Policy and Decision Making as a Focus for Integrated Data Management

Author(s):  
J. Mayda
Author(s):  
E. E. Akimkina

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.


10.28945/2192 ◽  
2015 ◽  
Author(s):  
Rogério Rossi ◽  
Kechi Hirama

[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] Considering that big data is a reality for an increasing number of organizations in many areas, its management represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial dimensions to facilitate the management of big data in any organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management must be supported by technology, people and processes; hence, this article discusses these three dimensions: the technologies for storage, analysis and visualization of big data; the human aspects of big data; and, in addition, the process management involved in a technological and business approach for big data management.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Elizaveta Gavrikova ◽  
Irina Volkova ◽  
Yegor Burda

PurposeThe purpose of this paper is to design a framework for asset data management in power companies. The authors consider asset data management from a strategic perspective, linking operational-level data with corporate strategy and taking into account the organizational context and stakeholder expectations.Design/methodology/approachThe authors conducted a multiple case study based on a literature review and three series of in-depth interviews with experts from three Russian electric power companies.FindingsThe main challenge in asset data management for electric power companies is the increasing amount and complexity of asset data, which is frequently incomplete or inaccurately collected, hard to translate to managerial language, focused primarily on the operational level. Such fragmented approach negatively affects strategic decision-making. The proposed framework introduces a holistic approach, provides context and accountability for decision-making and attributes data flows, roles and responsibilities to different management levels.Research limitations/implicationsThe limitations of our study lie in the exploratory nature of case study research and limited generalization of the observed cases. However, the authors used multiple sources of evidence to ensure validity and generalization of the results. This article is a first step toward further understanding of the issues of transformation in power companies and other asset intensive businesses.Originality/valueThe novelty of the framework lies in the scope, focus and detailed treatment of asset data management in electric power companies.


2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Azizah Abdul Rahman ◽  
Nooradilla Abu Hasan ◽  
Norminshah A. Lahad

Business Intelligence (BI) has embarked on decision-making setting. This has influenced many organizations from different industries that are located in diverse regions to implement BI. Critical Successful Factors (CSF) becomes the guideline for the implementer to adopt BI successfully. However, lack of BI knowledge and weak consideration of BI CSF led to the failure of BI implementation project. Several issues and challenges have been identified during the BI implementation. In addition, other researchers rarely discussed this subject. Thus, the objective of this paper is to recognize the issues and challenges on BI implementation. Through qualitative method, BI practices systematically described the purpose of BI execution on selected organizations, industries and regions. It has given the path towards the issues and challenges of BI implementation. The identified issues and challenges are defining the business goal, data management, limited funding, training and user acceptance as well as the lack of expertise issues. The findings categorized the issues and challenges into three dimensions of CSF for BI implementation, which are Organization, Process and Technology dimension. Limitation in this study requires future researchers to study in details of these issues and challenges including the solutions and the impact of BI implementation.


2016 ◽  
Vol 56 (1) ◽  
pp. 265 ◽  
Author(s):  
Aidan Shields ◽  
Marivic Mirhan ◽  
Emma Stratford

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.


2012 ◽  
Vol 490-495 ◽  
pp. 2091-2094 ◽  
Author(s):  
Bing Jian Wu ◽  
Hui Tian Zhu ◽  
Yuan Wang

With the edge of the application and key technologies, the management ideas that are the integration of equipment development and production stage of technology status management put forward and combed this project, providing comprehensive data management environment, providing rapid decision-making, timely information and scientific basis


Author(s):  
C. Ellul ◽  
V. Coors ◽  
S. Zlatanova ◽  
R. Laurini ◽  
M. Rumor

<p><strong>Abstract.</strong> Simply defined, a Smart City is a city overlaid by a digital layer, which is used for the governance of the city. A Smart City uses intelligent technology to enhance our quality of life in urban environments, bringing together people and data from disparate sources such as sensors, demographics, topographic and 3D mapping, Building Information Models and many more. Increasingly, Smart Cities use this data in a variety of ways, to address key challenges related to transportation, communications, air quality, noise, well-being of the citizens, decision making relating to education and health and urban planning, as well as in relation to initiatives such as startups and fostering economic growth and employment within the city. As more data becomes available, the challenges of storing, managing and integrating such data are also multiplied.</p><p> This increasing interest in Smart Cities world-wide, along with a growing understanding of the importance of integrating “Smart” data with other data and wider applications for the benefit of citizens, made the choice of hosting the third Smart Data, Smart Cities conference in Delft – in conjunction with three other conferences – a very natural one. Together the four conferences were held during the week of 1st–5th October 2018, and alongside SDSC participants were invited to attend the ISPRS Technical Commission IV Symposium, the 13th 3D GeoInfo Conference and the 6th International FIG Workshop on 3D Cadastres. Participant interaction – and the ability to attend sessions across the four events – was particularly encouraged. SDSC 2018 itself was organised by the Urban Data Management Society (UDMS www.udms.net), ISPRS and TU Delft (the Delft University of Technology), and Professor Volker Coors Chaired the SDSC committee.</p><p> As in previous years, three key conference themes were proposed to represent the Smart Cities: <b>Smart Data</b> (sensor network databases, on-the-fly data mining, geographic and urban knowledge modeling and engineering, green computing, urban data analytics and big data, big databases and data management), <b>Smart People</b> (volunteered information, systems for public participation) and <b>Smart Cities</b> (systems of territorial intelligence, systems for city intelligence management,3D modeling of cities, internet of things, social networks, monitoring systems, mobility and transportation, smart-city-wide telecommunications infrastructure, urban knowledge engineering, urban dashboard design and implementation, new style of urban decision-making systems, geovisualization devoted to urban problems, disaster management systems).</p><p> This volume consists of 18 papers, which were selected from 34 submissions on the basis of double blind review, with each paper being reviewed by a minimum of three reviewers. These papers present novel research concerning the use of spatial information and communication technologies in Smart Cities, addressing different aspects of Smart Data and Smart Citizens. The selected papers tackle different aspects of Smart Cities: 3D; Citizen Engagement; transport, sustainable mobility; dashboards and web GIS; citizen engagement and participation; sensors; urban decision making.</p><p> The editors are grateful to the members of the Scientific Committee for their time and valuable comments, which contributed to the high quality of the papers. Reviews were contributed by: Giorgio Agugiaro, Maria Antoniabrovelli, Ken Arroyoohori, Martina Baucic, Michela Bertolotto, Pawel Boguslawski, Azedine Boulmakoul, Caesar Cardenas, Ofelia Cervantes, Volker Coors, Isabel Cruz, Vincenzo Delfatto, Claire Ellul, Tarun Ghawana, Gesquiere Gilles, Gerhard Groeger, Eberhard Gulch, Jan-Henrik Haunert, Stephen Hirtle, Umit Isikdag, Martin Kada, Snjezana Knezic, Robert Laurini, Liu Liu, Ed Manley, Viviana Mascardi, Marco Minghini, Raul Monroy, Regina Motz, Beniamino Murgante, Marco Painho, Dev Paudyal, Alenka Poplin, Ivana Racetin, Ismail Rakip Karas, Preston Rodrigues, David Sol, Wei Tu, Wei Tu, Genoveva Vargas, Kavita Vemuri, Edward Verbree, Mingshu Wang, Maribel Yasminasantos, Sisi Zlatanova. We are also grateful to the work of the local organising committee at TU Delft, without whom this conference would not have been possible. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018 3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands</p>


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