IMAODBC 2016: And the winner is…

The Best Presentation Award of the International Marketing and Output Database Conference IMAODBC 2016 in Gozd Martuljek, Slovenia goes to Susanne Hagenkort-Rieger and her team from DESTATIS (Statistisches Bundesamt, Germany).

In her presentation Susanne highlighted the importance of web search statistics  and why intuition when emphasizing selected statistical data is often not sufficient. To achieve relevance and accessibility of most popular statistical data we should not ignore what the web search data say.


Presentation is available at the IMAODBC 2016 website…

A few facts about IMAODBC 2016 as presented in the second best presentation by Corey Jenkins, USDA – Foreign Agricultural Service, U.S.A.:




Disrupting Dissemination – From Print to Bots

Digitally disrupted data production

“The collection of statistics has been digitally disrupted, along with everything else, and there are important questions about collection methods and whether or not “big data” genuinely offers promise for a giant leap forward in the productivity of official statistics.”

This statement in Financial Times’ edition of August 20th, 2015 deals with UK’s Office for National Statistics ONS. Its title:  “UK needs a statistical strategy to catch up with digital disruption”. Its message: ONS (and I think all Official Statistics) has problems to keep up “with the profound changes in the structure of the economy during recent decades.”

The “Independent Review of UK Economic Statistics” by Professor Sir Charles Bean, Professor of Economics at the London School of Economics in March 2016 goes deeper and gives 24 recommendations, some of these obviously valid for statistics’ production and producers in general.


“Innovation and technological change are the wellspring of economic advancement. The rapid and sustained rise in computing power, the digitisation of information and increased connectivity have together radically altered the way people conduct their lives today, both at work and play. These advances have also made possible new ways of exchanging goods and services, prompted the creation of new and disruptive business models, and made the location of economic activity more nebulous. This has generated a whole new range of challenges in measuring the economy.” (p.71)

“Measuring the economy has become even more challenging in recent times, in part as a consequence of the digital revolution. Quality improvements and product innovation have been especially rapid in the field of information technology. Not only are such quality improvements themselves difficult to measure, but they have also made possible completely new ways of exchanging and providing services. Disruptive business models, such as those of Spotify, Amazon Marketplace and Airbnb, are often not well-captured by established statistical methods, while the increased opportunities enabled by online connectivity and access to information provided through the internet have muddied the boundary between work and home production. Moreover, while measuring physical capital – machinery and structures – is hard enough, in the modern economy, intangible and unobservable knowledge-based assets have become increasingly important. Finally, businesses such as Google operate across national boundaries in ways that can render it difficult to allocate value added to particular countries in a meaningful fashion. Measuring the economy has never been harder.” (p. 3)

And: “Recommended Action 4: In conjunction with suitable partners in academia and the user community, ONS should establish a new centre of excellence for the analysis of emerging and future issues in measuring the modern economy.”  (p.118)

Disrupting Dissemination of Statistics

The rise of new technologies followed by new information behavior has also disrupted existing dissemination formats (from print to digital) and dissemination practices (from quasi-monopolistic to open and multiple).

A well-known example for disrupting dissemination is given by Wikipedia and its subject is Wikipedia itself:
“The free, online encyclopedia Wikipedia was a disruptive innovation that had a major impact on both the traditional, for-profit printed paper encyclopedia market (e.g., Encyclopedia Britannica) and the for-profit digital encyclopedia market (e.g.,Encarta). The English Wikipedia provides over 5 million articles for free; in contrast, a $1,000 set of Britannica volumes had 120,000 articles.” (Article:

In fact, disruptive tendencies happen on both sides: in producing and in presenting or accessing statistical information.

Some thoughts about this:

  1. Until the end of the 20th-century, print was the main channel for disseminating statistics. Libraries in Statistical Offices and Society had their very vital role.
  2. With the internet opened a new channel: Statistical Offices’ Websites appeared, access to databases and attractive data presentation (visual, storytelling, see i.e. this) were top themes and stuff for long discussions. Access to data was now simple and for everyone.
  3. With the open data initiatives not only accessing but also disseminating statistical information got much easier. Nearly everyone could become a data provider. License fees no longer hindered the redisseminaton of official statistics and APIs or webservices provided by statistical offices made this possible in an automated way.
    Statistics can be easily integrated into websites and apps of non-official data providers, this with all the chances to enable democratic conversation and the risks of data misuse.
  4. All this gives statistics a much more important role in communication processes. On the other hand communicating with statistics gets simpler: Letters, telephone calls and even e-mails become cumbersome seen the possibility bots (will) provide. With a stats bot in my daily used messenger, I ask for a statistical information, and the bot uses a search engine or connects me directly to a statistical expert.
    Brands that already have full-fledged apps and responsive websites can take advantage of bots’ ability to act as concierges, handling basic tasks and micro-interactions for users and then gracefully connecting users with apps or websites, as appropriate, for a more involved experience.” (Adam Fingerman, venturebeat, 20.7.2016)
  5. What’s next? Innovation with disruption goes on, but disruption does not always mean destruction: It’s still a wise decision to keep some information in paper format. A statistical yearbook with key data lasts for centuries, not so a website, an API or a bot.



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

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.