The following is a summary of / reflection upon
Caesarius, L., & Lindvall, J. (2011). The quest to make sense of information: A research commentary. Nordic Academy of Management Conference 2011 (pp. 1–7). Stockholm, Sweeden.
I found a reference to the paper online and the authors have been kind enough to share a copy of the paper. It was of specific interest to me as I’m exploring the limitations of learning analytics.
The abstract for the paper is
Debates on the so-called issue of ‘Big Data’, especially those in the business press, tend to over-emphasize potential benefits focusing almost exclusively on the promise of turning data and information into actionable knowledge. Accordingly, operating in information-rich environments provides firms opportunities to engage in analytic processes drawing on data and information to gain new intelligence (knowledge). New intelligence is the supposed end product of such processes that include among other things the identification of patterns, the creation of scenarios, the testing of models, prediction making and the prescription of actions. Such descriptions, as straightforward as they may sound, do not parallel reality, which is far more complex and difficult, and above all dependent on aspects that tend to escape the attention of many debaters.
Yet, although a ‘neologism’, the issue of ‘Big Data’ is part of larger debate on firms’ efforts to make sense of information. As such it connects to more diachronic issues in research such as for instance decision making, information system support and knowledge management. But the debate needs to be balanced; potentials need to be investigated in light of their challenges.
In this commentary paper we seek to add to the debate on ‘Big Data’ and on firms’ quest for making sense of data and information. We do so by attempting to explicate the challenges associates with such endeavors. Our main arguments are that although ‘Big Data’ may hold potential to support firms’ information sense-making processes, firms’ methodological and epistemological directives condition this potential. The former concerns the level of scientification, i.e. how much firms are relying on and accommodating for the use of scientific methodologies and knowledge to produce, make sense of and use information in a highly disciplined, systematized, structured and experimental manner. The latter relates to who is given interpretation priority in the analytic process effectively forming that which the firm is to act on, namely knowledge. Based on these findings we propose a set of areas for further research on subject matter.
The authors have said that the paper is a work in progress. So, there is room for improvement. However, it does point to some of the broader philosophical and historical considerations that don’t seem to have been broadly considered by the learning analytics cloud.
A couple of useful distinctions that I’m hoping to build upon.
The call for design science research is interesting.
Since the industrial revolution firms have operated in information poverty. That’s changing. Leading to big data.
The centre of the big data debate is the possibility “to turn data and information into actionable knowledge (Dedrick et al, 2003; Zammutto et al., 2007)”.
Which raises a whole range of questions around who and with what perspectives is that information being analysed the action taken.
While firms are rich in data and information, they are knowledge poor.
- Working definition of big data and its driving factors.
- Analysis of cited potentials and advantages.
- Analysis of critical aspects and challenges of turning data to information to knowledge.
- Research implications – three main avenues.
Constitution of big data
refers to the complexity of information in terms of the voluminous sets of data and information, to their extreme velocity, increased granularity and their widely varied formats (structured and unstructured).
- “process of digitization” arising from the “infusion of information technology into the organisation (Zuboff, 1988)”
- The Internet and the subsequent expansion of digitization beyond the boundaries of the firm.
- “expansion of proprietary data in organisations due to the increased granularity and frequency by which this data is produced, collected and used”
There seems to be a fine line between this and driver #1.
- Increased use of (social) media channels.
- Detect-and-respond technology built into devices connected to networks – e.g. RFID tags.
Drivers that seem to be limited to technology drivers. The increasing drive to quantifying, benchmarking, quality assurance, managerialisation etc seem to me to be important drivers.
Potential of big data
Analysis of data will generate value. Firms can become “intelligent enterprises”. Focus attention. Optimise operations. By sharing information employees become more empowered.
Big data doesn’t change business directly, but indirectly through analysis. This visualisation allows organisations to be more transparent and integrated.
Apparently it will allow higher level of experimentation in matters of exploration and exploitation.
Challenges with big data
“Despite advances in IT current technical solutions have yet to replicate much less to replace human analytical capacity”. links to the aims of AI research and the quote
as noted by Kallinikos (2001:37): “[t]he project of constructing intelligent machines has helped to reveal the immense complexity of human beings”.
Points to the work in Management Information Systems, Executive Information Systems and Decision Support Systems at building systems to aid decision making and action taking. One explanation for the failure of this work is their foundation on “an unrealistic understanding of human cognition”. In particular “the assumption of the rational actor.”
It’s a pity that this line of though isn’t followed up more.
Two major interconnected challenges:
- methodological – how organisations set up their analytics processes in terms of structure, and
- epistemological – how they carry them out in practice.
methodological = the way analytical processes are set up and how they are conducted as a layer that covers all parts of the firm’s operations. Can organisational practice/structure be transformed in response to analytics?
Increased level of “scientification” requires new skills oft missing from organisations. That are employed continuous basis.
Given the diversity of the underlying sets of data and information and of their sources these analytical processes need to take into account the inherent cognitive variations and different lifeworlds (Bruner, 1986; 1990; Lloyd, 2007).
“who is given interpretive priority”.
Corespondence theory/realism/positivism is “ontologically the current dominant perspective within existing IT-research. More on other perspectives before leading to
there is a need to develop inter- subjective perceptions or shared understanding / cross understanding (Huber and Lewis, 2010).
epistemologically talks about “know that” and “know how”. Prior emphasis on the former and the need to consider the latter. And the implication that has as knowledge becomes more ephemeral (Cook and Brown, 1999).
Conclusions and new avenues for research
Mentions historical debate on the meaning of information and a need to consider more than the dominant model “..define information in terms of what it is” to another perspective that “defines it in terms of what it does” (Hayles, 1999, p. 56). i.e. it is situated, strongly related to a context and a specific place.
ICT is oriented towards interaction, “by its nature it is relational. Many of these important relations are not linear – more information is not always better to understand a phenomena”.
Since interpretation is crucial, actor(s)/users become more important.
Trade-off between high level abstractions and narrative particularlism.
I’m interested in this bit
Most of all we see a need for studies based up on Design Science. It is too modest an ambition in a dynamic world to answer a research question with a traditional positive ambition (”is”); we need as researchers in this important area to also have an idea about ”possible worlds” (”to be”).
Literature to look at
Arnott, D. and G. Pervan, (2005), “A critical analysis of decision support systems research”, Journal of IT, Vol. 20, No. 2, pp. 67–87.
Brown, J. S. and P. Duguid, (2000), The Social Life of Information, Boston, MA: Harvard Business School Press.
Cook, S. D. N and Brown, J. S., (1999), ”Bridging Epistemologies: The Generative Dance between Organizational Knowledge and Organizational Knowing”, Organization Science, Vol. 10, No. 4, pp. 381- 400.
Dreyfus, H. L. and Dreyfus, S. E., (1986), Mind Over Machine; The Power of Human Intuition and Expertise in the Era of the Computer, New York, NY: Free Press.
Hodgkinson, G. P. and Healey, M. P., (2008), “Cognition in Organizations”, Annual Review of Psychology, Vol. 59, pp. 387-419.
Kallinikos, J., (2011), Governing Through Technology: Information Artefacts and Social Practice, London, UK: Palgrave Mcmillan.
Monod, E., and Boland, R. J., (2007), ”Editorial. Special issue on philosophy and epistemology: A Peter Pan Syndrome?”, Information Systems Journal, Vol. 17, No. 2, . 133-141.
Orlikowski, W. J., (2002), ”Knowing in Practice; Enacting a Collective Capability in Distributed Organizing”, Organization Science, Vol. 13, No. 3, pp. 249-273.
Weick, K. E., (2008), “Information Overload Revisited”, in Hodgkinson, G. P. and Starbuck, W. H., [Eds.], The Oxford Handbok of Organizational Decision Making, New York, NY: Oxford University Press.
Zammuto, R. F., Griffith, T. L., Majchrzak, A., Dougherty, D. J., Faraj, S., (2007), ”IT and the changing fabric of organization”, Organization Science, Vol. 18, No. 5, pp. 749-762.