Running Head: TRENDS IN BUSINESS ECOSYSTEMS 1

Case Study 1: Trends in Business Ecosystems

BUS 4200 Enterprise Information Management Systems

Daria Dulan

Notre Dame de Namur University

Dr. Rodney Heisterberg

January 18, 2017

TRENDS IN BUSINESS ECOSYSTEMS

Problem Statement

It is imperative to develop Personal Learning Environments (PLEs) in order to

build a “Smart Ecosystem” during the Petabyte Age because the future of business will

no longer be about a single product/service, but about cross-organizational innovative

solutions.

Challenges and Opportunities

According to IMAILE, PLEs are systems, or digital toolboxes that help learners

take control of and manage their own learning. It aids a learner in setting their own

goals, managing the content they learn and managing the processes by which they

learn (“PLE and PLEI,” n.d.). With any new technology, product, or process there will be

a learning curve – time allotted to understand the new concepts and uses.

Vermesan and Friess (2013) describe the Internet of Things (IoT) as a concept

and a paradigm that “considers pervasive presence in the environment of a variety of

things/objects that through wireless and wired connections and unique addressing

schemes are able to interact with each other and cooperate with other things/objects to

create new applications/services and reach common goals” (7).

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TRENDS IN BUSINESS ECOSYSTEMS

Figure 1: IoT viewed as a network of networks

(Retrieved from Vermesan & Freiss, 2013, p. 13)

Another working definition of IoT is: “a global infrastructure for the information society,

enabling advanced services by interconnecting (physical and virtual) things based on

existing and evolving interoperable information and communication technologies”

(Vermesan & Freiss, 2013, p. 15). Imagine a world where the real, digital and the virtual

are converging to create smart environments that make energy, transport, cities and

many other areas more intelligent (Vermesan & Freiss, 2013).

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TRENDS IN BUSINESS ECOSYSTEMS

Figure 2: Internet of everything

(Retrieved from Vermesan & Freiss, 2013, p. 15)

Dr. Jens Knodel of Fraunhofer IESE predicts that Interconnected Systems “– the

cross-domain megatrend for software and systems – will be the challenge in future

software engineering as unique selling propositions (USPs) will increasingly be

generated by interconnecting one’s own software with other systems” (Knodel, n.d.). In

order to get here, a change of paradigms will occur: “from monolithic single systems to

open, interconnected, scalable, and service-oriented Software Ecosystems” (Knodel,

n.d.).

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TRENDS IN BUSINESS ECOSYSTEMS

So far, three different system classes can be characterized today: Information

Systems, Embedded Systems, and Mobile Apps. These system classes are continually

evolving. “In the area of Information Systems, Emergent Enterprise Software Systems

are the next phase of the evolution towards the Internet of Services. Interconnected

Embedded Systems, on the other hand, are becoming Cyber-Physical Systems (CPS)

and will finally lead to the Internet of Things. In both system classes, Mobile Apps are

also being increasingly integrated into business processes today” (Knodel, n.d.).

Smart Ecosystems represent the mid-term evolutionary phase; “they form a

bridge between the Information Systems domain and the Embedded Systems domain”

(Knodel, n.d.). In other words, Smart Ecosystems connect Emergent Systems and CPS

into a single ecosystem, in which the Internet of Services, Things, and Data merge with

each other, thus resulting in cross-organizational innovative solutions. Business

processes and technical processes are equally valuable and impact each other mutually

in order to achieve optimization from global perspectives. As an extension of the

classical Software Ecosystem, the Smart Ecosystem also integrates non-trivial

Information Systems and non-trivial Embedded Systems. They function as one unit,

which dynamically uses context-dependent information to achieve common higher-level

goals (which no single system would be able to achieve on its own) (Knodel, n.d.).

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TRENDS IN BUSINESS ECOSYSTEMS

Figure 3: Biological and Digital Ecosystems

(Retrieved from http://spectronet.de/story_docs/vortraege_2014/140703_silicon_saxony_day/140703_doerr_fraunhofer_iese.pdf)

So what does this mean for business? It means:

a. New business models

b. Private life is pushing business life

c. Physical objects go digital

d. Big data being used to exploit available data

e. Uncertainty

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TRENDS IN BUSINESS ECOSYSTEMS

Opportunities

The IoT genie is out of the bottle and growing! “According to Gartner, 6.4 billion

connected things will be in use worldwide in 2016, up 30 percent from 2015. This

number will soar to more than 20 billion by 2020” (Dickson, 2016). Others present even

higher estimates. “The opportunities in improved utility, energy-saving, efficiency and

safety lying in the data gathered by such immense numbers of connected sensors and

smart devices are huge and without precedent” (Dickson, 2016). However, the

challenges that come with the quick growth of IoT are also new and unfamiliar.

Challenges

Smart ecosystems offer opportunities, while raising new engineering challenges

at the same time. Fundamental differences in engineering just one of the two (either

information systems or embedded systems) bear the risk of entering the market too late,

with insufficient quality, or even both when engineering the integrated combination of

the two (Knodel, n.d.).

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TRENDS IN BUSINESS ECOSYSTEMS

Figure 4: Key Challenges

(Retrieved from http://spectronet.de/story_docs/vortraege_2014/140703_silicon_saxony_day/140703_doerr_fraunhofer_iese.pdf)

Smart ecosystems have an inherent complexity due to the number of systems

being integrated, their size (the sum is more than just adding up the pieces), and their

interconnections with other ecosystems (Knodel, n.d.).

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TRENDS IN BUSINESS ECOSYSTEMS

Smart ecosystems have different methods, processes, technologies, and tools in

each domain. Successful evolution (after successful integration) requires alignment and

coordination of all engineering activities (Knodel, n.d.).

Smart ecosystems often envision on-demand collaboration of organizational units

or whole organizations. Human and cultural factors have to be considered throughout

the entire ecosystem lifecycle (Knodel, n.d.).

Smart ecosystems do not always know their context – neither at development

time, nor at runtime, nor at operation time. The interactions between multiple services

and entities in different versions and variants deployed in arbitrary ways can easily lead

to unwanted side effects (Knodel, n.d.).

Another challenge presented by smart ecosystems is data management. Big

data is about “the processing and analysis of large data repositories, so

disproportionately large that it is impossible to treat them with the conventional tools of

analytical databases. Some statements suggest that we are entering the ‘Industrial

Revolution of Data’ where the majority of data will be stamped out by machines”

(Vermesan & Freiss, 2013, p. 81). These machines generate data a lot faster than

people can, and their production rates will grow exponentially with Moore’s Law. Storing

this data is cheap, and it can be mined for valuable information. “The trend is part of an

environment quite popular lately: the proliferation of web pages, image and video

applications, social networks, mobile devices, apps, sensors, and so on, able to

generate, according to IBM, more than 2.5 quintillion bytes per day, to the extent that

90% of the world’s data have been created over the past two years” (Vermesan &

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TRENDS IN BUSINESS ECOSYSTEMS

Freiss, 2013, p. 84). Big data requires exceptional technologies to efficiently process

large quantities of data within a tolerable amount of time.

“Technologies being applied to big data include massively parallel processing

(MPP) databases, data-mining grids, distributed file systems, distributed databases,

cloud” (Vermesan & Freiss, 2013). The biggest challenge of the Petabyte Age will not

be storing all this data, it will be figuring out how to make sense of it. “Big data deals

with unconventional, unstructured databases, which can reach petabytes, exabytes or

zettabytes, and require specific treatments for their needs, either in terms of storage or

processing/display” (Vermesan & Freiss, 2013, 85). In future, it is expected a huge

increase in adoption, and many questions that must be addressed.

“Among the imminent research targets in this field are: privacy - big data systems

must avoid any suggestion that users and citizens in general perceive that their privacy

is being invaded; integration of both relational and NoSQL systems; more efficient

indexing, search and processing algorithms, allowing the extraction of results in reduced

time and, ideally, near to “real time” scenarios; and optimized storage of data - given the

amount of information that the new IoT world may generate, it is essential to avoid that

the storage requirements and costs increase exponentially” (Vermesan & Freiss, 2013

84).

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TRENDS IN BUSINESS ECOSYSTEMS

Figure 5: Market drivers and barriers

(Retrieved from http://www.businessinsider.com/iot-ecosystem-internet-of-things-forecasts-and-business-opportunities-2016-2)

Business Solution

Enterprises, as the third category of IoT users have different needs and different

drivers that can potentially push the introduction of IoT-based solutions. Examples of

the needs are: increased productivity — this is at the core of most enterprises and

affects the success and profitability of the enterprise; market differentiation — in a

market saturated with similar products and solutions, it is important to differentiate, and

IoT is one of the possible differentiators; cost efficiency — reducing the cost of running

a business is a ‘mantra’ for most of the CEOs. Better utilization of resources, better

information used in the decision process or reduced downtime are some of the possible

ways to achieve this” (Vermesan & Freiss, 2013 38).

Another important topic which needs to be understood is the business

rationale behind each application. In other words, understanding the value an

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application creates. Important research questions are: “who takes the cost of creating

that value; what are the revenue models and incentives for participating, using or

contributing to an application?” (Vermesan & Freiss, 2013, 38).

Figure 6: IoT solution investments

(Retrieved from http://www.businessinsider.com/iot-ecosystem-internet-of-things-forecasts-and-business-opportunities-2016-2)

Lessons Learned / Business Case

Companies focused on the big data topic, “such as Google, Yahoo!, Facebook or

some specialized start-ups, currently do not use Oracle tools to process

their big data repositories, and they opt instead for an approach based on distributed,

cloud and open source systems” (Vermesan & Freiss, 2013). A popular example is

Hadoop, an Open Source framework in this field that allows applications to work with

huge repositories of data and thousands of nodes. “These have been inspired by

Google tools such as the MapReduce and Google File system, which in many cases do

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TRENDS IN BUSINESS ECOSYSTEMS

not comply with the ACID (atomicity, consistency, isolation, durability) characteristics of

conventional databases” (Vermesan & Freiss, 2013).

The ecosystems of intelligent systems have opened opportunities for businesses

to provide new innovative ways within their products and services to enhance the quality

of life for the customer or consumer (Schechter, 2015). The Internet of Things came

about quickly and hit the ground running – and is moving at an even faster rate. “What

will the Internet of Things evolve towards in the future? The answer to this question is –

possibly everything” (Schechter, 2015). If a person can think it, technology intelligence

can create it.

An example of the Internet of Things is something Streetline mobile app offers.

When a parking garage or lot business uses Streetline technology, a driver using their

app can be “told” and guided to where an empty parking spot is available. The

convenience for the driver and increase in business revenue go hand-in-hand

(Schechter, 2015).

So what’s really required to drive innovation and new collaborative business

models for Smart Systems? Changing the risk/reward formulas for alliances and new

relationships for the Internet of Things involves three interrelated elements: a vision for

how collaboration networks will drive “catalytic” innovation to help focus participants; a

platform to organize value creation which provides leverage to reduce the investment

and effort participants need to make; and relationship enablers and incentives which

persuade participants that the ecosystem developer is serious and can really scale

entirely new value creation and delivery system (Harbor Research). These three

ecosystem design and development elements combine to help the “organizer” quickly

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attract and mobilize a critical mass of participants. This is what is required to unleash

the powerful network effects we all expect to see as the Internet of Things evolves

(Harbor Research).

Why I Care

The first takeaway is that society, as well as enterprises can strongly benefit from

Smart Ecosystems although it poses to be both an opportunity and threat for

companies. The second takeaway from the research is that context-sensitivity,

intelligence and added value are all delivered by software, making software the unique

selling propositions in the future. Lastly, the key to success for smart ecosystem

enterprises will be software engineering as it will be pivotal to achieve the right goals, at

the right time with the right level of quality.

Figure 7: ROI for IoT ecosystem entities

(Retrieved from http://www.businessinsider.com/iot-ecosystem-internet-of-things-forecasts-and-business-opportunities-2016-2)

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References

Dickson, B. (2016, June 22). The implications of large IoT ecosystems. TechCrunch,

Retrieved from https://techcrunch.com/2016/06/22/the-implications-of-large-iot-

ecosystems/

Has anyone seen a real internet of things ecosystem? (2013, November 19). Harbor

Research. Retrieved from http://harborresearch.com/has-anyone-seen-a-real-

internet-of-things-ecosystem/

Here's how the Internet of Things will explode by 2020. (2016, August 31). Business

Insider, Retrieved from http://www.businessinsider.com/iot-ecosystem-internet-

of-things-forecasts-and-business-opportunities-2016-2

Knodel, J. (n.d.). Smart ecosystems. Retrieved from

https://www.iese.fraunhofer.de/en/innovation_trends/smart_ecosystems.html

PLE and PLEI. (n.d.). In Innovation Methods for Award Procedures of ICT Learning in

Europe. Retrieved January 15, 2017, from http://www.imaile.eu/about/ple-

personal-learning-environments/

Schechter, B. (2015, December 16). Building intelligent ecosystems – the internet of

things (IoT). Covisint, Retrieved from https://www.covisint.com/blog/building-

intelligent-ecosystems-the-internet-of-things-iot/

Vermesan, O. & Friess, P. (2013). Internet of things: Converging technologies for smart

environments and integrated ecosystems. Aalborg, Denmark: River Publishers.

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