Perceptions of data quality dimensions and data roles

Several authors stress the importance of data’s crucial foundation for operational, tactical and strategic decisions (e.g., Redman 1998, Tee et al. 2007). Data provides the basis for decision making as data collection and processing is typically associated with reducing uncertainty in order to make more effective decisions (Daft and Lengel 1986). While the first series of investments of Information Systems/Information Technology (IS/IT) into organizations improved data collection, restricted computational capacity and limited processing power created challenges (Simon 1960). Fifty years on, capacity and processing problems are increasingly less relevant; in fact, the opposite exists. Determining data relevance and usefulness is complicated by increased data capture and storage capacity, as well as continual improvements in information processing capability. As the IT landscape changes, businesses are inundated with ever-increasing volumes of data from both internal and external sources available on both an ad-hoc and real-time basis. More data, however, does not necessarily translate into more effective and efficient organizations, nor does it increase the likelihood of better or timelier decisions. This raises questions about what data managers require to assist their decision making processes.

A data mining approach to improve the automated quality of data

This thesis describes the development of a robust and novel prototype to address the data quality problems that relate to the dimension of outlier data. It thoroughly investigates the associated problems with regards to detecting, assessing and determining the severity of the problem of outlier data; and proposes granule-mining based alternative techniques to significantly improve the effectiveness of mining and assessing outlier data.

Priorities for developing and evaluating data quality characteristics of road crash data in Australia

The National Road Safety Strategy 2011-2020 outlines plans to reduce the burden of road trauma via improvements and interventions relating to safe roads, safe speeds, safe vehicles, and safe people. It also highlights that a key aspect in achieving these goals is the availability of comprehensive data on the issue. The use of data is essential so that more in-depth epidemiologic studies of risk can be conducted as well as to allow effective evaluation of road safety interventions and programs. Before utilising data to evaluate the efficacy of prevention programs it is important for a systematic evaluation of the quality of underlying data sources to be undertaken to ensure any trends which are identified reflect true estimates rather than spurious data effects. However, there has been little scientific work specifically focused on establishing core data quality characteristics pertinent to the road safety field and limited work undertaken to develop methods for evaluating data sources according to these core characteristics. There are a variety of data sources in which traffic-related incidents and resulting injuries are recorded, which are collected for a variety of defined purposes. These include police reports, transport safety databases, emergency department data, hospital morbidity data and mortality data to name a few. However, as these data are collected for specific purposes, each of these data sources suffers from some limitations when seeking to gain a complete picture of the problem. Limitations of current data sources include: delays in data being available, lack of accurate and/or specific location information, and an underreporting of crashes involving particular road user groups such as cyclists. This paper proposes core data quality characteristics that could be used to systematically assess road crash data sources to provide a standardised approach for evaluating data quality in the road safety field. The potential for data linkage to qualitatively and quantitatively improve the quality and comprehensiveness of road crash data is also discussed.

Visual metrics for the evaluation of sensor data quality in outdoor perception

This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke. An example of application is given with monocular SLAM estimating the pose of the UGV while smoke is present in the environment. It is shown that the proposed novel quality metric can be used to anticipate situations where the quality of the pose estimate will be significantly degraded due to the input image data. This leads to decisions of advantageously switching between data sources (e.g. using infrared images instead of visual images).

Visual metrics for the evaluation of sensor data quality in outdoor perception

This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke.

'Explain in plain English' questions revisited : data structures problems

Recent studies have linked the ability of novice (CS1) programmers to read and explain code with their ability to write code. This study extends earlier work by asking CS2 students to explain object-oriented data structures problems that involve recursion. Results show a strong correlation between ability to explain code at an abstract level and performance on code writing and code reading test problems for these object-oriented data structures problems. The authors postulate that there is a common set of skills concerned with reasoning about programs that explains the correlation between writing code and explaining code. The authors suggest that an overly exclusive emphasis on code writing may be detrimental to learning to program. Non-code writing learning activities (e.g., reading and explaining code) are likely to improve student ability to reason about code and, by extension, improve student ability to write code. A judicious mix of code-writing and code-reading activities is recommended.

The potential of XML technology as an answer to the data interchange problems of the construction industry

The complex supply chain relations of the construction industry, coupled with the substantial amount of information to be shared on a regular basis between the parties involved, make the traditional paper-based data interchange methods inefficient, error prone and expensive. The successful information technology (IT) applications that enable seamless data interchange, such as the Electronic Data Interchange (EDI) systems, have generally failed to be successfully implemented in the construction industry. An alternative emerging technology, Extensible Markup Language (XML), and its applicability to streamline business processes and to improve data interchange methods within the construction industry are analysed, as is the EDI technology to identify the strategic advantages that XML technology provides to overcome the barriers to implementation. In addition, the successful implementation of XML-based automated data interchange platforms for a large organization, and the proposed benefits thereof, are presented as a case study.

ERP stakeholders’ perceptions of data quality and utility

While data quality has been identified as a critical factor associated with enterprise resource planning (ERP) failure, the relationship between ERP stakeholders, the information they require and its relationship to ERP outcomes continues to be poorly understood. Applying stakeholder theory to the problem of ERP performance, we put forward a framework articulating the fundamental differences in the way users differentiate between ERP data quality and utility. We argue that the failure of ERPs to produce significant organisational outcomes can be attributed to conflict between stakeholder groups over whether the data contained within an ERP is of adequate ‘quality’. The framework provides guidance as how to manage data flows between stakeholders, offering insight into each of their specific data requirements. The framework provides support for the idea that stakeholder affiliation dictates the assumptions and core values held by individuals, driving their data needs and their perceptions of data quality and utility.

Quality of child abuse information for measurement and surveillance : linking hospital and child welfare data

National estimates of the prevalence of child abuse-related injuries are obtained from a variety of sectors including welfare, justice, and health resulting in inconsistent estimates across sectors. The International Classification of Diseases (ICD) is used as the international standard for categorising health data and aggregating data for statistical purposes, though there has been limited validation of the quality, completeness or concordance of these data with other sectors. This research study examined the quality of documentation and coding of child abuse recorded in hospital records in Queensland and the concordance of these data with child welfare records. A retrospective medical record review was used to examine the clinical documentation of over 1000 hospitalised injured children from 20 hospitals in Queensland. A data linkage methodology was used to link these records with records in the child welfare database. Cases were sampled from three sub-groups according to the presence of target ICD codes: Definite abuse, Possible abuse, unintentional injury. Less than 2% of cases coded as being unintentional were recoded after review as being possible abuse, and only 5% of cases coded as possible abuse cases were reclassified as unintentional, though there was greater variation in the classification of cases as definite abuse compared to possible abuse. Concordance of health data with child welfare data varied across patient subgroups. This study will inform the development of strategies to improve the quality, consistency and concordance of information between health and welfare agencies to ensure adequate system responses to children at risk of abuse.

Using image quality metrics to detect smoke-affected laser data

Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.

Health Information Exchange in the wake of ‘big data’ paradigm – the theme is from ‘quality of information to quality of life’

Health Information Exchange (HIE) is an interesting phenomenon. It is a patient centric health and/or medical information management scenario enhanced by integration of Information and Communication Technologies (ICT). While health information systems are repositioning complex system directives, in the wake of the ‘big data’ paradigm, extracting quality information is challenging. It is anticipated that in this talk, ICT enabled healthcare scenarios with big data analytics will be shared. In addition, research and development regarding big data analytics, such as current trends of using these technologies for health care services and critical research challenges when extracting quality of information to improve quality of life will be discussed.