Operative efficiency in Labour Agency
Description
This scenario is a real implementation in use.
What is the application scenario about? German Federal
Labour agency to improve customer services and cut
operations costs.
What is the goal of the application scenario? Enable a new
range
of
personalized
services.
What problems can be solved by means of the application
scenario?
Personalize
services
at
a
minimum
cost.
And how are those issues handled today? Everybody was
receiving the same standard services despite having different
profiles.
Who
are
the
users?
Unemployed
workers.
What is the value generated? Reduce spending by €10 billion
yearly and reduce the amount of time that unemployed workers
took to find employment.
What is the underlying business model? Who is paying for the
new value generated? Who is providing the solution? It is an
efficiency improvement of an existing public service.
Why is it Big Data? It analysed historical data on its customers,
including histories, interventions and the time they took to find a
job, to develop a segmentation based on this analysis.
Example Use
Case
Based on the segmentation the Labour agency could tailor its
interventions for unemployed workers. The agency built capabilities
for producing and analysing data that enabled a range of new
programs and new approaches to existing programs. The Labour
agency is now able to analyse outcomes data for its placement
programs more accurately, spotting those programs that are
relatively ineffective and improving or eliminating them. The agency
has greatly refined its ability to define and evaluate the
characteristics of its unemployed and partially employed customers.
As a result, it has developed a segmented approach that helps the
agency offer more effective placement and counselling to more
carefully targeted customer segments. Surveys of their customers
show that they perceive and highly approve of the changes it is
making.
User Value
User Impact of Application Scenario: high, (It has reached to goals,
reduce the cost of the service, and provide a better service to the
users, as now they are able to find a new job in a shorter period of
time).
Maturity of Application Scenario: Implemented.
Financial Impact: How much?: saving of €10 billion yearly in costs
For whom?: The public Labour agency.
Prerequisites
N/A
Data Sources
Historical Data on costumers
Application
Domain
Public employment services
Type of
Analytics
Select one of the three categories (provide rational for classification)
Basic Analytics (e.g. monitoring, reporting, statistics)
Mature Analytics (e.g. data mining, machine learning)
Advanced Analytics (e.g. prediction, devise)
Required
Big Data
Technology
Data acquisition: yes, for the periodical addition of new historical
data
Data analysis: yes, for segmentation purposes of historical data
Data curation: n/d
Data storage: n/d
Data usage: yes, to compare analysed data with the information form
the unemployed worker
Sources
(McKinsey Global Institute, 2011)
2.4. Conclusion and Next Steps
So far, with the information collected about Big Data in the public sector, it can be said that it is more or less aware of the potentials of these technologies, but the path to success is not currently clear due to some uncertainties, the most important of which are:
Big Data technology is immature.
Lack of skilled people.
New European directives about Data protection and PSI to be approved in the next one to two years display some uncertainties about the impact on the implementation of Big Data and Open Data initiatives in the public sector. Specifically, Open Data is set to be a catalyst from the public sector to the private sector to establish a powerful data industry.
It needs to gain momentum. Today, there is more marketing around Big Data in public sector than real experiences from which to learn which applications are more profitable and how it should be deployed.
There are many bodies in public administration (especially in those which are widely decentralized), so much energy is lost and will remain so until a common strategy is realised for reuse cross technology platforms.
The next steps in relation to the collection of public sector requirements will be the collection of requirements based of workshops for public sector officials. The first one took place on the16th of April in Madrid where the Spanish public sector was invited. Two additional workshops are foreseen in other European countries. Detailed information about the conclusions from these workshops will be provided in the coming reports for public sector.
2.5. Abbreviations and acronyms
DG Directorate GeneralEC European Commission EU European Union
GDP Gross Domestic Product
ICT Information and Communication Technologies
OECD Organisation for Economic Co-operation and Development PSI Public Sector Information
2.6. References
1105 Government Information Group. (n.d.). The chase for big data skills. Retrieved March 26, 2013, from GCN.com: http://gcn.com/microsites/2012/snapshot-managing-big-data/04--chasing- big-data-skill-sets.aspx
Ashford, W. (2012, January 2012). Big changes expected as EC publishes data protection review. Retrieved April 10, 2013, from computereekly.com:
http://www.computerweekly.com/news/2240114258/Big-changes-due-in-revised-EC-data- protection-rules
Bossaert, D. (2012). The impact of demographic change and its challenges for the workforce in the European public sectors. European Institute of Public Administration (EIPA).
Boyer, K. (n.d.). Sentiment Analysis. Retrieved March 25, 2013, from DMGFederal.com:
http://www.dmgfederal.com/what-is-sentiment-analysis/
Correia, Z. P. (2004). "Toward a stakeholder model for the co-production of the public-sector information system". Information Research, 10(3) paper 228 . Retrieved February 27, 2013, from InformationR.net: http://InformationR.net/ir/10-3/paper228.html
Cottingham, R. (2010). Greatest hits: Facebook and social networking. Retrieved April 18, 2013, from Noise To Signal: http://www.robcottingham.ca/cartoon/greatest-hits-facebook-and-social- networking/
EPSIplatform. (2013, April 11). The EU Endorses a New PSI Directive. Retrieved April 18, 2013, from epsiplatform.eu: http://epsiplatform.eu/content/eu-endorses-new-psi-directive
European Commission. (1998). COM(1998)585. PUBLIC SECTOR INFORMATION : A KEY RESOURCE FOR EUROPE. GREEN PAPER ON PUBLIC SECTOR INFORMATION IN THE INFORMATION SOCIETY. European Commission.
Hunton & Williams LLP. (2013, April 9). Article 29 Working Party Clarifies Purpose Limitation Principle; Opines on Big and Open Data. Retrieved April 18, 2013, from huntonprivacyblog.com:
http://www.huntonprivacyblog.com/2013/04/articles/article-29-working-party-clarifies-purpose- limitation-principle-opines-on-big-and-open-data/
McKinsey & Company. (2011). The public-sector productivity imperative. McKinsey & Company. McKinsey Global Institute. (2011, June). BIG data: The next frontier for innovation, competition, and productivity. McKinsey & Company.
OECD. (2006). DSTI/ICCP/IE(2005)2/FINAL. DIGITAL BROADBAND CONTENT: PUBLIC SECTOR INFORMATION AND CONTENT. Organisation for Economic Co-operation and Development.
Oracle. (2012). Big Data: A big Deal for Public Sector Organizations. Oracle.
TechAmerica Foundation. (n.d.). “Big Data” Can Save Money and Lives Say Government IT Officials. Retrieved April 15, 2013, from TechAmerica Foundation:
http://www.techamericafoundation.org/content/wp-content/uploads/2013/02/SAP-Public-Sector- Big-Data-Report_FINAL-2.pdf
The European Parliament and the Council of The European Union. (2003, November 17). Directive 2003/98/EC of the European Parliament and of the Council of 17 November 2003 on the re-use of public sector information. Official Journal L 345 , 31/12/2003 P. 0090 - 0096. Brussels: The European Parliament and the Council of The European Union.
The White House. (2012, March 29). Big Data is a Big Deal. Retrieved January 18, 2013, from The White House: http://www.whitehouse.gov/blog/2012/03/29/big-data-big-deal
World Economic Forum. (2012). Big Data, Big Impact: New Possibilities for International Development. Geneva: The World Economic Forum.
Yiu, C. (2012). The Big Data Opportunity. Making govenrment faster, smarter and more personal. London: Policy Exchange.