputation study. See he slides at https://blogstats.wordpress.com/2013/09/28/imaodbc-2013-and-the-winner-is/

Data Journalism avant la lettre

From Data to Insight

Where there are data, there is insight. However, insight needs know how – know how about data sources, know how about analyzing data (with particular tools), about the context of the data and – last but not least – know how about presenting and communicating the insight.


William Playfair

These steps characterize what for some time now is called data journalism. More than 200 years ago we can find a brilliant example of ‘data journalism avant la lettre’ by the person who is thought to have invented statistical charts (or ‘lineal arithmetic’): William Playfair.

In his book ‘Lineal Arithmetic’ published in 1798 he presents several short articles about trade relations and the income produced by this trade. His aim is to describe long time developments not the actual situation in his difficult period of revolution and war. Mercantilism seems to be the context of his argumentation, but his primary interest surely is to demonstrate his innovative visual presentation.


Open Data 1798

Playfair gets his data from the House of Commons’ yearly accounts. Open Data 18th century!


Analyzing and presenting

Playfair’s data research is quite easily done. There aren’t big data to be traveled. Some time series of import and export data are the result. It’s  his presentation that marks the point!



Playfair presents his findings in a new form. The visual presentation of data is his invention, and he proudly explains this visual ‘mode of representing‘ in the introduction of his work. That’s scientific and convincing.

2015-05-17_playfair-table ,

And to make his readers familiar with charts, especially bar charts, he gives a fascinating explanation leading from real-world  money staples to abstract bars of a painted chart:

‘This method has struck several persons as being fallacious, because geometrical measurement has not any relation to money or to time; yet here it is made to represent both. The most familiar and simple answer to this objection is by giving an example. Suppose the money received by a man in trade were all in guineas, and that every evening he made a single pile of all the guineas received during the day, each pile would represent a day, and its height would be proportioned to the receipts of that day; so that by this plain operation, time, proportion, and amount, would all be physically combined.
Lineal arithmetic then, it may be averred, is nothing more than those piles of guineas represented on paper, and on a small scale, in which an inch (perhaps) represents the thickness of five millions of guineas, as in geography it does the breadth of a river, or any other extent of country.’ (p.7/8)



Charts and textual explanation go hand in hand. Playfair discusses all charts in short texts. For chart 3 (Germany)  – see above – it looks like this:



‘ … to aim at facility, in communicating information’ (p.8)

Communicating information is where Playfair excels. And he has studied how to do this and where his target groups are:

‘ …. we think it better to confine this work to mere matter of fact, as much as possible, being’ fully satisfied that in this small volume is contained what every man in this country, who aims at the reputation of a well-informed merchant, ought to be acquainted with; at the same time, that the Statesman will find in it things which he perhaps already knows, but which are here painted to the eye in a more agreeable and distinct manner than is possible to be done by writing or figures. It is on these grounds that this small, but compendious volume, claims the public attention.(p.4)


 The title has the message


Visual first – Visual.ONS

Visual representations of statistical data are attractive – and worth to build an own website with nothing but (info)graphs and maps … and more behind it!

ONS did it:


‘The Office for National Statistics (ONS) is the UK’s largest independent producer of official statistics and is the recognised national statistical institute for the UK. Visual.ONS is a website exploring new approaches to making ONS statistics accessible and relevant to a wide public audience. The site supports the UK Statistic Authority’s publicly stated intention of “making data, statistics and analysis more accessible, engaging and easier to understand”.
The site will be a home to a variety of different content, including infographics, interactive visualisations and short analysis, exploring data from a range of ONS outputs. It is neither a replacement nor a rebuild of the current ONS website which continues to be the home of ONS’ regular outputs and statistics.’

So far the statement of ONS.


More than pictures

Behind the graphs you can find lots of interactive tools.
A calculator to find out life expectancy is one example:


Great! And the graphs and interactive tools can be embedded into other websites.


Which is the working model helping to get the best results from data? It’s not a specific qualification alone, it’s melting together multiple skills around data: data strategy, best methods, analytical and statistical skills. ‘The ability to work together quickly and flexibly is critical.’

‘Matt Ariker, Peter Breuer, and Tim McGuire from McKinsey give some hints in their article ‘How to get the most from big data?‘. And this could also be of interest for Statistical Offices, traditional specialists in working with Big Data.


Listicles: Where Stats are Popular


There are many forms statistical information can be published. In most cases Official Statistics use press releases or reports or single tables to be downloaded, some also (more and more) visualisations.

But it’s quite rare that rankings or numbered lists are used. And just these forms are among the most popular and attract attention. Who or what is biggest, smallest, first, best? Listicles answer such popular questions: ‘listicle is a short-form of writing that uses a list as its thematic structure, but is fleshed out with sufficient copy to be published as an article’ Wikipedia explains.




Also Huffpost does it and …

…  gives a listicle with 8 reasons to avoid listicles



The  phenomenon of written lists Explained (?)

Steven Poole from The Guardian on the crucial facts about the internet phenomenon of written lists and ‘top nine things you need to know about ‘listicles”:
‘Psychologically, the listicle is seductive because it promises upfront to condense any subject into a manageable number of discrete facts or at least factoids. When you embark on reading an ordinary article, you have no way of knowing how many things it will tell you. Maybe 15, maybe two. Frustrating. Plus, if you’re reading online and it’s more than a single screen long, you can’t be sure when it’s going to end. A listicle keeps helpfully informing you how much of it there is left. Great! You’ve now read three out of nine! Keep going!’

Arika Okrent, in University of Chicago Magazine writes about ‘The listicle as literary form’ and gives -as a conclusion – a list of Eight fun facts about the listicle:
‘1.     A listicle is an article in the form of a list.
2.     It is kind of like a haiku or a limerick.
3.     It has comforting structure.
4.     It makes pieces.
5.     It puts them in an order.
6.     Language does that too.
7.     Sometimes with great difficulty.
8.     Lists make it look easier.’

Rachel Edidin from WIRED gives ‘5 Reasons Listicles Are Here to Stay, and Why That’s OK’

And Maria Konnikova from The New Yorker gives ‘A List (yes!) of Reasons Why Our Brains Love Lists’:
‘In the current media environment, a list is perfectly designed for our brain. We are drawn to it intuitively, we process it more efficiently, and we retain it with little effort. Faced with a detailed discussion of policies toward China or five insane buildings under construction in Shanghai, we tend to choose the latter bite-sized option, even when we know we will not be entirely satisfied by it. And that’s just fine, as long as we realize that our fast-food information diet is necessarily limited in content and nuance, and thus unlikely to contain the nutritional value of the more in-depth analysis of traditional articles that rely on paragraphs, not bullet points.’


And Official Statistcs?  …… Bar Charts as Hidden lists

(Official) Statistics are a big provider of lists too …. but they do it not in a very prominent way, often hidden in visualisations. Bar charts compare countries, resources.

Sometimes quite modestly …


Sometimes explicitly,,,



Timeline of Statistics


In 2010 the Royal Statistical Society (RSS) launched the getstats campaign in order to bring statistics to various groups like general public, journalists or teachers and ‘to increase statistical literacy and … to raise the profile of statistics and its increasing relevance in today’s data-rich society’.

getstats offers a lot of interesting resources, One of these is …

The Timeline


‘Statistics is about gathering data and working out what the numbers can
tell us. From the earliest farmer estimating whether he had enough grain
to last the winter to the scientists of the Large Hadron Collider confirming
the probable existence of new particles, people have always been making
inferences from data. Statistical tools like the mean or average summarise
data, and standard deviations measure how much variation there is within a
set of numbers. Frequency distributions – the patterns within the numbers
or the shapes they make when drawn on a graph – can help predict future
events. Knowing how sure or how uncertain your estimates are is a key part
of statistics.
Today vast amounts of digital data are transforming the world and the
way we live in it. Statistical methods and theories are used everywhere, from
health, science and business to managing traffic and studying sustainability
and climate change. No sensible decision is made without analysing the data.
The way we handle that data and draw conclusions from it uses methods
whose origins and progress are charted here’ (in this timeline).
Julian Champkin
Significance magazine

Timeline PDF