Thoughts on an interlinked knowledge management transdisciplinary and ethically-driven global collaboration initiative
guest article by Karim Sidaoui
We live in an age where we currently produce more than 2.5 quintillion bytes of data per day, and where 90% of that data was created in just the last two years (Marr, 2018). To put this into perspective, let us consider the world’s longest novel according to the Guinness World Records: ‘A la recherche du temps perdu’ (‘In Search of Lost Time’) by Marcel Proust which is made up of 9,609,000 characters (Guinness World Records, 2019). If we assume that each byte makes up a character, then we would be producing the amount of data equivalent to approximately 260 billion times this novel per day! Having said that, this amount of data is incomprehensible to humans without the development of technology which would help make sense of it. However, this is not the only matter which we struggle to comprehend. We also grapple with explaining and understanding more intrinsic and subjective data which attributes itself to concepts such as consciousness and subjective experience. In the service world, the latter seep into constructs such as customer experience, engagement and other customer-centric concepts. Therefore, what are alternative methods and means to explore such concepts, and is there a place for technology to offer a hand on this matter?
The aim of this article is to encourage and enlist service researchers in the exploration and development of such a means, proposed and defined hereafter as the Data Exchange Web (DEW).
Why start with services? Because services aim to serve humanity through the offerings of industry. Improving services and service reach to enable more people to live better lives (Fisk et al., 2016) is one of the many impacts attributed to the development of such means. The following sections will describe the conception and components of DEW, along with suggested implementation stages and possible future work.
Dataism, beyond the -ism.
In the book Homo Deus: A Brief History of Tomorrow (Harari, 2016), Dataism “declares that the universe consists of data flows, and the value of any phenomenon or entity is determined by its contribution to data processing”. At first, one struggles to fathom the meaning of this statement, but then after much contemplation, it becomes apparent that the idea behind this “ism” is aligned with a philosophical stance that data is the currency of exchange in occurring phenomena. Adopting an input-output system allows us to illustrate this data exchange. If we take customer experience for example, a customer’s interaction with a service provider could be observed as a data input (e.g. service settings and quality) resulting in an experience (e.g. emotions). We are not strangers to studying entity interactions in such a manner. Mathematical formulas, chemical reactions, and social science hypotheses to name a few, have been studied for years in a standardized fashion. However, the benefit to using data as a currency-of-exchange enables forming an abstraction layer over discipline-specific notions and allows data to be exchanged across disciplines. To illustrate this further, customer experience studies could contain data pertaining to chemical reactions occurring in the brain, behavioral eye movement data, and transactional data collected from CRM and customer support systems to name a few. What this means, is that researchers can pool different sources to provide more holistic, yet standardized, manners to pursue even highly subjective constructs, such as customer experience, to better our interpretation of this phenomenon.
Data Exchange Web (DEW)
A deeper understanding of constructs requires understanding the underlying constructs within that constitute that complex construct. In computer science, tackling a complex problem usually involves breaking it into smaller components via a divide and conquer strategy (Figure 2).
With this strategy, combining the inputs of lesser constructs to those of bigger ones creates a data exchange web where data flows from one endpoint to another, changing form depending on the exchange occurring. A metaphorical illustration would be observing a spider web at dawn during the formation of dew drops on different segments of the web, which then move towards web junctions to form bigger droplets (Figure 3). The web junctions, in this case, denote input-output nodes which accept outputs from preceding nodes and inputs to superseding ones, thus forming the DEW structure.
Since the exchange in this model does not constitute the dew drops but data, the exchanges occurring at each node would require an algorithm to allow the exchange to happen. Thus, the data exchange nodes (DENs) themselves (depicted D1, D2, and D3 in Figure 3) would house the algorithms involved in the data exchange. DEW “droplets” would denote different data flows between a set of nodes, transformed by their respective algorithms.
Two important distinctions must be highlighted however. The first relates to algorithms. What is meant by algorithm in this context does not necessarily relate to computer software, but rather to the sequential problem solving procedure developed by the famous 9th century mathematician “Al-Khawarizmi” (Baraka et al., 1998). Hence, for data to be transformed from one state to another, an algorithm specifying the step-by-step procedure for this exchange would be required. The other distinction relates to DEW and the data. The DEW exists as a structure separately from data and acts solely to transform that data. This distinction is important because sharing DEW does not mean sharing the data. This means that DEW enables a collaborative model without compromising data privacy which is a key focus for this work. To elaborate further, and using the previous distinction related to algorithms, a step-by-step procedure devised to transform eye-movement behaviour data to insights regarding customer experience does not need data to be shared. Thus, a DEN or a set of DENs, which include their respective algorithms, could be shared in the same manner as physics or mathematical formulas which only when populated by data would yield some form of results.
Such a model would allow global collaborations to occur across disciplines to aid in building knowledge node by node or DEN by DEN. An effort this large would need a standardised and consolidated way to register and evaluate the DENs. Thus, a consortium could be set up to organise this endeavour.
Data Exchange Nodes (DENs)
DENs are responsible for exchanging the data inputted into them into (an) output(s). DENs contain the algorithms which enables this. Because the DEW is a humanitarian effort aimed towards better serving humanity, two main attributes which act as a safety mechanism to ensure the DEN contains both an algorithm which is transparent and reproducible as well as compliant with social values is necessary to embed. The validity of these attributes is evaluated in a similar fashion to the peer-review process for academic journals. Creating an algorithm from data inputs, abiding by algorithmic transparency as well as satisfying a certain social value would enable the creation of new data with a specific business value (Figure 4).
To clarify further, DENs can be singular or can co-exist as different versions. For instance, a team could develop a customer experience DEN which has a different algorithm to another created by another team. DEN versions could thus be chosen as needed and become more popular the more they are adopted. This creates an ever-growing web of DENs where knowledge is created as DEW “droplets” or data and passes from one DEN to another, fuelled by a collaborative and transdisciplinary humanitarian effort.
Implications and Future Work
From a theoretical perspective, DEW provides a collaborative transdisciplinary model to study complex concepts using a divide and conquer strategy. This enables researchers to better understand holistic and subjective constructs in addition to simpler ones by allowing different disciplines to “speak the same language” and thus provide deeper insights into such constructs. From a bird’s eye perspective, DEW aims to build a theoretically-driven database of data exchange structures, which translates into an interlinked collaborative global knowledge management system with data as its currency. Following this logic, feeding a mature DEW with even miniscule and insignificant data can lead to the formation of knowledge depending on the DENs available, just like “droplets” moving from one web junction to the next.
From a managerial perspective, segments of DEW (a subset of DENs) could be adopted by companies and thus become a form of ethical validation to what companies do with their data as well as provide a technological backbone to companies which do not have access to such algorithms. This would encourage companies to utilise further technologies such as big data, artificial intelligence, and the internet of things to use in such a DEW segment. Furthermore, DEW could provide companies with a more standardised way to transform data, which in turn creates more cohesion in data infrastructures, and thus creates data bridges and exchange opportunities between companies to collaborate with their data. Simultaneously, DEW provides a platform where academics and executives can work together to achieve both theoretical and practical value. This would address the gap which exists between industry and academia (Benoit et al., 2017) as well as pool people from all disciplines to provide a more holistic wealth of knowledge globally.
If this proposed concept withstands initial scrutiny and invites a general interest, there are a plethora of areas to develop within sequential phases (Figure 5). The first phase would require a more in-depth investigation of this concept to establish a clearer overview of each component of DEW and the many implications it carries with regards to society, industry, and research. This expansion could explore the sociological implications of transparent data algorithms, the technological challenges, and operational feasibility. The second phase would require a consortium to foster and act as an overseer to not only the operational aspects of this initiative but also begin seeding and populating available DENs. DENs would need to be populated both retrospectively and actively. This phase would also include efforts into public relations and network assemblage. Additionally a soft / beta launch of the platform would take place with early adopters, network representatives, and sponsors. Lastly, a launch phase would ensure the platform is available publicly and globally.
Conclusion
There will always be agreements and disagreements among researchers on different concepts. However, one of the roles of researchers is to not only pursue the truth, but to also to scrutinise prior attempts to construct that truth. In other words, problematization, “a methodology for identifying and challenging assumptions that underlie existing theories and, based on that, generating research questions that lead to the development of more interesting and influential theories” (Alvesson and Sandberg, 2011, p. 248). This is where this proposed model of knowledge management comes into play. Is this model feasible both theoretically and practically? Can the model enable collaboration efforts between disciplines while also serve humanity to reduce suffering and improve quality of life? Will this model bring forth a new dynamic between academics and practitioners that reduces the existing gap between both? What are the challenges in adopting this model and maintaining it?
The above are some of the questions posed for this model to be considered as a serious humanitarian project. May I please ask the readers and extended community of SERVSIG for their feedback, comments, or thoughts on this matter in any shape or form.
Karim Sidaoui is a PhD researcher at the Alliance Manchester Business School in the Management Science & Marketing division within the University of Manchester (UK) (Expected graduation September, 2020). His educational background comprises a Bachelor in Computer Science, and a Master’s in Business Administration.
His academic interests include marketing, customer experience, artificial intelligence, machine learning, data mining, and big data analytics. His teaching experience includes Digital Marketing and Business Intelligence and Analytics at the Alliance Manchester Business School.
He possesses more than 8 years of corporate and start-up professional experience ranging from software development and IT project management to retail category management and business development. His involvement with international brands such as Samsung, LG, Philips, Toshiba and others in multinational settings enabled rapid and successful business expansion in the MENA region. Karim has managed customer retail strategies pertaining to product design, marketing, sales, cross-departmental liaison, and customer feedback & support in both online and brick-and-mortar settings.
Contact details:
Email: karim.sidaoui@manchester.ac.uk
Linkedin: https://www.linkedin.com/in/karimsidawi/
Twitter: https://twitter.com/mirak_iuoadis
References
Alvesson, M., Sandberg, J., 2011. Generating Research Questions Through Problematization. Academy of Management Review 36, 247–271. https://doi.org/10.5465/amr.2009.0188
Baraka, A., Salem, R.M., Joseph, N.J., 1998. The Origin of the “Algorithm.” Anesthesiology: The Journal of the American Society of Anesthesiologists 89, 277–277.
Benoit, S., Scherschel, K., Ates, Z., Nasr, L., Kandampully, J., 2017. Showcasing the diversity of service research. Journal of Service Management 28, 810–836. https://doi.org/10.1108/JOSM-05-2017-0102
Fisk, R., Anderson, L., Bowen, D.E., Gruber, T., Ostrom, A., Patrício, L., Reynoso, J., Sebastiani, R., 2016. Billions of impoverished people deserve to be better served: A call to action for the service research community. Journal of Service Management 27, 43–55. https://doi.org/10.1108/JOSM-04-2015-0125
Harari, Y.N., 2016. Homo deus: a brief history of tomorrow.
Longest novel [WWW Document], 2019. . Guinness World Records. URL http://www.guinnessworldrecords.com/world-records/longest-novel (accessed 5.4.19).
Marr, B., 2018. How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read [WWW Document]. Forbes. URL https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/ (accessed 5.4.19).
