IAKM PANEL ON “"Big data for knowledge management, knowledge management for big data" at IFKAD 2017 in St Petersburg
On 7th June, IAKM co-organized a panel discussion on the topic of KM and Big Data. Panelist were Meliha Handzic (International Burch University - Sarajevo), Ettore Bolisani (University of Padova), Enrico Scarso (University of Padova), Tatiana Gavrilova (Graduate School of Management, St. Petersburg), all members of IAKM, and our colleague Daniela Carlucci (University of Basilicata).
https://goo.gl/photos/YHzoCZ1DBH9Pnm8D9
The panel focused on the connection between the emerging topic of “big data” (and all the related concepts - business intelligence, business analytics and competitive intelligence) and Knowledge Management. This is a very hot issue, for both research and practice. Key questions are for example: how to extract useful knowledge from big data? how to transform big data into “really useful” knowledge? How can big data analysis boost knowledge creation processes? What is the actual role of technology and artificial intelligence in that? How to combine a hard approach to data with personal attitudes of people and with social processes of interaction? How can big data analysis be useful and feasible to face challenging contexts under a knowledge-based perspective, like those regarding humanities and arts?
The goal of the panel was to analyze, by means of an open discussion with attending colleagues, the state of the art of the topic and the emerging trends, seen from the perspective of KM researchers and professionals. Also, the panel offered an opportunity to interact to all the people interested in the issue, for facilitating reciprocal knowledge and future joint activities.
The panel started with a brief presentation by panelists (see SLIDE), just with the purpose to stimulate the debate, particularly on these issues:
Later, all participants were divided into two groups, with the purpose to animate a debate and to provide some possible “key points” for research and practice. The first Group discussed about “Theories, Models and Technologies for Big Data in KM”, the second Group about “Big Data and KM in social context and complex environments”. Some of the main results were highlighted in a final summary, as presented in these pictures (PICTURE 1 PICTURE 2)
The discussion provided very interesting food for thought to participants. In particular, it was highlighted how the field of KM can provide interesting perspectives on Big Data analysis. For instance, there is the need to “go back to the roots”, because a reflection on some typical points for KM (such as the relationship between data and knowledge, and therefore between big data and “big” knowledge) can provide a fresh view of potential and limitation of methods and technologies for big data analysis. Also, the participants underlined the broader implications of big data (in terms, for instance, of strategic value, value measurement, new decision-making models, managerial training, policy-making, etc.): again, research and practice in the KM field can provide useful suggestions here.
Now, the wish is that this initial discussion can be transformed into future opportunities for publication, research, and also lessons learned for the practice.
https://goo.gl/photos/YHzoCZ1DBH9Pnm8D9
The panel focused on the connection between the emerging topic of “big data” (and all the related concepts - business intelligence, business analytics and competitive intelligence) and Knowledge Management. This is a very hot issue, for both research and practice. Key questions are for example: how to extract useful knowledge from big data? how to transform big data into “really useful” knowledge? How can big data analysis boost knowledge creation processes? What is the actual role of technology and artificial intelligence in that? How to combine a hard approach to data with personal attitudes of people and with social processes of interaction? How can big data analysis be useful and feasible to face challenging contexts under a knowledge-based perspective, like those regarding humanities and arts?
The goal of the panel was to analyze, by means of an open discussion with attending colleagues, the state of the art of the topic and the emerging trends, seen from the perspective of KM researchers and professionals. Also, the panel offered an opportunity to interact to all the people interested in the issue, for facilitating reciprocal knowledge and future joint activities.
The panel started with a brief presentation by panelists (see SLIDE), just with the purpose to stimulate the debate, particularly on these issues:
- Connection between KM and Big data: useful concepts and theoretical models (E. Bolisani)
- Techniques, tools and applications to manage Big Data in KM (M. Handzic)
- Social media, big data, and impact on KM (E. Scarso)
- Big Data in complex and emerging contexts (such as Arts and Humanities) (D. Carlucci)
- KM, Knowledge Engineering, and Big Data (T. Gavrilova)
Later, all participants were divided into two groups, with the purpose to animate a debate and to provide some possible “key points” for research and practice. The first Group discussed about “Theories, Models and Technologies for Big Data in KM”, the second Group about “Big Data and KM in social context and complex environments”. Some of the main results were highlighted in a final summary, as presented in these pictures (PICTURE 1 PICTURE 2)
The discussion provided very interesting food for thought to participants. In particular, it was highlighted how the field of KM can provide interesting perspectives on Big Data analysis. For instance, there is the need to “go back to the roots”, because a reflection on some typical points for KM (such as the relationship between data and knowledge, and therefore between big data and “big” knowledge) can provide a fresh view of potential and limitation of methods and technologies for big data analysis. Also, the participants underlined the broader implications of big data (in terms, for instance, of strategic value, value measurement, new decision-making models, managerial training, policy-making, etc.): again, research and practice in the KM field can provide useful suggestions here.
Now, the wish is that this initial discussion can be transformed into future opportunities for publication, research, and also lessons learned for the practice.
