Strategies for effective web-based data dissemination include identifying different types of users like tourists, harvesters, and builders and tailoring content and features to their needs. An optimal strategy considers technical aspects like platforms, hosting, and design as well as administrative aspects like content management, user support, and resource allocation to balance costs and usability. The goal is to facilitate two-way communication through data access and promote statistical knowledge.

1. Strategies for web based data dissemination
A strategy is a plan of action designed to achieve a vision - from Greek "στρατηγία" (strategia).
Zoltan Nagy – Statistics Division, Department of Economic and Social affairs, United Nations
United Nations Regional Workshop on Data Dissemination and Communication
Rio de Janeiro, Brazil, 5 - 7 June 2013

2. Existing Strategies
Fundamental Principles of Official Statistics
“statistics that meet the test of practical utility are to be compiled and made available on an
impartial basis by official statistical agencies to honor citizens' entitlement to public information”
Handbook of Statistical Organizations
National Strategies for the Development of Statistics
(NSDS)
The Generic Statistical Business Process Model (GSBPM)

3. Data dissemination = communication
Policy makingProfessionals & public
COMMUNICATION
COMMUNICATION
COMMUNICATION
COMMUNICATION
Analysis & research
Statistical needs
and education
Knowledge based society
Policy accountability
Analysis & assessment
Policy options
Policy decisions
Policy validations
INDEPENDENT OFFICIAL STATISTICS
ECONOMIC AND SOCIAL PROGRESS

4. The importance of web-based data-dissemination
 Everyone who has access to internet is becoming a potential user of statistics.
 From 2008 to 2013 the number of Internet users grows by 67%
Forget the last war.

5. Identifying users
User groups
 Decision makers (government at central and local level, businesses)
 Academia (institution that use, research and analyze data)
 Educational (primary, secondary, tertiary)
 Public at large
Tourists Harvesters and (data) Miners

6. Tourists
Novice or infrequent users, and typically make up the
majority of individual users.
Looking for basic data either out of curiosity, or to
inform personal decisions.
Want to be able to find and view data quickly and
easily, they prefer low levels of complexity and need
only limited functionality.

7. Harvesters
Intermediate and fairly frequent users, who are
looking for data to inform basic research or economic
decisions.
They will accept increased complexity if it results in
addition functionality and flexibility in the way they
can view and download data.

8. (Data) Miners
Expert users, typically small in number, but using
large volumes of data on a regular basis, often for
detailed research or analysis.
They want simplicity, easy downloads functionality
and flexibility, take data offline

9. A new type - Builders
Experts that want to reuse statistical data without copying
or downloading it.
Requesting ability to access data servers at 24/7 and feed
data to maps, visualizations and other applications.
Web services - interoperable machine-to-machine
interaction over a network".
Mashups – hybrid web applications
 Visualizations
 Mappings
 Data aggregators

10. Defining the content
Data
 Topic – domain specific or across-domain
 Coverage – geographical and time
 Aggregation level - micro and macro data
 Nature of the data – tables, tabulations, time-series, datapoints
Documentation
 Metadata (descriptive and structural)
 Methodologies and standards
 Classifications
 Best practices, business processes, etc.

11. Subscription models
Registration
 No registration required
 Registration required (provides better tracking,
communication etc)
Subscription
 Free (preferred by many countries)
 For fee (cost recovery, profit, one-time, periodical, service
based )
 Multi-tier (free basic and for fee premium services)

12. User management
User access (registered vs unregistered users)
User support, helpdesk
User surveys (online polling)
User activity tracking
 Web server statistics
 Analytic services (Google analytics)
 Custom built tracking services
Social networking (Facebook, Twitter..)

13. Site administration
Data Management
 Data correction facility
 Data upload facility
 Data availability
Metadata Management
 Structural metadata
 Descriptive metadata
 Data upload calendar
Management Reporting

14. Resource allocation
+ Data dissemination group
(Centralized or Decentralized)
+ Systems/Application development
+ Hardware and software requirements
+ Long-term maintenance
+ Operation
+ Helpdesk
--------------------------------------------------
= TOTAL COST OF OWNERSHIP (TCO)

15. Content delivery
Bandwidth conservation
Browser considerations

16. Mobile devices

17. Software platform and architecture
Off-the-shelf products
Custom development (in-house, outsourcing)
Open source platforms
Proprietary platforms
Self hosting
Outsourced hosting

18. Design considerations
Simplicity and ease of use
Easy of navigation
Bookmarking
Searchability
 Drill down
 Dimensional search
 Full text search

19. Conclusions
One size does not fit all
Web-based data dissemination should work as a two
way communication
Focus has to be on users who frequently visit our
sites
The maintenance of web-based data-dissemination
products is a long term commitment
We have to be aware of TCO