Data Smells in Public Datasets

The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife preservation, autonomous driving and criminal justice system calls for a data-centric approach to AI. Data scientists spend the majority of their time studying and wrangling the data, yet tools to aid them with data analysis are lacking. This...

Automated Sample Ratio Mismatch (SRM) Detection and Analysis

Background: Sample Ratio Mismatch (SRM) checks can help detect data quality issues in online experimentation [3]. Not all experimentation platforms provide these checks as part of their solution. Users of these platforms must therefore manually check for SRM, or rely on additional processes—such as checklists [2]—or automation. Objective: To...

A Checklist to Combat Cognitive Biases in Crowdsourcing

Recent research has demonstrated that cognitive biases such as the confirmation bias or the anchoring effect can negatively affect the quality of crowdsourced data. In practice, however, such biases go unnoticed unless specifically assessed or controlled for. Task requesters need to ensure that task workflow and design choices do not trigger...

Using Business Data in Customs Risk Management: Data Quality and Data Value Perspective

With the rise of data analytics use in government, government organizations are starting to explore the possibilities of using business data to create further public value. This process, however, is far from straightforward: key questions that governments need to address relate to the quality of this external data and the value it brings. In the...

Citizens’ Trust in Open Government Data: A Quantitative Study about the Effects of Data Quality, System Quality and Service Quality

Previous research assumes that poor quality of Open Government Data (OGD), OGD portals, and the services provided for OGD may result in reduced trust of citizens in OGD. However, studies that empirically test this assumption are scarce. Using the Information Systems (IS) Success Model as a theoretical basis, this study aims to examine the...

Data Governance as Success Factor for Data Science

More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures...

Challenges with obstacle data for manned and unmanned aviation

The objective of this paper is to raise awareness about the significance of collecting and ensuring the quality of the obstacle data required for the safety of air navigation for both manned and unmanned aviation. This information could be of importance to geodetic, CityGML, 3D model and Building Information Management (BIM) community. With the...

Relating big data and data quality in financial service organizations

Today’s financial service organizations have a data deluge. A number of V’s are often used to characterize big data, whereas traditional data quality is characterized by a number of dimensions. Our objective is to investigate the complex relationship between big data and data quality. We do this by comparing the big data characteristics with...

Antecedents of big data quality: An empirical examination in financial service organizations

Big data has been acknowledged for its enormous potential. In contrast to the potential, in a recent survey more than half of financial service organizations reported that big data has not delivered the expected value. One of the main reasons for this is related to data quality. The objective of this research is to identify the antecedents of...

Participation and Data Quality in Open Data use: Open Data Infrastructures Evaluated

Infrastructures may improve the use of Open Government Data (OGD) by providing insight in how individuals can participate in data reuse and in the quality of open data. Yet, most OGD infrastructures do not support such activities. The objective of this paper is to evaluate the importance and usability of participation mechanisms and data quality...

The effect of inaccurate traffic data for ramp metering: Comparing loop detectors and cameras using information utility

This paper introduces a topic which is of growing interest in the traffic control and management community, namely the relation between traffic data quality and the efficiency of traffic management. After some explanatory background and state of the art, it will become clear that no standard approach is available for traffic engineers to...

Trends in detecting changes from repeated laser scanning data

Change detection is an important application of laser scanning data. It is also a challenging application as errors that are inevitably present when determining the geometric state of a scene of interest in a certain epoch will somehow add up when comparing the geometric state between epochs. As a consequence it is often difficult to distinguish...

The Impact of Loop Detector Distance and Floating Car Data Penetration Rate on Queue Tail Warning

There is a growing interest in the traffic community about the relation between traffic data quality and the efficiency of traffic management. Data collection is expensive and if the same level of traffic performance can be reached with less data or if traffic management becomes more efficient with better data, then that is interesting for a lot...

What is required to guarantee data quality for regional traffic management. Implementation of traffic data quality management in the Middle Netherlands

VERDER, a partnership of different governmental organizations in the Middle Netherlands, initiated different projects for regional traffic management to better use existing road capacity and safeguard mobility in the Middle Netherlands. This active form of traffic management requires traffic data with a certain quality level, although this is...

Facilitating Adoption of International Information Infrastructures: A Living Labs Approach

One of the key challenges that governments face in supervising international supply chains is the need for improving the quality of data accompanying the logistics flow. In many supply chains, individual parties in the chain work with low quality data for their operations and compliance, even though somewhere in the supply chain, better data is...