The Big Policy Canvas Knowledge Base is a state-of-the-art, online and dynamic repository that functions as an accumulator uniting all the knowledge produced during the project. It is structured along the three dimensions of needs, trends and assets and furthermore offers a mapping among them by defining how they are interconnected and how they influence each other.
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The Big Policy Canvas Knowledge Base is a state-of-the-art, online and dynamic repository that functions as an accumulator uniting all the knowledge produced during the project. It is structured along the three dimensions of needs, trends and assets and furthermore offers a mapping among them by defining how they are interconnected and how they influence each other.
Accordingly, the Knowledge Base comprises all current and emerging needs and trends as well as the technological and methodological assets serving and addressing them respectively. Every element identified in the three dimensions is defined according to its type, scope, priority, and the policy domains and policy cycle stages in which it applies. A list of criteria, namely namely the type of need, trend or asset, the priority, the Big data potential, as well as the type of item itself enable the user to filter and navigate through the Knowledge Base contents.
The Knowledge Base targets to support the uptake and reuse of solutions that hold the potential to advance policy making processes, leading towards next-generation policy making.
Read the article about the Knowledge Base in the blog and know more about how we have identified and analysed the trends and how we have assessed the Big Data Potential.
New technologies have great influence on work and work processes, as we know them today. For example, new technologies offer the possibility of mobile work, can optimise the work process and make it more economical. [1]
The term “smart” in this trend indicates, that it is not only about collecting and storing data, but about automation of data analyses. Kim et al. describe this as follows: “Smart surveillance system is mainly composed of automatic video/audio analysis. Therefore, an emerging surveillance system must consider multimedia information for monitoring activities and extracting meaningful information from the environment.”[1]
This trend deals with the use of algorithms in policy and decision making. First, there is the trend of algorithmic regulation. Algorithmic regulation means that regulatory decision making is delegated to algorithms. The algorithms give the instructions of what should be done to achieve a desired outcome. [1]
The trend of using algorithms in governance and an increasing reliance of public decision making on algorithms is sometimes also called algocracy. [2]
Nudge or Nudging in governmental context can be seen as a concept of libertarian paternalism, which is about improving decision making. Nudging comes from the behavioural economics and the basic assumption is that people are not able to make the right decisions. Therefore, the behaviour of individuals should be influenced in a predictable way. Experts decide what is the best possible decision and try to steer the people into this direction. The people still have the option to choose an alternative, which makes this concept different to classical paternalism. [1]
Until now, it has usually been the case that documents have to be submitted in every authority, i.e. it is always the same information that is passed on to the authorities. This procedure is paper-based and involves a great deal of effort.
The goal of IT consolidation is to simplify and modernise the IT infrastructure in order to increase IT security, data protection and IT controllability. For example, central IT service centers with a strong focus on the service aspect are being set up and modern infrastructure platforms are being shared. The IT consolidation creates a basis for successful digitization. [1][2]
The DCAT Application Profile for data portals (DCAT-AP) is a specification based on the Data Catalogue vocabulary (DCAT) for describing public sector datasets in Europe. Its basic use case is to enable cross-data portal search (primary Open Data Portals) for data sets and make public sector data better searchable across borders and sectors. This can be achieved by the exchange of descriptions of datasets among data portals.
An App, which collects data and shows weather related information (e.g. duration of rain showers) but also predicts the weather in the Netherlands
The analysis is powered by a hybrid Natural Language Processing (NLP) engine that runs highly sophisticated linguistic algorithms and Machine Learning classifiers. The engine is wrapped in a platform-agnostic REST API service that enables your software applications, workflows, and services to receive rich TheySay JSON metadata with minimal integration work
Google Fusion tables is a web application for data analysis, large data-set visualisation, and mapping. It allows users to easily create data visuals and publish them online instantly with provided subsets and an easy format similar to online files. It further supports the ability to work through larger data sets including filtering, sorting, summarising them in collaboration with other users online. It enables users to share and combine multiple tables between users and publicly available data and merge them into one. The application is still experimental and its API has released V2
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