More than just Numbers

The historical research uses statistics since ever.  And Official Statistics are a favourite source for quantitative historiography and digital humanities.

‘Statistics are more than just numbers’

Statistics New Zealand depicts the history of the capital Wellington with a popular format – an infographic.

‘In 1865, following a period of heated debate, an independent tribunal of three Australians selected Wellington to be New Zealand’s capital. Since that time a lot has changed in Wellington and Statistics NZ has been able to follow and track that change through the data it collects.’

How true!: ‘Statistics are more than just numbers, they can chart change, record history, and be a tool for decision making.’ (Source: Stats NZ)




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



Open, Useful, Reusable

In OECD’s brand new publication ‘Government at a Glance 2015’ we can find a new indicator: The OUR Index. It stands for ‘Open, Useful, Reusable Government Data’.

‘The new OECD OURdata Index reveals that many countries have made progress in making public data more available and accessible, but large variations remain, not least with respect to the quality of data provided. Governments need to make participation initiatives more accessible, targeted, relevant and appealing.’ (p.8)


‘The data come from the 2014 OECD Survey on Open Government Data. Survey respondents were predominantly chief information officers in OECD countries and two candidate countries (Colombia and Latvia). Responses represent countries’ own assessments of current practices and procedures regarding open government data. Data refer only to central/federal governments and exclude open government data practices at the state/local levels.’ (p.150)


Based on G8 recommendations

‘The OECD OURdata Index measures government efforts to implement the G8 Open Data charter based on the availability, accessibility and government support to promote the reuse of data, focusing on the central OGD portal in each country'( p.33)

‘The G8 Open Data Charter defines a series of five principles: 1) open data by default; 2) quality and quantity data; 3) usable by all; 4) releasing data for improved governance and; 5) releasing data for innovation, as well as three collective actions to guide the implementation of those principles.’
‘As a first step in producing a comprehensive measure of the level of implementation of the G8 Open Data Charter, the OECD pilot Index on Open government data assesses governments’ efforts to implement open data in three dimensions:
1. Data availability on the national portal (based on principle 1 and collective action 2);
2. Data accessibility on the national portal (based on principle 3) and
3. Governments’ support to innovative re-use and stakeholder engagement (principle 5).
The only principle not covered in this year’s index is Principle 4: Releasing Data for improved governance value (e.g. transparency) as existing measurement efforts have focused primarily on socio economic value creation’ (p.150)


And here comes the ranking

2015-07-10-OURdataIndexData for this chart:

Detailed data for the countries:

The publication

The publication: OECD (2015), Government at a Glance 2015, OECD Publishing, Paris.


The Cui-bono Approach to Open Data

What’s the problem? Which data are needed to solve it? Who gets an advantage of it?

These few questions are valuable key for implementing the open data culture. Open data not as ‘l’art pour l’art’ but in a pragmatic approach, demonstrating that the ‘proof of the pudding is in the eating’.


It seems to work very well as Ton Zijlstra showed in his presentation at the Swiss Opendata Conference 2015.

He gives some examples of situations where open data helped to provide a solution to a problem and where stakeholders got an answer to their issues.


link ti

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

Big Data and Official Statistics



Big Data is THE topic of the freshly published Statistical Journal of the IAOS – Volume 31, issue 2.


Five articles deal with Big-Data topics:

In the editorial Fride Eeg-Henriksen and Peter Hackl give an overview of the Big-Data discussions hold in Official Statistics. Here some remarks taken from this editorial:

‘In spite of the wide interest in and the great popularity of Big Data, no clear and commonly accepted definition of the notion Big Data could be established so far [3]. Modern technological, social and economic developments including the growth of smart devices and infrastructure, the growing availability and efficiency of the internet, the appeal of social networking sites and the prevalence and ubiquity of IT systems are resulting in the generation of huge streams of digital data. The complexities of the structure and dynamic of corresponding datasets, the challenges in developing the suitable software tools for data analytics, generally the diversity of potentials in making use of the masses of available data make it difficult to find a suitable and generally applicable definition. The often mentioned characterization of Big Data by 3 – or more – Vs (volume, velocity, variety – as well as veracity and value), does not capture the enormous scope of the corresponding data sets and the extensive potentials of making use of these data. A highly relevant aspect is that Big Data are so large and complex that traditional database management tools and data processing applications are not feasible and efficient means. This is illustrated by a look at the categories of data sources which typically are seen in the context of Big Data: Such data sources may be
– Administrative, e.g., medical records, insurance records, bank records.
– Commercial transactions, e.g., credit card transactions, scanners in supermarkets.
– Sensors, e.g., satellite imaging, environmental sensors, road sensors.
– Tracking devices, e.g., tracking data from mobile telephones, GPS
– Tracks of human behaviour, e.g., online searches, online page viewing.
– Documentation of opinion, e.g., comments posted in social media.


‘A general conclusion from the set of articles in this Special Section can be drawn as follows: The feasibility and the potentials of using Big Data in official statistics have to be assessed from case to case. In some areas the use of Big Data sources has already proved to be feasible. The choice of the appropriate IT technology and statistical methods must be specific for each situation. Also issues like the representativity and the quality of the resulting statistics, or the confidentiality and the risk of disclosure of personal data need to be assessed individually for each case. There is no doubt that Big Data will have a place in the future of official statistics, helping to reduce costs and burden on respondents. However, major efforts will be necessary to establish the routine wise use of Big Data, and new approaches will be needed for assessing all aspects of quality.’

[3] C. Reimsbach-Kounatze, (2015), The Proliferation of “Big Data” and Implications for Official Statistics and Statistical Agencies: A Preliminary Analysis”, OECD Digital Economy Papers, No. 245, OECD Publishing.


See also: Big Data in Action May 2015