Los datos: ¿una amenaza?

Tábula ◽  
2021 ◽  
pp. 43-63
Author(s):  
Jordi Serra Serra
Keyword(s):  
Data Management ◽  
Information Management ◽  
Data Analytics ◽  
Management Model ◽  
Records Management ◽  
Potential Threat ◽  
Management Perspective

El salto de los datos a la primera plana de la gestión de la información está moviendo los cimientos de la disciplina archivística. Conceptos ya consolidados por la gestión documental se ven clonados en un ámbito que posee un atractivo mediático y una pátina de modernidad muy superiores a la gestión documental. Es inevitable, pues, que la gestión de datos sea percibida por el colectivo archivístico como una potencial amenaza. Hasta dónde es real esta amenaza, y de qué forma se puede positivar, es lo que se analiza en este artículo. El análisis se realiza no desde la perspectiva archivística, sino desde la perspectiva de la propia gestión de datos y de sus actores. Partiendo en primer lugar de las necesidades de la analítica de datos y de los científicos de datos, el autor analiza de dónde se obtienen estos datos y, utilizando como marco de referencia el modelo DMBOK2 de gestión de datos, qué oportunidades ofrece este origen para la gestión documental. The leap of data management to the forefront of information management is shaking the foundations of the archival discipline. Concepts consolidated by records management far away are cloned by a discipline with a media attraction and a look of modernity widely higher than records management. It is therefore unavoidable that records management looks data management as a potential threat. This article analyzes to what extent this threat is real, and how it can be converted to an opportunity. The analysis is carried out not from the archival perspective, but from the data management perspective and its agents. Starting from the needs of data analytics and data scientists, the author analyzes the sources of the data and, using the DMBOK2 data management model as a framework, what opportunities are opening for records management.

scholarly journals Risk Detection in Digital Information Management: A Records Management Perspective

2020 ◽  
Vol 8 (2) ◽  
pp. 251-267
Author(s):  
Harry Bawono ◽  
Keyword(s):  
Information Management ◽  
Qualitative Method ◽  
Government Agencies ◽  
Records Management ◽  
Digital Information ◽  
Digital Environment ◽  
Paradigmatic Shift ◽  
Risk Detection ◽  
Starting Point ◽  
Management Perspective

The Presidential Regulation on e-Government in 2018 became the starting point for accelerating e-Government implementation in Indonesia. This moment prompted government agencies to compete in digitizing their organizations to apply the regulation. Digital information as the output of this massive digitalization will be abundant and must be managed properly which is certainly vulnerable to threats. Such threats can compromise the authenticity of records and make them untrustworthy. An adequate risk detection framework that fits the context of the digital environment is needed to minimize these incidents. This framework contains a records management perspective that has undergone a paradigmatic shift. This framework adopts the view that records management is an integral part of digital information management. The study used a qualitative method and found that from the perspective of records management, the risk detection framework in digital information management sheds light on aspects of context (external and internal), systems, and processes. However, its smooth implementation in the digital environment, especially in Indonesia, is determined by the extent to which paradigmatic reforms in records management have taken place.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

FDM: Fuzzy-Optimized Data Management Technique for Improving Big Data Analytics

2021 ◽  
Vol 29 (1) ◽  
pp. 177-185
Author(s):  
Gunasekaran Manogaran ◽  
P. Mohamed Shakeel ◽  
S. Baskar ◽  
Ching-Hsien Hsu ◽  
Seifedine Nimer Kadry ◽  
...  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Technique

Research on personalized image retrieval technology of video stream big data management model

2021 ◽  
Author(s):  
Wei Zhuo ◽  
Zhichao He ◽  
Mengying Zheng ◽  
Beichen Hu ◽  
Ruijuan Wang
Keyword(s):  
Big Data ◽  
Image Retrieval ◽  
Data Management ◽  
Video Stream ◽  
Management Model

Experiences in Establishing Trustworthy Digital Repositories Within a Large Multi-National Pipeline Company

2012 ◽  
Author(s):  
David A. Weir ◽  
Stephen Murray ◽  
Pankaj Bhawnani ◽  
Douglas Rosenberg
Keyword(s):  
Data Management ◽  
Lessons Learned ◽  
Records Management ◽  
Digital Repositories ◽  
Geographically Dispersed ◽  
Local Business ◽  
Master Data ◽  
Business Area ◽  
Master Data Management ◽  
Trusted Data

Traditionally business areas within an organization individually manage data essential for their operation. This data may be incorporated into specialized software applications, MS Excel or MS Access etc., e-mail filing, and hardcopy documents. These applications and data stores support the local business area decision-making and add to its knowledge. There have been problems with this approach. Data, knowledge and decisions are only captured locally within the business area and in many cases this information is not easily identifiable or available for enterprise-wide sharing. Furthermore, individuals within the business areas often keep “shadow files” of data and information. The state of accuracy, completeness, and timeliness of the data contained within these files is often questionable. Information created and managed at a local business level can be lost when a staff member leaves his or her role. This is especially significant given ongoing changes in today’s workforce. Data must be properly managed and maintained to retain its value within the organization. The development and execution of “single version of the truth” or master data management requires a partnership between the business areas, records management, legal, and the information technology groups of an organization. Master data management is expected to yield significant gains in staff effectiveness, efficiency, and productivity. In 2011, Enbridge Pipelines applied the principles of master data management and trusted data digital repositories to a widely used, geographically dispersed small database (less than 10,000 records) that had noted data shortcomings such as incomplete or incorrect data, multiple shadow files, and inconsistent usage throughout the organization of the application that stewards the data. This paper provides an overview of best practices in developing an authoritative single source of data and Enbridge experience in applying these practices to a real-world example. Challenges of the approach used by Enbridge and lessons learned will be examined and discussed.

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