Data are from the Past

There’s a lot of discussion and also big hope about what is called Big Data and the role of Data Scientists. Will Data Scientists help us to create a better future?

Yes and no. ‘Making predictions about unprecedented futures requires more than data, it requires theory-driven models that envision futures that do not exist in data. Fortunately, digital tools also assist us in envisioning futures that have never been.’

This talk by Martin Hilbert published 13.01.2015 explains why Data are from the past and are not enough.

‘During his 15 years at the United Nations Secretariat, Martin Hilbert assisted governments to take advantage of the digital revolution. When the ‘big data’ age arrived, his research was the first to quantify the historical growth of how much technologically mediated information there actually is in the world.’

‘After joining the faculty of the University of California, Davis, he had more time to think more deeply about the theoretical underpinning and fundamental limitations of the ‘big data’ revolution.’

‘Martin Hilbert holds doctorates in Economics and Social Sciences, and in Communication, and has provided hands-on technical assistance to Presidents, government experts, legislators, diplomats, NGOs, and companies in over 20 countries.
At the University of California, Davis, Martin thinks about the fundamental theories of how digitization affects society.’ More

[Source: youtube]

Official Statistic’s SWOT

In the official statistics industry (an industry!) reflection and  collaboration are highly prioritized.

As an example: HLG-BAS.

What’s this? ‘The High-Level Group for Strategic Developments in Business Architecture in Statistics (HLG-BAS) was set up by the Bureau of the Conference of European Statisticians in 2010 to oversee and coordinate international work relating to the development of enterprise architectures within statistical organisations.’ More about HLG-BAS on UNECE statistics wikis.

And more about the Conference of European Statisticians CES:

Implement the HLG-BAS vision

HLG-BAS presents a very interesting paper for the 60th plenary session of the Conference of European Statisticians. It’s the ‘Strategy to implement the vision of the High-level Group for Strategic Developments in Business Architecture in Statistics‘.

This paper positions official statistics as part of the information industry:
‘The official statistics industry is part of a more extensive information industry. Within this wider information industry other players are claiming their place and statistical organisations cannot automatically assume that they will retain their current position and relevance.’ (point 5)


And the paper summarizes in a short and impressive manner the Strengths, Weaknesses, Opportunities and Threats of Official Statistics. (point 9)

‘A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was undertaken by Capgemini Consulting working for Statistics Netherlands to define the current situation of the official statistics industry assessing it from an international perspective. This analysis was based on existing information on the industry (including the vision of the HLG-BAS) complemented by interviews with members of the HLG-BAS (internal stakeholders), commercial organisations and government bodies (external stakeholders).

The results of this exercise are:

1. Strengths

(a) High quality with relevant and very strong statistical products over long term;
(b) Strong “brand value” of official statistics locally and internationally;
(c) Ability and ‘stamina’ to produce statistics for long-term records and consistency;
(d) International collaboration has started mainly because it is becoming too expensive for each NSO to individually change their tailor-made production processes and products.

2. Weaknesses

(a) A limited outside and “client-centric” view;
(b) Communication of products and results is often not good enough;
(c) Workforce and processes should be more agile to follow rapidly the changing needs of society;
(d) NSOs are not efficient enough in their processes and rely too much on human effort;
(e) The statistical industry as a whole has no clear silhouette or definition; international coherence is low;
(f) NSOs should provide more information about statistics, regarding both quality and other metadata;
(g) Top-level commitment to bring about the changes needed to align the statistical industry with the changing environment is not broadly understood as the key factor in this change process.

3. Opportunities

(a) In some specific statistical domains, cross-border data become more important (globalisation, enterprise groups, climate change). The work and products of NSOs should be expanded to explain what is happening on a multinational level;
(b) The “open data” movement may increase the sources available for official statistics;
(c) NSOs could collaborate (more) with (commercial) external parties;
(d) The official statistics industry could play a more active role regarding new and alternative data sources and collection methods;
(e) NSOs could be quality institutes that certify statistical inputs/outputs of other (commercial) parties;
(f) In the statistical domain the NSOs can lead when it comes to defining and maintaining international standards;
(g) Standardisation of production process (plug and play technology) and products of NSOs to increase international comparison and quality control of products;
(h) Consolidation of NSOs roles as public supplier of trust and quality;
(i) International coherence and the willingness to form a more closely knit statistical community or industry are beginning to materialize;
(j) Specialisation of NSOs in certain products to increase efficiency in the production process of these products. This specialisation in products could vary across countries and sectors to optimize the possibilities of specialisation.

4. Threats

(a) Other organisations are starting to create output NSOs used to have a monopoly on;
(b) Reduced staff and budget cuts;
(c) Weak/fragile coordination of international collaboration activities;
(d) Society wants more timeliness in statistics, both in disseminating existing products and in developing new products;
(e) Some government clients do not distinguish between official and non-official data sources for ad hoc questions, as long as it meets their purpose;
(f) New technologies like open data can seduce NSOs into losing focus of their core business.’