Analysis of Information Systems

This report analyzes the implementation of a data-driven business model by Nettavisen, a Norwegian online news organization, and explores the reasons behind the usage of big data, key sources and activities, customers for DDBM, ways to monetize DDBM, and barriers faced by Nettavisen. The report highlights the importance of data analysis in gaining a competitive advantage in the industry.

Running head: ANALYSIS OF INFORMATION SYSTEMS
ANALYSIS OF INFORMATION SYSTEMS
Name of the Student
Name of the University
Author Note

1ANALYSIS OF INFORMATION SYSTEMS
Table of Contents
Introduction....................................................................................................................2
Reasons behind the usage of big data by Nettavisen.....................................................3
Customers or users for DDBM......................................................................................4
Key sources and key activities for DDBM....................................................................5
Ways by which Nettavisen can monetise DDBM and also generate revenues..............6
Barriers faced by Nettavisen in pursuing DDBM..........................................................9
Conclusion....................................................................................................................11
References....................................................................................................................12

2ANALYSIS OF INFORMATION SYSTEMS
Introduction
The use of information and data has started increasing in the past few years due to
rising competition in the industry. The power based on the prices of the services and products
have recently shifted to the consumers. The media organizations that do not use big data
based technology have not been successful in the proper growth of a sustainable competitive
advantage in the industry (Abdelkafi and Pero 2018). The levels of demands that exist in
different industries can be met effectively with the support of appropriate study of data. The
competitive advantage is thereby gained by the companies that are able to offer the media
companies with a major position in the industry (Adrian et al. 2018).
The report will be built on the analysis of a firm named Nettavisen that is aiming at
shifting its operations towards data-driven business model or DDBM. The most important
factor related to the usage of this model is built on the methods by which the buying pattern
of customers can be analysed and the information can be used for the proper development of
different products of the organization. The analysis that will be developed in the report will
help the organization to take a decision based on implementation of DDBM in order to
develop the products that need to be offered to the consumers (Akter et al. 2016).
Nettavisen had started its operations in Norway in the year 1996 and is a totally online
start up that is competing with the different incumbent publishing based firms in the market
of online new that is rapidly growing in nature. Nettavisen has been providing tough
competition to some major organizations with the help of its products and services (Zheng,
Xu and Chen 2018). The organization has been successful in gaining its share in the market
with the backing that is offered by the disruptive business models. The use of use of
disruptive technologies have helped the organization that is new to the business to grow a
place in the industry. The success that was gained by Nettavisen within a short period of time

3ANALYSIS OF INFORMATION SYSTEMS
has helped Nettavisen to develop a different image in comparison to the competitors (Zheng,
Xu and Chen 2018).
Reasons behind the usage of big data by Nettavisen
Nettavisen has been successful in developing a competitive advantage in the industry
with the backing that is offered by the proper business model that is considered to be
disruptive in nature. The organization was acquired by a Swedish Web portal named Spray
and further the charges of its operations were transferred to another German organization
named Lycos. The acquisition based process of Nettavisen continued when the organization
was further taken over by Norwegian broadcasting organization named TV2 (Basole et al.
2015). The online operations of TV2 were already active that were mainly able to offer key
stages of support to the growth of Nettavisen in the industry. The collapse of dot.com had a
major influence on the processes of Nettavisen after it was acquired by TV2 in the year 2002.
The partnership that had been developed by the two organizations TV2 and Nettavisen did
not prove to be effective for the operations of the organization and online revenues that have
been gained as well (Bocken and Short 2016).
This had led to the separation of Nettavisen from TV2 due to lack of proper revenues
in the industry. The business model that had been followed by Nettavisen in industry was
considered to be entirely based on revenues that were gained from advertisements. The entire
amount of money was earned by Nettavisen with the help of advertisements that were placed
on the website. The major goal of the organization from the start of its operations has been on
providing direct competition to the online news based sites that are developed and operated
by the established broadcasters and newspapers (Bradlow et al. 2017). The most significant
strategic intent of the organization was to develop a competitive benefit in the online new
based market. The requirement of innovation in the media based organizations is higher in

4ANALYSIS OF INFORMATION SYSTEMS
comparison to the those which operate in the other industries. The organization is not
concerned about the levels of cannibalisation that can have an impact on the different areas of
business. The implementation of new processes and strategies is however considered to be
extremely significant for the organization to uphold the processes (Choi, Chan and Yue
2016).
The need for effective innovation for development of sustainable competitive
advantage is considered to be an important reason based on implementation of DDBM or data
driven business model. The dynamic nature of organizational operations has proved to be a
major factor built on the processes of Nettavisen (Demirkan et al. 2015). An important
competitive benefit that had been gained by Nettavisen is built on the methods by which the
organization is able to publish the news without any restriction or making changes. The
company aimed at using the internet as a media to develop its revenues and stages of
profitability by publishing news that is considered to be important for the proper development
of its competitive advantage and presence in comparison to the other large organizations
(Cronholm, Göbel and Rittgen 2017). The online revenues that have been grown by the firm
are also built on the growth of strategies and business model of Nettavisen. The move of
Nettavisen towards DDBM is also based on the ways by which demands and needs of
consumers can be taken into consideration in order to grow an advantage in the industry. The
experiences that are provided by the organizations to its customers are considered to be
important factors which are developed with the support of appropriate big data study (Fan,
Lau and Zhao 2015).
Customers or users for DDBM
The management of the organization is quite clear about the objectives that have been
set after implementation of DDBM in the operations. The online platforms and big data are

5ANALYSIS OF INFORMATION SYSTEMS
considered to be the most significant factor related to the business processes that are achieved
by Nettavisen. The organization has also tried to push the boundaries with the help of proper
application of big data in the operations. Data that is used by the organizations is mainly
based on the resources that can help in the development of effective experiences that can be
provided to the customers (Gölzer and Fritzsche 2017). The organization also aims at pushing
the boundaries based on development of online new based activities for the customers who
are highly technology savvy in nature. The needs and demands of the customers will be taken
into consideration by the firm in order to develop the Data Driven Business Model. The
major customers of Nettavisen who are affected by the application of DDBM or Data Driven
Business model are analysed with respect to their behaviour (Hartmann et al. 2016).
The analysis of data of the customers in an effective manner is able to offer backing to
the company based on the position that can be gained by Nettavisen and the experience that
they can provide as well. The strategies that are developed by Nettavisen based on analysis of
customers are able to influence the stages of profitability of the organization and the
competitive advantage that is gained in the market as well (Heinze et al. 2016). The process
of big data analytics can be used by Nettavisen in order to uphold its place in the market for a
lengthier period of time. The levels of sustainability in competitive benefit that has been
grown by the organization are considered to be the most significant results of the
implementation of the Data Driven Business Model or DDBM in Nettavisen (Ji-fan Ren et al.
2017).
Key sources and key activities for DDBM
The data sources that are used by Nettavisen in order to uphold its processes in the
industry are based on the usage of internal data that is made available with the help of
existing database. The major usage of existing database is related to the activities related to

6ANALYSIS OF INFORMATION SYSTEMS
social networks, advertising, readership, sales, user-ship, mobile data, click-stream based
data, accounting based web logs, membership and content (Kumar, Shankar and Thakur
2018). The unstructured data is collected by the firms from different digital channels that
include, audio, photos, digital channels, social media based text and photos. The activities
that are performed in the newsroom of Nettavisen are able to influence the methods by which
the firm is able to uphold its place. The different teams that are formed within the
organization are responsible for the analysis of Twitter, Instagram and Facebook in order to
collect data based on the consumers (Larson and Chang 2016).
The communication of the employees of Nettavisen with the employees is developed
in an active method with the support of analysis that the organization implements based on
the big data and its usage. The blogger network is another major data source that is used by
Nettavisen in order to maintain gather and analyse data in an efficient way. The blog data is
considered to be highly useful for the ways by which Nettavisen can analyse data or
information based on customers (Liu et al. 2016). The requirements and demands of the
online surfers are successfully understood by Nettavisen in order to develop the editorial
content. The different sources of data that are used by Nettavisen are able to play a key part
in the methods by which the company can implement the Data Driven Business Model or
DDBM. Nettavisen was able to bring a major change in the online newspaper market with the
support of its disruptive technologies and innovative capabilities as well (Loebbecke and
Picot 2015).
Ways by which Nettavisen can monetise DDBM and also generate revenues
The actions that are completed by Nettavisen built on the usage of big data in the
operations are able to offer key development opportunities to the organization. The editor of
the front page of the newspaper is provided with the most significant part in the methods by

7ANALYSIS OF INFORMATION SYSTEMS
which the firm is able to reach the customers with important news. The stories are sold by the
reporters to front page editor of Nettavisen in order to develop the revenues of the
organization in an active way (Mandelbaum, Kadish and Bunnell 2015). The usage of data
analytics will provide support to Nettavisen so that the organization is able to take the real
time based decisions in an efficient manner. The real-time based decisions that are taken by
the management of Nettavisen is supported by the proper analysis of data of customers. The
online purchases made by customers are tracked and the organization aims at implementing
appropriate strategies that can help in usage of the data in order to expand the stages of their
experiences that are offered to them (Mathis and Köbler 2016).
The experiences offered to the customers are considered to be the most significant
result of the implementation of DDBM in the organization. Nettavisen will be able to upsurge
the levels of revenues and develop the competitive advantage with the support that is
provided by the data analysis of customers in a detailed way. The company will also be able
to divide the customers into different groups based on data that has been gained related to
their online activities (Mikalef et al. 2018). The advertising methods that are used by the
organizations in this case are also considered to be highly personalised built on the
requirements of the customers. The decisions based on development of effective
advertisements are taken by the management of Nettavisen (Olszak 2017).
The advertisements that are developed by the organization are mainly built on the
methods by which the company can provide the messages to respective customers in an
efficient manner. The personal information based on different activities of the consumers are
able to offer basic information related to their behaviour. The development of better
advertisements can also play a key part in targeting the customers (Prajogo 2016). The data
based on customers are used by Nettavisen in order to develop deep understanding related to
their needs. The process of personalised advertisements is considered to be a significant

8ANALYSIS OF INFORMATION SYSTEMS
factor that is able to offer key chances to the company in order to the communicate with the
consumers (Senderovich et al. 2016). The development of tailored content is also possible
with the support of appropriate study of data or information based on the customers. The
degrees based on influence of customers on the operations of Nettavisen are related to
development of different organizational activities (Täuscher 2018).
A large share of the market has already been gained by Nettavisen in the early years
of its processes in the online news industry. The company has gained a position among the
top three online news organizations during the period of 1996 to 2001. The performance that
has been depicted by Nettavisen has been quite slow or sluggish in nature. The lack of
effective performance had led to many different acquisitions that had taken place in the
industry. The market shares of Nettavisen had been influenced in an adverse way by the
absence of proper revenues (Vidgen, Shaw and Grant 2017). Nettavisen had been operating
in a flat way during the years 2004 to 2007. The market of online news was however
booming and the levels of competition are also growing. Nettavisen had faced a huge loss of
around 88.4 Million in the year 2008. The organization had started becoming profitable until
the year 2011 that was around 15 years of its establishment in the online news market. The
implementation of DDBM or Data Driven Business Model will be able to play a crucial part
in the growth of high levels of revenues in the market (Wang and Zeng 2019).
Nettavisen will be provided with an opportunity to increase its revenues in an
effective way with the support of effective data analysis process. The profitability that has
been developed by Nettavisen in the industry is supported and affected in a positive way with
the help of appropriate way with the data that is gained by the organization. The appropriate
analysis of big data is able to play a major part in the growth of the operations and
profitability levels of Nettavisen (Wong and Kim 2017). The competition that has been
developed in the industry over the years has been able to play a key part in increasing the

9ANALYSIS OF INFORMATION SYSTEMS
levels of importance of the analytical process related to big data. The difference between
commercial content and editorial content is influenced by the online services that are
developed by Nettavisen (Yin and Kaynak 2015).
The improvement of the turnover of the company is also related to the improvement
of the profitability levels of Nettavisen. The usage of data is considered to be extremely
significant for the methods by which customers and their levels of expectations can be
fulfilled by the management of Nettavisen (Zheng, Xu and Chen 2018). The dependence of
Nettavisen on the revenues that are gained by the advertisements is a key aspect that is able to
influence the processes. The competition levels in the industry will be faced in an effective
and efficient way with the help of appropriate study of big data that is available to the
management (Zaki, Bøe-Lillegraven and Neely 2016).
Barriers faced by Nettavisen in pursuing DDBM
The implementation of Data Driven Business Model or DDBM is a significant aspect
that is able to influence the methods by which Nettavisen can improve its operations. The
new technology that had been applied by the company in order to manage the operations of
Nettavisen is quite new. The organization will need to face major levels of challenges based
on the methods by which data analytics can be implemented by Nettavisen in order to the
manage the customers (Zaki, Bøe-Lillegraven and Neely 2016). A major issue that has been
faced by Nettavisen in the implementation of the technology is related to high levels of
personnel turnover in the organization. The lack of proper levels of consistency in the
company is considered to be significant aspect that has an influence on the work method of
employees. The experiences and training provided to the employees are based on the long
term career based plans (Wong and Kim 2017).

10ANALYSIS OF INFORMATION SYSTEMS
The collaboration levels that need to be formed by the employees are considered to be
significant aspects related to application of big data analytics in an effective manner. The lack
of appropriate data practices is able to influence the methods by which data or information of
the customers can be analysed by the organization. The volume or levels of data that is
gained by the organization have an influence on the operations of Nettavisen in the online
new based industry (Mandelbaum, Kadish and Bunnell 2015). The appropriate coordination
of the activities is considered to be a significant aspect that is able to support the processes of
Nettavisen (Mathis and Köbler 2016). The levels of complexity that exist in the analysis of
data is able to offer major stages of challenges built on the methods by which an organization
is able to function in the industry. The unique demands and needs of the consumers and their
behaviour has an influence on the methods by which the company is able to maintain its
operations in the industry (Zheng, Xu and Chen 2018).
The preferences of the readers are taken into consideration for development and
implementation of the strategy based on proper analysis of data. Nettavisen needs to consider
sentiments of the customers into consideration in order to develop the approaches that can be
implemented based on the proper usage of data. The competition that is provided to
Nettavisen is based on the methods by which the organization is able to uphold its place in
the online news based market (Loebbecke and Picot 2015). The industry has been dominated
and ruled by the large media based organizations and the proper development and
maintenance of the competitive benefit of Nettavisen is based on the proper usage of data.
Nettavisen therefore needs to use the existing resources in an active way in order to generate
high levels of revenues (Loebbecke and Picot 2015).
The competition is also faced by Nettavisen from the large organizations like
Facebook and Google based on the levels of data analytics that are performed. The readers or
the advertisers can migrate to the new digital platforms, however, the usage of Google and

11ANALYSIS OF INFORMATION SYSTEMS
Facebook is considered to be a significant aspect. The financial resources that are required for
the processes of Nettavisen in the industry is a major aspect that is able to affect
implementation of big data analytics. The revenue streams that are gained by the company
are also considered to be significant aspects based on proper growth of the data analysis
based methods (Zaki, Bøe-Lillegraven and Neely 2016). The levels of investments that are
required for the formation of proper data analytics are considered to be major barriers based
on implementation of data analytics. The data of customers is used by the company in order
to develop the operations or products based on their requirements and demands (Vidgen,
Shaw and Grant 2017). The levels of diversification of the products are also based on the
appropriate analysis of the reading based habits. The financial capabilities of the firm have a
key influence on the ways by which the company can develop the operations in the industry
with the proper analysis of the data that is provided to the customers. The lack of proper
financial capabilities is able to influence the productivity stages of the organization and data
analysis based process (Gölzer and Fritzsche 2017).
Conclusion
The report can be settled by asserting that the processes of Nettavisen is influenced in
a huge way by the proper usage of big data. The users or customers for Data Driven Business
Model are able to play a key part in the development of the services and experiences that are
provided to the customers. The activities and key resources based on implementation of
DDBM are related to the development of appropriate processes of Nettavisen in the online
news industry. Nettavisen can also monetise the data of the customers in order to proper
stages of revenues and to uphold a sustainable place in the market. The organization can also
face major levels of barriers related to application of data analytics in order to develop
effective customer experiences.

12ANALYSIS OF INFORMATION SYSTEMS

13ANALYSIS OF INFORMATION SYSTEMS
References
Abdelkafi, N. and Pero, M., 2018. Supply chain innovation-driven business models:
Exploratory analysis and implications for management. Business Process Management
Journal, 24(2), pp.589-608.
Adrian, C., Abdullah, R., Atan, R. and Jusoh, Y.Y., 2018, March. Expert Review on Big Data
Analytics Implementation Model in Data-driven Decision-Making. In 2018 Fourth
International Conference on Information Retrieval and Knowledge Management
(CAMP) (pp. 1-5). IEEE.
Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe, S.J., 2016. How to improve
firm performance using big data analytics capability and business strategy
alignment?. International Journal of Production Economics, 182, pp.113-131.
Basole, R.C., Russell, M.G., Huhtamäki, J., Rubens, N., Still, K. and Park, H., 2015.
Understanding business ecosystem dynamics: A data-driven approach. ACM Transactions on
Management Information Systems (TMIS), 6(2), p.6.
Bocken, N.M. and Short, S.W., 2016. Towards a sufficiency-driven business model:
Experiences and opportunities. Environmental Innovation and Societal Transitions, 18,
pp.41-61.
Bradlow, E.T., Gangwar, M., Kopalle, P. and Voleti, S., 2017. The role of big data and
predictive analytics in retailing. Journal of Retailing, 93(1), pp.79-95.
Choi, T.M., Chan, H.K. and Yue, X., 2016. Recent development in big data analytics for
business operations and risk management. IEEE transactions on cybernetics, 47(1), pp.81-92.

14ANALYSIS OF INFORMATION SYSTEMS
Cronholm, S., Göbel, H. and Rittgen, P., 2017. Challenges Concerning Data-Driven
Innovation. In The 28th Australasian Conference on Information Systems, Hobart Australia,
December 4-6, 2017..
Demirkan, H., Bess, C., Spohrer, J., Rayes, A., Allen, D. and Moghaddam, Y., 2015.
Innovations with Smart Service Systems: Analytics, Big Data, Cognitive Assistance, and the
Internet of Everything. CAIS, 37, p.35.
Fan, S., Lau, R.Y. and Zhao, J.L., 2015. Demystifying big data analytics for business
intelligence through the lens of marketing mix. Big Data Research, 2(1), pp.28-32.
Gölzer, P. and Fritzsche, A., 2017. Data-driven operations management: organisational
implications of the digital transformation in industrial practice. Production Planning &
Control, 28(16), pp.1332-1343.
Hartmann, P.M., Zaki, M., Feldmann, N. and Neely, A., 2016. Capturing value from big
data–a taxonomy of data-driven business models used by start-up firms. International
Journal of Operations & Production Management, 36(10), pp.1382-1406.
Heinze, A., Fletcher, G., Rashid, T. and Cruz, A., 2016. Digital and social media marketing:
a results-driven approach. Routledge.
Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R. and Childe, S.J., 2017. Modelling
quality dynamics, business value and firm performance in a big data analytics
environment. International Journal of Production Research, 55(17), pp.5011-5026.
Kumar, A., Shankar, R. and Thakur, L.S., 2018. A big data driven sustainable manufacturing
framework for condition-based maintenance prediction. Journal of computational
science, 27, pp.428-439.

15ANALYSIS OF INFORMATION SYSTEMS
Larson, D. and Chang, V., 2016. A review and future direction of agile, business intelligence,
analytics and data science. International Journal of Information Management, 36(5), pp.700-
710.
Liu, H., Guo, J., Yu, W., Zhu, L., Liu, Y., Xia, T., Sun, R. and Gardner, R.M., 2016, May.
The design and implementation of the enterprise level data platform and big data driven
applications and analytics. In 2016 IEEE/PES Transmission and Distribution Conference and
Exposition (T&D) (pp. 1-5). IEEE.
Loebbecke, C. and Picot, A., 2015. Reflections on societal and business model transformation
arising from digitization and big data analytics: A research agenda. The Journal of Strategic
Information Systems, 24(3), pp.149-157.
Mandelbaum, A., Kadish, S. and Bunnell, C.A., 2015, August. Data-Driven Performance
Analysis of Scheduled Processes. In Business Process Management: 13th International
Conference, BPM 2015, Innsbruck, Austria, August 31--September 3, 2015,
Proceedings (Vol. 9253, p. 35). Springer.
Mathis, K. and Köbler, F., 2016, May. Data-Need Fit–Towards data-driven business model
innovation. In Service Design Geographies. Proceedings of the ServDes. 2016
Conference (No. 125, pp. 458-467). Linköping University Electronic Press.
Mikalef, P., Pappas, I.O., Krogstie, J. and Giannakos, M., 2018. Big data analytics
capabilities: a systematic literature review and research agenda. Information Systems and e-
Business Management, 16(3), pp.547-578.
Olszak, C.M., 2017. Big Data driven strategy in organizations. ICT Management for Global
Competitiveness and Economic Growth in Emerging Economies (ICTM), p.59.

16ANALYSIS OF INFORMATION SYSTEMS
Prajogo, D.I., 2016. The strategic fit between innovation strategies and business environment
in delivering business performance. International Journal of Production Economics, 171,
pp.241-249.
Senderovich, A., Rogge-Solti, A., Gal, A., Mendling, J., Mandelbaum, A., Kadish, S. and
Bunnell, C.A., 2016, September. Data-driven performance analysis of scheduled processes.
In International Conference on Business Process Management (pp. 35-52). Springer, Cham.
Täuscher, K., 2018. Using qualitative comparative analysis and system dynamics for theory-
driven business model research. Strategic Organization, 16(4), pp.470-481.
Vidgen, R., Shaw, S. and Grant, D.B., 2017. Management challenges in creating value from
business analytics. European Journal of Operational Research, 261(2), pp.626-639.
Wang, Y. and Zeng, Z., 2019. Overview of Data-Driven Solutions. In Data-Driven Solutions
to Transportation Problems (pp. 1-10). Elsevier.
Wong, K.S. and Kim, M.H., 2017. Privacy protection for data-driven smart manufacturing
systems. International Journal of Web Services Research (IJWSR), 14(3), pp.17-32.
Yin, S. and Kaynak, O., 2015. Big data for modern industry: challenges and trends [point of
view]. Proceedings of the IEEE, 103(2), pp.143-146.
Zaki, M., Bøe-Lillegraven, T. and Neely, A., 2016. A transition towards a data-driven
business model (ddbm): A case study of nettavisen online newspaper publishing. SAGE
Publications: SAGE Business Cases Originals.
Zaki, M., Bøe-Lillegraven, T., & Neely, A. (2016). A transition towards a data-driven
business model (ddbm): A case study of nettavisen online newspaper publishing. SAGE
Publications: SAGE Business Cases Originals.

17ANALYSIS OF INFORMATION SYSTEMS
Zheng, P., Xu, X. and Chen, C.H., 2018. A data-driven cyber-physical approach for
personalised smart, connected product co-development in a cloud-based
environment. Journal of Intelligent Manufacturing, pp.1-16.