Strategies and Approaches for Exploiting the Value of Open Data

Data is increasingly permeating into all dimensions of our society and has become an indispensable commodity that serves as a basis for many products and services. Traditional sectors, such as health, transport, retail, are all benefiting from digital developments. In recent years, governments have also started to participate in the open data venture, usually with the motivation of increasing transparency. In fact, governments are one of the largest producers and collectors of data in many different domains. As the increasing amount of open data and open government data initiatives show, it is becoming more and more vital to identify the means and methods how to exploit the value of this data that ultimately affects various dimensions. In this thesis we therefore focus on researching how open data can be exploited to its highest value potential, and how we can enable stakeholders to create value upon data accordingly. Albeit the radical advances in technology enabling data and knowledge sharing, and the lowering of barriers to information access, raw data was given only recently the attention and relevance it merits. Moreover, even though the publishing of data is increasing at an enormously fast rate, there are many challenges that hinder its exploitation and consumption. Technical issues hinder the re-use of data, whilst policy, economic, organisational and cultural issues hinder entities from participating or collaborating in open data initiatives. Our focus is thus to contribute to the topic by researching current approaches towards the use of open data. We explore methods for creating value upon open (government) data, and identify the strengths and weaknesses that subsequently influence the success of an open data initiative. This research then acts as a baseline for the value creation guidelines, methodologies, and approaches that we propose. Our contribution is based on the premise that if stakeholders are provided with adequate means and models to follow, then they will be encouraged to create value and exploit data products. Our subsequent contribution in this thesis therefore enables stakeholders to easily access and consume open data, as the first step towards creating value. Thereafter we proceed to identify and model the various value creation processes through the definition of a Data Value Network, and also provide a concrete implementation that allows stakeholders to create value. Ultimately, by creating value on data products, stakeholders participate in the global data economy and impact not only the economic dimension, but also other dimensions including technical, societal and political

Saffron: a data value assessment tool for quantifying the value of data assets

Data has become an indispensable commodity and it is the basis for many products and services. It has become increasingly important to understand the value of this data in order to be able to exploit it and reap the full benefits. Yet, many businesses and entities are simply hoarding data without understanding its true potential. We here present Saffron; a Data Value Assessment Tool that enables the quantification of the value of data assets based on a number of different data value dimensions. Based on the Data Value Vocabulary (DaVe), Saffron enables the extensible representation of the calculated value of data assets, whilst also catering for the subjective and contextual nature of data value. The tool exploits semantic technologies in order to provide traceable explanations of the calculated data value. Saffron therefore provides the first step towards the efficient and effective exploitation of data assets

Semantic data ingestion for intelligent, value-driven big data analytics

In this position paper we describe a conceptual model for intelligent Big Data analytics based on both semantic and machine learning AI techniques (called AI ensembles). These processes are linked to business outcomes by explicitly modelling data value and using semantic technologies as the underlying mode for communication between the diverse processes and organisations creating AI ensembles. Furthermore, we show how data governance can direct and enhance these ensembles by providing recommendations and insights that to ensure the output generated produces the highest possible value for the organisation

Data Literacy - What is it and how can we make it happen?

With the advent of the Internet and particularly Open Data, data literacy (the ability of non-specialists to make use of data) is rapidly becoming an essential life skill comparable to other types of literacy. However, it is still poorly defined and there is much to learn about how best to increase data literacy both amongst children and adults. This issue addresses both the definition of data literacy and current efforts on increasing and sustaining it. A feature of the issue is the range of contributors. While there are important contributions from the UK, Canada and other Western countries, these are complemented by several papers from the Global South where there is an emphasis on grounding data literacy in context and relating it the issues and concerns of communities

Exploring data value assessment: a survey method and investigation of the Perceived relative importance of data value dimensions

This paper describes the development and execution of a data value assessment survey of data professionals and academics. Its purpose was to explore more effective data value assessment techniques and to better understand the perceived relative importance of data value dimensions for data practitioners. This is important because despite the current deep interest in data value, there is a lack of data value assessment techniques and no clear understanding of how individual data value dimensions contribute to a holistic model of data value. A total of 34 datasets were assessed in a field study of 20 organisations in a range of sectors from finance to aviation. It was found that in 17 out of 20 of the organisations contacted that no data value assessment had previously taken place. All the datasets evaluated were considered valuable organisational assets and the operational impact of data was identified as the most important data value dimension. These results can inform the community’s search for data value models and assessment techniques. It also assists further development of capability maturity models for data value assessment and monitoring. This is to our knowledge the first publication of the underlying data for a multi-organization data value assessment and as such it represents a new stage in the evolution of evidence-based data valuation

National Language Technology Platform (NLTP) : overall view

The work in progress on the CEF Action National Language Technology Platform (NLTP) is presented. The Action aims at combining the most advanced Language Technology (LT) tools and solutions in a new state-of-the-art, Artificial Intelligence (AI) driven, National Language Technology Platform (NLTP).peer-reviewe

National language technology platform for public administration

This article presents the work in progress on the collaborative project of several European countries to develop National Language Technology Platform (NLTP). The project aims at combining the most advanced Language Technology tools and solutions in a new, state-of-the-art, Artificial Intelligence driven, National Language Technology Platform for five EU/EEA official and lower-resourced languages.peer-reviewe

A Semantic Data Value Vocabulary Supporting Data Value Assessment and Measurement Integration

In this paper we define the Data Value Vocabulary (DaVe) that allows for the comprehensive representation of data value. This vocabulary enables users to extend it using data value dimensions as required in the context at hand. DaVe caters for the lack of consensus on what characterises data value, and also how to model it. This vocabulary will allow users to monitor and asses data value throughout any value creating or data exploitation efforts, therefore laying the basis for effective management of value and efficient value exploitation. It also allows for the integration of diverse metrics that span many data value dimensions and which most likely pertain to a range of different tools in different formats. This data value vocabulary is based on requirements extracted from a number of value assessment use cases extracted from literature, and is evaluated using Gruber?s ontology design criteria, and by instantiating it in a deployment case study