scholarly journals Maturity Model for Cognitive Computing Systems in the Public Sector

Author(s):  
Kevin Desouza ◽  
Franziska Götz ◽  
Gregory S. Dawson
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aki Jääskeläinen ◽  
Virpi Sillanpää ◽  
Nina Helander ◽  
Riikka-Leena Leskelä ◽  
Ira Haavisto ◽  
...  

Purpose This study aims to report the design and testing of a maturity model for information and knowledge management in the public sector, intended for use in frequent monitoring, trend analysis and in-depth analysis of the contemporary information and knowledge management practices of an organization. Design/methodology/approach A design science approach was used to develop the proposed model. Creation of the model was based on an extensive literature review. Testing of the model was implemented as a survey receiving 37 responses from nine organizations organizing and purchasing public services. Findings The study presents four alternative profiles for an organization’s status, novice, experimenter, facilitator and advanced exploiter, and investigates the differences between these profiles on the basis of the empirical data gathered. The model was found to be both a valid and practical way to determine the state of an organization’s information and knowledge management and identify development needs. Research limitations/implications Testing was conducted in the Finnish public sector and further studies applying the model could be implemented in other countries. The model presented was designed specifically for the public sector and more research is needed to test its applicability in the private sector. Originality/value Maturity models are useful when evaluating information and knowledge management status in an organization, and beneficial for improving organizational performance. The proposed maturity model combines the fields of knowledge management and information management and contributes to the literature with an overarching maturity model that includes a dimension of satisfaction with the organizational maturity level. While many earlier models originate from the consultancy business, the model presented here was also designed for research purposes and tested in practice.


2018 ◽  
Vol 13 (5) ◽  
pp. 957-966
Author(s):  
Shigeo Mori ◽  
◽  
Atsuhiro Goto

The damages caused by cyber-attacks are becoming larger, broader and more serious and to include monetary losses and losses of lifeline. Some cyber-attacks are arguably suspected to be parts of national campaigns. Under such circumstances, the public sector must endeavour to enhance the national cybersecurity capacities. There are several benchmarks for national cybersecurity, i.e., a snapshot relative assessment of a nation’s cybersecurity strength at a global level. However, by considering the development of technology, attackers’ skills and capacities of other nations, we believe that it is more important to review the national strategy for cybersecurity capacity enhancement and to ensure that the national capacity advances adequately in the coming years. We propose a method of reviewing national strategies. Additionally, we performed a trial review of the Japanese cybersecurity strategy using the Cybersecurity Capacity Maturity Model for Nations (CSCMMN) developed by the Global Cyber Security Capacity Centre. This trial proved to be workable because it detected various possibly inadequate (insufficient, inappropriate or inefficient, although further investigation is needed) approaches in the Japanese strategy. Moreover, the review also discovered the shortcomings of the capacity areas in the CSCMMN. We plan to improve the reviewing method and develop the improvement process of national strategies for cybersecurity capacity enhancement.


2020 ◽  
Vol 14 (4) ◽  
pp. 681-699
Author(s):  
Aras Okuyucu ◽  
Nilay Yavuz

Purpose Despite several big data maturity models developed for businesses, assessment of big data maturity in the public sector is an under-explored yet important area. Accordingly, the purpose of this study is to identify the big data maturity models developed specifically for the public sector and evaluate two major big data maturity models in that respect: one at the state level and the other at the organizational level. Design/methodology/approach A literature search is conducted using Web of Science and Google Scholar to determine big data maturity models explicitly addressing big data adoption by governments, and then two major models are identified and compared: Klievink et al.’s Big Data maturity model and Kuraeva’s Big Data maturity model. Findings While Klievink et al.’s model is designed to evaluate Big Data maturity at the organizational level, Kuraeva’s model is appropriate for assessments at the state level. The first model sheds light on the micro-level factors considering the specific data collection routines and requirements of the public organizations, whereas the second one provides a general framework in terms of the conditions necessary for government’s big data maturity such as legislative framework and national policy dimensions (strategic plans and actions). Originality/value This study contributes to the literature by identifying and evaluating the models specifically designed to assess big data maturity in the public sector. Based on the review, it provides insights about the development of integrated models to evaluate big data maturity in the public sector.


Author(s):  
Csaba Veres

Cognitive Computing has become somewhat of a rallying call in the technology world, with the promise of new smart services offered by industry giants like IBM and Microsoft. The recent technological advances in Artificial Intelligence (AI) have thrown into the public sphere some old questions about the relationship between machine computation and human intelligence. Much of the industry and media hype suggests that many traditional challenges have been overcome. On the contrary, our simple examples from language processing demonstrate that present day Cognitive Computing still struggles with fundamental, long-standing problems in AI. An alternative interpretation of cognitive computing is presented, following Licklider's lead in adopting man-computer symbiosis as a metaphor for designing software systems that enhance human cognitive performance. A survey of existing proposals on this view suggests a distinction between weak and strong versions of symbiosis. We propose a Strong Cognitive Symbiosis which dictates an interdependence rather than simply cooperation between human and machine functioning, and introduce new software systems which were designed for cognitive symbiosis. We conclude that strong symbiosis presents a viable new perspective for the design of cognitive computing systems.


Author(s):  
Csaba Veres

Cognitive Computing has become somewhat of a rallying call in the technology world, with the promise of new smart services offered by industry giants like IBM and Microsoft. The recent technological advances in Artificial Intelligence (AI) have thrown into the public sphere some old questions about the relationship between machine computation and human intelligence. Much of the industry and media hype suggests that many traditional challenges have been overcome. On the contrary, our simple examples from language processing demonstrate that present day Cognitive Computing still struggles with fundamental, long-standing problems in AI. An alternative interpretation of cognitive computing is presented, following Licklider's lead in adopting man-computer symbiosis as a metaphor for designing software systems that enhance human cognitive performance. A survey of existing proposals on this view suggests a distinction between weak and strong versions of symbiosis. We propose a Strong Cognitive Symbiosis which dictates an interdependence rather than simply cooperation between human and machine functioning, and introduce new software systems which were designed for cognitive symbiosis. We conclude that strong symbiosis presents a viable new perspective for the design of cognitive computing systems.


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