Institutions willing to consider MOOCs at the current stage have to keep envi- sioning the possibilities of MOOCs not as a single business model, but as a set of business models centered around the notions of global online access, free or rela- tively low fee, edification. The use of the termedificationas opposed to education is used here to connote both engagements in education: (i) those for obtaining formal qualifications for employability purposes, and (ii) those for continuous development through the pursuit of personal interests. With this in mind, interested institutional parties should keep the following in mind during their search for a positioning in the MOOC market.
• Different strands of MOOCs are very likely to develop depending on their utilisation of big data.
• Big data analytics can be utilised as a marketing tool to target potential learners. • Big data can be utilised as a learning tool to assist help—learning, by profiling students learning patterns and to alert the education institution when human intervention is required to motivate or support student learning.
• Within cMOOCs, big data analytics can be utilised as a tool to source and integrate learning relevant learning material.
• xMOOCs are likely to transform into the MOOC equivalent of vocational education, big data analytics can play a crucial role in matching industry- required employment skills and curriculum development.
3.8
Summary
While MOOCs facilitated the distance learning aspects of formal education insti- tutions, they also introduced an even more fundamental educational change; they opened up participation to education by enabling a peer-to-peer learning. MOOCs open new income streams for traditional institutions by not only relying on state education subsidies and student tuition, but though employment recruiting services, syndication, and sponsoring, as well as by advertising income, selling student information to potential employers or advertisers. Big data analytics can enable the personalization of the online learning process that was missing in previous online instruction methods and facilitate this to happen at a global scale. Used as a ped- agogical tool in learning analytics, big data along with educational data mining and
teaching analytics can all be seen as three aspects of the same solution to raising the education standards of the youth without necessarily increasing the number of educators required, thus making institutions more cost efficient. Utilizing existing knowledge and education models relating to student effort and success, institutions can use big data technologies to monitor students’ interaction with the system, alerting administrators when student engagement patterns change in order to initiate communication with the student, or indeed send a predetermined default email communication. Big data in the form of learning analytics focus on capturing student behavior and correlating it to achieving learning objectives, educational data mining seek to design predictive analytics models for student attainment while teaching analytics helps educator translate suchfindings into better course design and student support procedures and interventions.
The reality however is quite different, MOOCs platforms do not share some of the basic assumptions about student motivation and institutional norms, expecta- tions, and obligations, so preexisting educational models are not a goodfit for their analysis. Moreover, as the Livemocha case suggested, MOOCs could utilize gamification or other learning from online communities and worlds to ensure continuous engagement and commitment.
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4
Abstract
This chapter outlines the concept of‘big data driven business model’and utilizes it to describe a set of businesses that rely on big data to achieve their key value proposition and to substantially augment their value proposition to differentiate themselves in order to gain competitive advantage. It describes the impact of big data on each of the elements as identified in the Business model canvas. Also the chapter discusses the potential of big data for mass customization and personalization of product and services, as a value proposition in its own right, on B2B and B2C logistics as well as for customer relationship management and customer service. It also touches upon how big data has facilitated a shift in our conceptions of utility as opposed to resources as the basis for socio-economic value creation. Also, the chapter explores this issue a bit further by understanding the implications of big data on partnerships, monetization, and the opportunities and challenges it raises for accounting, budgeting and performance metrics. In conclusion, the chapter acknowledges the synergistic potential with other emerging technologies such as 3D printing, Robots, Drones, self-driving cars, and the like.
4.1
Introduction
The focus of this chapter merits a more specific definition of the term Business Model. Business models are a natural fit with IT-related business innovations. Although rooted in transaction cost economics, it was really an Information Communication Technology (ICT) related phenomenon. ICT made it feasible and cost effective for businesses to collaborate with value networks in order to compete (Don et al.2000; Amit and Zott2001). Bundling of product and services became very popular during the 90s blurring the boundaries between industries.
So, what is a business model? Among the diverse available definitions (Amit and Zott 2001), a business model can be seen as outlining the architectural logic of business elements, such as a business structure, business processes, infrastructure, and
©Springer International Publishing Switzerland 2015 V. Morabito,Big Data and Analytics,
DOI 10.1007/978-3-319-10665-6_4
systems as well asfinance options, i.e. how they allfit together to coordinate value creation. It describes who buys the company’s products and services and why, how a company organizes and which resources they utilize in order tofinance, produce and deliver their offerings, what they pay for doing so and how they get their money in.
Much like any model (Baden-Fuller and Morgan 2010), business models are abstractions of real life and in this particular case, business models have been used to describe various business phenomena. According to (Osterwalder et al.2005) people have used the term loosely to describe all real world businesses (e.g. the capitalist model), or particular types of business with common characteristics (e.g. the auction model), or a very particular real world business model (e.g. the Apple model). Furthermore, according to (Osterwalder and Pigneur 2010), business models comprise nine fundamental elements illustrated in Table4.1.
While in the past, organizations relied on managers intuition tofill in the gaps of scarce incomplete, poor information to make business decisions, the proliferation of data generated every day through social media, cloud computing, and mobile phones and soon the Internet of Things (IoTs) give managers a new headache, more information that what they know what to do about. This combination of digital intensity, connectivity, and big data provides a context of networked abundance (Bharadwaj et al.2013).
The purpose of this brief introduction of the concept was twofold. First it was necessary in order to clarify how we are going to use the term in this chapter. We are going to use the term ‘big data driven business models’, describing a set of businesses which rely on big data to achieve their key value proposition and to substantially augment their value proposition to differentiate themselves in order to gain competitive advantage. Second, it would be useful to establish on the outset the scope of this chapter. Most big data driven business models are currently augmentations of existing value propositions (Hagen et al.2013). Big data driven business models are currently in development. Most businesses have introduced big data in their projects portfolio on the basis of efficiency, which by default means doing the same thing, with less money, and even fewer on effectiveness, i.e. doing things better (Hagen et al. 2013). It will be some time before true visionaries introduce business models whose unique selling point will rely on big data, and
Table 4.1 Nine fundamental elements of business models (Osterwalder and Pigneur2010) 1 Thecustomer segmentsthe company serves
2 Thevalue propositionsthat offer a solution to its customers
3 Thechannelsthrough which the company communicates, distributes, and fulfils its sales 4 Thecustomer relationshipsthe company established and maintained with customers 5 Therevenue streamsaccruing to the company from its customers
6 Key resourcesor assets required
7 Key activities, i.e. the tasks, processes and behaviors required
8 Key partnerships, which undertake responsibility for some of the above 9 Cost structure, i.e. the cost of maintaining and developing all of the above.
very likely such value propositions to be augmented by other innovations also in development in the social, business and technology spheres. So, within this chapter we offer some projections along with descriptions of such scenarios.
Big data will have big implications for variousfields (Hagen et al. 2013). Yet, such changes seek to complement today’s sectors and business models. It is worth noting that a business sector consists of a number of companies or business units operating effectively under the same or similar business models. Table4.2shows some of the indicated changes.
So far, there is a limited impetus for fundamental structural changes that place big data to the core of company value creation as opposed to ‘nice-to-have’ capabilities. In that sense, the suggested changes seek to enhance existing business models rather than disrupt them. For example, big data has been utilized to improve existing business models as shown in Table4.3.
Table 4.2 Existing business models currently enhanced by big data
1 Improving customer intimacy through customer profiling and personalized customer service
2 Opening up product/service innovation by explicitly crowdsourcing ideas or analyze social media generated opinions and feedback
3 Improving decision-making by generating business options based on larger amounts of data, triangulated through various sources,filtered down to workable solutions in real time 4 Enabling real-time operations, utilizing smart assets, which can coordinate to carry out a process or shift business processes instantly, while providing real-time tracking feedback
Table 4.3 Industries using big data to transform business models and improve performance in many areas
Retail Manufacturing
Customer relationship management
Fraud detection and prevention
Product research
Process and quality analysis
Store location and layout Supply chain optimization
Engineering analytics
Distribution optimization Dynamic pricing Predictive
maintenance
Financial services Media and telecommunication
Algorithmic trading Fraud detection Network optimization Churn prevention Risk analysis Portfolio analysis Customer scoring Fraud prevention Advertising and public relations Energy
Demand signaling Sentiment analysis Smart grid Operational modeling Targeted advertising Customer acquisition Exploration Power-line sensors Government Healthcare and life sciences Market governance Econometrics Pharmacogenomic Pharmaceutical
research Weapon systems and
counterterrorism
Health informatics
Bioinformatics Clinical outcomes research Adapted from Hagen et al. (2013)
These incremental changes will bring significant value to existing organizations and change our customer expectations, which leave analytics lagging companies behind in the competition game. Then again such models are not fundamentally big-data driven, but big data enabled.
Fundamental changes can be envisioned by combining the analytical power of big data analytics with new production technologies and new concepts about production business value. For example, what business models can evolve by combining the power of big data analytics, with other emerging technologies, such as the ones shown in Table4.4.
Will social services and primary care become obsolete by smart homes and health tracking devices providing real-time information about the health status of an individual, while artificially intelligent social robotics decide and administer appropriate regular care to the elderly? Will bitcoin become part of the mix of or a substitute for existing finance options? Can we organize centralized farming by utilizing smart assets, drones and swarm robotics to tend to agricultural production. Will the distinction between public and private transport systems disappear by merging to create a new, on demand, door-to-door self-driven transport system? To some of these ideas may seem far fetch, but for others some of these are expected to become mainstream in the next 15−20 years. For example, pod based inner city transport is already a reality in Masdar, Abu Dhabi (Feuilherade et al.2014).
Real-time, big data analytics can transform production processes. For example, big data analytics is utilized by General Electric as an ‘in process’ monitoring mechanism to quality control highly sensitive and very expensive industrial 3D printing processes of aerospace components where structural integrity is critical to safety (Gereports 2013). For example, IBM’s Deep Thunder program is oriented towards precision agriculture that combines microclimate predictive models with remote drone monitoring to optimize agricultural processes, such as weeding, spraying, watering and harvesting crops.
We continue this chapter by understanding the fundamental implications of big data on each of the nine identified elements comprising a business model.
Table 4.4 Emerging technologies suitable to be combined with big data analytics
1 Home 3D printing 2 Health andfitness monitors 3 Eye and hand tracking device
4 Drones
5 Self-driving cars
6 Bitcoin
7 (Ro)bots 8 Smart homes