Relevant bibliographies by topics / Data maturity

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data maturity'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Data maturity"

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Knoflach, Lukas, Lin Shao, and Torsten Ullrich. "A Comprehensive Data Maturity Model for Data Pre-Analysis." Data 10, no. 4 (2025): 55. https://doi.org/10.3390/data10040055.

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Data analysis is widely used in research and industry where there is a need to extract information from data. A significant amount of time within a data analysis project is required to prepare the data for subsequent analysis. This paper presents a comprehensive weighted maturity model to estimate the readiness of data for subsequent data analysis, with the goal of avoiding delays due to data quality problems. The maturity model uses a questionnaire with nine criteria to determine the maturity level of data preparation. The maturity model is integrated into a web application that provides an a
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Hochkamp, Florian, Anne Antonia Scheidler, and Markus Rabe. "Review of Maturity Models for Data Mining and Proposal of a Data Preparation Maturity Model Prototype for Data Mining." Computers 14, no. 4 (2025): 146. https://doi.org/10.3390/computers14040146.

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Companies face uncertainties evaluating their own capabilities when implementing data analytics or data mining. Data mining is a valuable process used to analyze data and support decisions based on the knowledge generated, creating a pipeline from the phases of data collection to data preparation and data mining. In order to assess the target state of a company in terms of its data mining capabilities, maturity models are viable tools. However, no maturity models exist that focus on the data mining process and its particular phases. This article discusses existing maturity models in the broade
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Wagner, Michael, and Christin Henzen. "Quality Assurance for Spatial Research Data." ISPRS International Journal of Geo-Information 11, no. 6 (2022): 334. http://dx.doi.org/10.3390/ijgi11060334.

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In Earth System Sciences (ESS), spatial data are increasingly used for impact research and decision-making. To support the stakeholders’ decision, the quality of the spatial data and its assurance play a major role. We present concepts and a workflow to assure the quality of ESS data. Our concepts and workflow are designed along the research data life cycle and include criteria for openness, FAIRness of data (findable, accessible, interoperable, reusable), data maturity, and data quality. Existing data maturity concepts describe (community-specific) maturity matrices, e.g., for meteorological
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Cech, Thomas G., Trent J. Spaulding, and Joseph A. Cazier. "Data competence maturity: developing data-driven decision making." Journal of Research in Innovative Teaching & Learning 11, no. 2 (2018): 139–58. http://dx.doi.org/10.1108/jrit-03-2018-0007.

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Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to se
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Surendranadha Reddy Byrapu Reddy. "Unified Data Analytics Platform For Financial Sector Using Big Data." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 3878–85. http://dx.doi.org/10.52783/tjjpt.v44.i4.1560.

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Organizations in today's data-driven digital economy are seeking ways to leverage the immense value of massive amounts of information so as to make more informed decisions. They are able to not only uncover new prospects, but also learn more and improve their performance thanks to big data analytics. Although many businesses have poured resources into big data analytics projects, most have failed to reap the benefits. While there has been a lot of research into the topic of big data analytics, relatively little is known about the strategies that organizations use to merge the various aspects o
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Berndtsson, Mikael, and Stefan Ekman. "Assessing Maturity in Data-Driven Culture." International Journal of Business Intelligence Research 14, no. 1 (2023): 1–17. http://dx.doi.org/10.4018/ijbir.332813.

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Research on assessing a group's maturity in data-driven culture is rare and fragmented. This article investigates how maturity in data-driven culture can be assessed from a historical perspective. A case study was done on how the Education Council evolved in analytics maturity and as a group during 2014-2023. The assessment showed that the Education Council experienced both successful progression of group development and usage of analytics, as well as regression in group development and analytics usage. The practical implications of the findings are that group leaders need to be aware of the i
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Wilantika, Nori, and Wahyu Catur Wibowo. "Data Quality Management in Educational Data." Jurnal Sistem Informasi 15, no. 2 (2019): 52–67. http://dx.doi.org/10.21609/jsi.v15i2.848.

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Every varsity in Indonesia is responsible for ensuring the completeness, the validity, the accuracy, and the currency of its educational data. The educational data is used for implementing higher-education quality assurance system and formulating policies related to universities and majors in Indonesia. Data quality assessment result indicates that educational data in Statistics Polytechnic did not meet completeness, validity, accuracy, and currency criteria. Data quality management maturity has been measured using Loshin’s Data Quality Maturity Model which result is in level 1 to level 2 of m
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Schmuck, Matthias. "Master data management as part of data governance: A maturity model to improve efficiency and trust in master data and thus business performance." Business Performance Review 2, no. 2 (2024): 20–34. http://dx.doi.org/10.22495/bprv2i2p2.

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An optimized master data management correlates with improved data quality, enhanced process integration and increased business agility, leading to overall better business performance. This study proposes a maturity model for structured master data management improvement that has emerged from analysing previous maturity model research, data governance, master data management and the practical experience of the researcher. The model comprises six maturity levels for eight design levels with 23 assessment factors, which are framed by six organizational factors. It extends previous maturity models
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Ryu, Kyung Seok, Joo Seok Park, and Jae Hong Park. "A Data Quality Management Maturity Model." ETRI Journal 28, no. 2 (2006): 191–204. http://dx.doi.org/10.4218/etrij.06.0105.0026.

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Stalla-Bourdillon, Sophie. "A Maturity Spectrum for Data Institutions." IEEE Security & Privacy 19, no. 5 (2021): 90–94. http://dx.doi.org/10.1109/msec.2021.3094985.

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Dissertations / Theses on the topic "Data maturity"

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Kollen, Christine, Inna Kouper, Mayu Ishida, Sarah Williams, and Kathleen Fear. "Research Data Services Maturity in Academic Libraries." American Library Association, Association of College and Research Libraries, 2017. http://hdl.handle.net/10150/622168.

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An ACRL white paper from 2012 reported that, at that time, only a small number of academic libraries in the United States and Canada offered research data services (RDS), but many were planning to do so within the next two years (Tenopir, Birch, and Allard, 2012). By 2013, 74% of the Association of Research Libraries (ARL) survey respondents offered RDS and an additional 23% were planning to do so (Fearon, Gunia, Pralle, Lake, and Sallans, 2013). The academic libraries recognize that the landscape of services changes quickly and that they need to support the changing needs of research and inst
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Rivera, Stephanie, Nataly Loarte, Carlos Raymundo, and Francisco Dominguez. "Data governance maturity model for micro financial organizations in Peru." SciTePress, 2017. http://hdl.handle.net/10757/656360.

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Micro finance organizations play an important role since they facilitate integration of all social classes to sustained economic growth. Against this background, exponential growth of data, resulting from transactions and operations carried out with these companies on a daily basis, becomes imminent. Appropriate management of this data is therefore necessary because, otherwise, it will result in a competitive disadvantage due to the lack of valuable and quality information for decision-making and process improvement. Data Governance provides a different approach to data management, as seen fro
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Vásquez, Zúñiga Daniel, Cruz Romina Kukurelo, Ibañez Carlos Raymundo, Francisco Dominguez, and Javier Moguerza. "Master data management maturity model for the microfinance sector in Peru." Association for Computing Machinery, 2018. http://hdl.handle.net/10757/624687.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>The microfinance sector has a strategic role since they facilitate integration and development of all social classes to sustained economic growth. In this way the actual point is the exponential growth of data, resulting from transactions and operations carried out with these companies on a daily basis, becomes imminent. Appropriate management of this data is therefore necessary because, otherwise, it will result in a competitive disadvantage d
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demirci, özlem. "Development of MBSE/UML Maturity Model." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Data- och elektroteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-13159.

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Garcia, Arturo, Luis Calle, Carlos Raymundo, Francisco Dominguez, and Javier M. Moguerza. "Personal data protection maturity model for the micro financial sector in Peru." Institute of Electrical and Electronics Engineers Inc, 2018. http://hdl.handle.net/10757/624636.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>The micro financial sector is a strategic element in the economy of developing countries since it facilitates the integration and development of all social classes and let the economic growth. In this point is the growth of data is high every day in sector like the micro financial, resulting from transactions and operations carried out with these companies on a daily basis. Appropriate management of the personal data privacy policies is therefo
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Grillo, Aderibigbe. "Developing a data quality scorecard that measures data quality in a data warehouse." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17137.

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The main purpose of this thesis is to develop a data quality scorecard (DQS) that aligns the data quality needs of the Data warehouse stakeholder group with selected data quality dimensions. To comprehend the research domain, a general and systematic literature review (SLR) was carried out, after which the research scope was established. Using Design Science Research (DSR) as the methodology to structure the research, three iterations were carried out to achieve the research aim highlighted in this thesis. In the first iteration, as DSR was used as a paradigm, the artefact was build from the r
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Myburgh, Suzanne. "Conceptualisation of a big data maturity model based on organisational decision making." Diss., University of Pretoria, 2015. http://hdl.handle.net/2263/52271.

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A big data maturity model, based on the usage of big data analytics in organisational decision making was conceptualised and then tested amongst the executives of South African listed companies.<br>Mini Dissertation (MBA)--University of Pretoria, 2015.<br>zk2016<br>Gordon Institute of Business Science (GIBS)<br>MBA<br>Unrestricted
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D, Vásquez, Daniel Vásquez, Romina Kukurelo, Carlos Raymundo, Francisco Dominguez, and Javier Moguerza. "Master data management maturity model for the successful of mdm initiatives in the microfinance sector in Peru." Association for Computing Machinery, 2018. http://hdl.handle.net/10757/624683.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>The microfinance sector has a strategic role since they facilitate integration and development of all social classes to sustained economic growth. In this way the actual point is the exponential growth of data, resulting from transactions and operations carried out with these companies on a daily basis, becomes imminent. Appropriate management of this data is therefore necessary because, otherwise, it will result in a competitive disadvantage d
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Agnesson, Daniel, and Necip Yener Önder. "Customer Maturity Analysis Tool : A case study in designing a Customer MaturityAnalysis Tool." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Informatik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-14799.

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The IT consultancy industry is characterized by knowledge intensive implementationprojects related to varying levels of standardized information system software. When faced with a large implementation project at a previously unknown customer various assessmentsis usually conducted to identify the level of fit between the software and organizationin order to plan and structure the implementation process. However, there are several aspects of the customer organization that potentially can affect the end result as well as the implementation process that is unrelated to the fit between the softwar
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Ampuero, Mendoza Libusi, and Carranza Rosa Alfaro. "Modelo de madurez Tecno-organizacional para la puesta en marcha exitosa de iniciativas de Data Governance." International Institute of Informatics and Systemics, IIIS, 2017. http://hdl.handle.net/10757/622492.

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Septima Conferencia Iberoamericana de Complejidad, Informatica y Cibernetica, CICIC 2017 - 7th Ibero-American Conference on Complexity, Informatics and Cybernetics, CICIC 2017; Orlando; United States; 21 March 2017 through 24 March 2017; Code 131437<br>Data management has undergone several changes over the last few years, leaving behind the days when it was necessary to convince people about the value of data in their organizations. Over the years, the volume and expense of data management have been increasing at a high rate. Today, organizations need to have strategic management that allows t
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Books on the topic "Data maturity"

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1958-, Wilson Thomas C., ed. Impact of technology on resource sharing: Experimentation and maturity. Haworth Press, 1992.

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M, Finn Thomas, Johnson Ronald Carl 1950-, and Geological Survey (U.S.), eds. Thermal maturity data used for the assessment of gas resources in Wind River Basin, Wyoming. U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Dawe, Earle G. Analysis of length, sex and maturity data for short-finned squid (Illex illecebrosus) from samples collected at Newfoundland, Canada, 1965-88. Science Branch, Dept. of Fisheries and Oceans, 1992.

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Osadetz, K. G. BURIAL, a program that calculates and plots the burial history curves and thermal maturity history of a stratigraphic section. Geological Survey of Canada, 1994.

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W, Schmoker J., and Geological Survey (U.S.), eds. Porosity, depth, and thermal-maturity data for sandstones of the Anadarko Basin, Oklahoma, and other selected locations in the northern hemisphere. Dept. of the Interior, U.S. Geological Survey, 1993.

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Bill, Curtis. People CMM: A framework for human capital management. 2nd ed. Addison-Wesley, 2010.

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Bill, Curtis. People CMM: A framework for human capital management. 2nd ed. Addison-Wesley, 2010.

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Das, S. R. Mixed-longitudinal growth data for 22 measures, the Sarsuna Barisha series (301 boys and 261 girls, 6 months to maturity), West Bengal, India. Anthropological Survey of India, Govt. of India, Dept. of Culture, 1985.

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Das, S. R. Mixed-longitudinal growth data for 22 measures, the Sarsuna Barisha series (301 boys and 261 girls, 6 months to maturity), West Bengal, India. Anthropological Survey of India, Govt. of India, Dept. of Culture, 1985.

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Carruthers, Andrew, and Sahir Ahmed. Maturing the Snowflake Data Cloud. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9340-9.

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Book chapters on the topic "Data maturity"

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Burnette, Gerald A. "Maturity." In Managing Environmental Data. CRC Press, 2021. http://dx.doi.org/10.1201/9781003131953-15.

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Mahanti, Rupa. "Data Governance Maturity Models." In Data Governance Success. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5086-4_4.

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Caballero, Ismael, Fernando Gualo, Moisés Rodríguez, and Mario Piattini. "Maturity Models for Data Governance." In Data Governance. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43773-1_7.

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Curry, Edward, and Tuomo Tuikka. "An Organizational Maturity Model forData Spaces: A Data Sharing Wheel Approach." In Data Spaces. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98636-0_2.

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AbstractThis chapter presents a maturity model for Data Spaces, which provides a management system with associated improvement roadmaps that guide strategies to continuously improve, develop, and manage the data space capability within their organization. It highlights the challenges with data sharing and motivates the benefit of maturity models. This chapter describes the Maturity Model for Data Spaces (MM4DS) and its use to determine an organization’s data space capability maturity. The MM4DS takes an organization’s user-centric/demand-side perspective utilizing a data space. The development
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Baškarada, Saša. "Analysis of Data." In Information Quality Management Capability Maturity Model. Vieweg+Teubner, 2009. http://dx.doi.org/10.1007/978-3-8348-9634-6_4.

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Burk, Scott, and Gary Miner. "Data and Analytics Maturity Models." In It's All Analytics, Part III. Productivity Press, 2023. http://dx.doi.org/10.4324/9780429343971-2.

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Vogel, Justus, Johannes Hollenbach, Alexander Haering, David Ehlig, and Alexander Geissler. "Digital Maturity Data: Extracting Insights for Health System Management." In Digital Maturity in Hospitals. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80704-6_4.

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Seebacher, Uwe. "The Predictive Intelligence Maturity Model." In Predictive Intelligence for Data-Driven Managers. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69403-6_4.

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Vogel, Justus, Johannes Hollenbach, Alexander Haering, David Ehlig, and Alexander Geissler. "Correction to: Digital Maturity Data: Extracting Insights for Health System Management." In Digital Maturity in Hospitals. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80704-6_20.

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Sargiotis, Dimitrios. "Data Governance Maturity Models: Assessing and Enhancing Your Program." In Data Governance. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67268-2_17.

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Conference papers on the topic "Data maturity"

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Hlel, Jihen, Nesrine Ben Yahia, and Narjès Ben Saoud. "Explainable Assessment Model for Digital Transformation Maturity." In 14th International Conference on Data Science, Technology and Applications. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013459300003967.

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Hendrawan, Fadhil Rozi, and Widia Febriyani. "Assessing Data Quality Maturity Models: Enhancing Organizational Readiness and Improvement." In 2024 IEEE International Conference on Computing (ICOCO). IEEE, 2024. https://doi.org/10.1109/icoco62848.2024.10928198.

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Luan, Zhong, Yanhua Wu, and Ping Li. "Research and Application of Railway Data Management Maturity Assessment Model." In 2024 14th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2024. https://doi.org/10.1109/itme63426.2024.00196.

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Kaewkamol, Porntida, and Jirapipat Thanyaphongphat. "Data Maturity Model and Goal-Oriented Approach to Elicit Business Requirements." In 2024 5th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON). IEEE, 2024. http://dx.doi.org/10.1109/times-icon61890.2024.10630740.

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Dhoundiyal, Sandeepan, Shivam Kumar, Debosmita Paul, Malcolm Aranha, Guneshwar Thangjam, and Alok Porwal. "Optimal Feature Selection for Estimating Lunar Soil Maturity from Spectroscopic Data." In 2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2024. https://doi.org/10.1109/whispers65427.2024.10876492.

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Shabou, Basma Makhlouf. "Maturity Assessment of Appraisal Processes in the AI Age: Ongoing Framework and Measuring Method." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10826084.

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N, Aishwarya, Devisowjanya P, Deshana Vikas Shah, and Raavi Niharika. "Dragon Fruit Maturity Detection and Quality Grading with Scalable Deep Neural Networks." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914783.

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Al-Ruithe, Majid, and Elhadj Benkhelifa. "Cloud data governance maturity model." In ICC '17: Second International Conference on Internet of Things, Data and Cloud Computing. ACM, 2017. http://dx.doi.org/10.1145/3018896.3036394.

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Cheng, Guangming, Yao Li, Zhiwei Gao, and Xiaoyin Liu. "Cloud data governance maturity model." In 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2017. http://dx.doi.org/10.1109/icsess.2017.8342968.

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Zitoun, Cyrine, Oumaima Belghith, Syrine Ferjaoui, and Sabri Skhiri Dit Gabouje. "DMMM: Data Management Maturity Model." In 2021 International Conference on Advanced Enterprise Information System (AEIS). IEEE, 2021. http://dx.doi.org/10.1109/aeis53850.2021.00013.

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Reports on the topic "Data maturity"

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Dodds, Leigh, Andrew Newman, Anna Scott, Kathryn Corrick, Phil Lang, and Adrian Philpott. Open Data Maturity Model. Open Data Institute, 2015. http://dx.doi.org/10.61557/goub2586.

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Read, P. B., J. F. Psutka, and J. Fillipone. Organic maturity data for the Canadian Cordillera. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/131843.

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Wielens, J. B. W., and M. P. Avery. Maturity trends from vitrinite data on the northern Grand Banks. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2004. http://dx.doi.org/10.4095/215384.

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Calderon, Marta, and Luis Jugo. Leveraging Data Analytics Beyond Assurance. Inter-American Development Bank, 2014. http://dx.doi.org/10.18235/0006992.

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This presentation gives an overview of the importance of creating a data analytics strategy in an internal audit department. It describes reasons for having a data analytics strategy, where to start (steps) when formulating a strategy, typical challenges to be addressed, what should be the focus of the strategy, and how to analyze the data analytics capability and usage maturity. Finally, it presents the IDB experience implementing a data analytics strategy for its internal audit activities.
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Quismorio, Brenda, Maria Antonette Pasquin, and Claire Tayco. Assessing the Alignment of Philippine Higher Education with the Emerging Demands for Data Science and Analytics Workforce. Philippine Institute for Development Studies, 2019. https://doi.org/10.62986/dp2019.34.

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Rapid advancement in technology has allowed for far-reaching use of data. This has consequently led to an increasing demand for Data Science and Analytics (DSA) professionals. However, recent studies show that such demand is often not met in many economies. Such DSA skills shortage is claimed to be rooted in the mismatch between the skills the industry demands and the skills academic institutions supply. As the first step in addressing this shortage of industry-ready DSA workers, it is necessary to know the DSA skills demanded by the industry and the DSA skills with which academic institutions
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Stasiuk, L. D., M. G. Fowler, and G. Addison. Thermal maturity evaluation of Lower Cretaceous Mannville Group and equivalent coals in the Western Canada Sedimentary Basin: a compilation of vitrinite reflectance data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/213611.

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Potter, J., F. Goodarzi, D. W. Morrow, B. C. Richards, and L R Snowdon. Organic petrology, thermal maturity and Rock-Eval/TOC data for upper Paleozoic to Upper Cretaceous strata from wells near Liard River, northeast British Columbia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/214596.

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Boyarchenko, Nina, and Leonardo Elias. Corporate Debt Structure over the Global Credit Cycle. Federal Reserve Bank of New York, 2024. https://doi.org/10.59576/sr.1139.

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We study the determinants of active debt management through issuance and refinancing decisions for firms around the world. We leverage instrument-level data to create a comprehensive picture of the maturity, currency, and security type composition of firms' debt for a large cross-section of countries. At the instrument level, we estimate a predictive model of prepayment as a function of interest costs savings and maturity lengthening motives. We document that there is substantial heterogeneity in prepayment across bonds and loans and across firms, depending on their reliance on bank lending. W
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Evenchick, C. A., M. C. Hayes, K. A. Buddell, and K. G. Osadetz. Vitrinite and bitumen reflectance data and preliminary organic maturity model for the northern two thirds of the Bowser and Sustut basins, north-central British Columbia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/213538.

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10

Licht, Mark, and Matt Schnabel. Corn Date of Planting and Maturity. Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/farmprogressreports-180814-1420.

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