Maximising value by using good data to drive good decisions

2016 ◽  
Vol 56 (1) ◽  
pp. 265 ◽  
Author(s):  
Aidan Shields ◽  
Marivic Mirhan ◽  
Emma Stratford
Keyword(s):  
Decision Making ◽  
Data Management ◽  
Situational Awareness ◽  
Operating Cost ◽  
Petroleum Industry ◽  
Problem Analysis ◽  
Time Data ◽  
Data Accessibility ◽  
Good Data ◽  
Management Standards

The industry-wide move towards big data and the digital oilfield is underpinned by good data. This paper outlines a suite of data management standards, systems and processes, and provides examples of how these have led to improved decision making. The approach involved the development of standards and streamlined business processes followed by the implementation of systems focusing on production data accessibility, quality and integration. Accessibility was addressed by making real-time data readily available from multiple devices so users spend more time using data instead of locating it. Quality was improved through the implementation of processes such as operational data validation (ODV) and production allocation (PA). Integration was facilitated so that users could view data from various systems in a single location. The implementation of data management standards, systems and processes led to improved decision making in the areas of external reporting, operating cost, safety, environment, commercial, reservoir management, well surveillance, and situational awareness. In particular, implementation of the ODV process ensured the completeness, accuracy and timeliness of data from reservoir to sales. Furthermore, improved accessibility and integration increased situational awareness, reduced troubleshooting time, and improved problem analysis. While the concept of data management and quality control is not new, the novelty is in the approach of developing robust standards, implementation of systems based on these standards, and creating the supporting business process and culture aligned to what drives value in the organisation. This is easily transferable and adaptable across all facets of the petroleum industry.

scholarly journals The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Computation ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 12
Author(s):  
Evangelos Maltezos ◽  
Athanasios Douklias ◽  
Aris Dadoukis ◽  
Fay Misichroni ◽  
Lazaros Karagiannidis ◽  
...  
Keyword(s):  
Decision Making ◽  
Object Detection ◽  
Situational Awareness ◽  
Machine Learning Techniques ◽  
Measurement Instruments ◽  
Time Data ◽  
Modular Architecture ◽  
Seamless Integration ◽  
Object Detection And Tracking ◽  
Detection And Tracking

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

Structuring and visualization of indicators in multidimensional data cubes

2018 ◽  
pp. 79-87
Author(s):  
E. E. Akimkina
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Management ◽  
Multidimensional Data ◽  
End User ◽  
Processing Technologies ◽  
Data Cubes ◽  
Multidimensional Visualization ◽  
Multidimensional Data Cubes ◽  
Practical Recommendations

The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

Attention Allocation to Physically Separate Task and Situation Displays in a Command and Control Setting

2020 ◽  
Vol 64 (1) ◽  
pp. 1130-1134
Author(s):  
H. Golan ◽  
A. Parush ◽  
E. Jaffe
Keyword(s):  
Decision Making ◽  
Emergency Medical Services ◽  
Situational Awareness ◽  
Command And Control ◽  
Attention Allocation ◽  
Incident Management ◽  
Allocation Strategy ◽  
Individual Task ◽  
Improved Performance ◽  
And Control

Using a simulated Emergency Medical Services (EMS) dispatch center during multi-casualty incident management, this study explored whether the presence of a separate situation display in a Command and Control (C2) setting might require attention at the expense of attending an individual task display, and how it influenced performance and situational awareness. Overall, participants always attended the task display more than the situation display. However, the situation display drew attention at the expense of attending less the task display. The presence of the situation display was related to improved performance and better situational awareness (SA), particularly in the projection level of the SA, which could account also for the better decision-making performance. Participants may have developed an attention allocation strategy to effectively utilize the information of the situation display and execute their tasks on the task display.

Co-Design of an Electronic Dashboard to Support Coproduction of Care in Pediatric Rheumatic Disease (Preprint)

2021 ◽  
Author(s):  
Alysha Taxter ◽  
Lisa Johnson ◽  
Doreen Tabussi ◽  
Yukiko Kimura ◽  
Brittany Donaldson ◽  
...  
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Clinical Data ◽  
The United States ◽  
Shared Decision ◽  
Time Data ◽  
Vertical Orientation ◽  
Patient Reported ◽  
Data Elements ◽  
The Right

BACKGROUND Coproduction of care involves patients and families partnering with their clinicians and care teams, with the premise that each brings their own perspective, knowledge, and expertise, as well as their own values, goals, and preferences to the partnership. Dashboards can display meaningful patient and clinical data to assess how a patient is doing and inform shared decision making. Increasing communication between patients and care teams is particularly important for children with chronic conditions, such as juvenile idiopathic arthritis (JIA), which is the most common, chronic rheumatic condition of childhood, and is associated with increased pain, decreased function, and decreased quality of life. OBJECTIVE We aimed to design a dashboard prototype for use in coproducing care for JIA patients. We evaluated the context use and needs of end users, obtained consensus on the necessary dashboard data elements, and constructed display prototypes to inform meaningful discussions for coproduction. METHODS A human-centered design approach involving parents, patients, clinicians, and care team members was used to develop a dashboard to support coproduction of care in four diverse ambulatory pediatric rheumatology clinics across the United States. We engaged a multidisciplinary team (n=18) of patients/parents, clinicians, nurses, and staff during an in-person kick-off meeting, followed by bi-weekly meetings. We also leveraged advisory panels. Teams mapped workflows and patient journeys, created personas, and developed dashboard sketches. Final necessary dashboard components were determined using Delphi consensus voting. Low-tech dashboard testing was completed during clinic visits, and visual display prototypes were iterated using PDSA methodology. Patients and providers were surveyed about their experiences. RESULTS Teams achieved consensus on what data matters most at point-of-care to support JIA patients, families, and clinicians partnering together to make the best possible decisions for better health. Notable themes included: the right data, in the right place, at the right time; data in once for multiple purposes; patient and family self-management components; and opportunity for education and increased transparency. A final set of 11 dashboard data elements were identified which include patient-reported outcomes, clinical data, and medications. Important design considerations include incorporation of real-time data, clearly labeled graphs, and vertical orientation to facilitate review and discussion. Prototype paper testing with 36 patients/families yielded positive feedback about the dashboard’s usefulness during clinic discussions, helped to talk about what mattered most, and informed healthcare decision making. CONCLUSIONS Our study developed a dashboard prototype that displays patient-reported and clinical data over time, along with medications, that can be used during a clinic visit to support meaningful conversations and shared decision making between JIA patients/families and their clinicians and care teams. CLINICALTRIAL N/A

Sign in / Sign up

Export Citation Format

Share Document