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All content in this area was uploaded by Mohammad Fazli Baharuddin on Dec 22, 2015
Content may be subject to copyright.
Proceedings of the International Conference on Information Science (ICIS) 2015, SHAH ALAM
24
Identifying Big Data in Information Spectrum Using an Ontology for Efficient
Decision-Making
Tengku Adil Tengku Izhar, Halida Yu, Mohammad Fazli Baharuddin
Faculty of Information Management, Universiti Teknologi MARA, MALAYSIA.
(Email:tengkuadil@yahoo.co.uk)
ABSTRACT
Big data have transformed the way organizations across industries implement new approach to
handle huge amount of data. It means change in skills, structures, technologies and architectures.
Organizations rely to this data to achieve specific business priorities. The challenge is to capture
and analyse this data into useful information for the specific organization activities because
determining relevant data is a key to delivering value of information and knowledge from massive
amounts of data collection. The aim of this paper is to describe big data in information spectrum to
identify relevant data from large collection of big data to assist information professionals with
useful information for decision-making process. The paper discusses how this approach provides
conceptually simple yet meaningful results that can be used to evaluate big data in organizations.
Ontology is applied to illustrate the relationship between big data and information spectrum.
Keywords: analytic, big data, ontology, organizational goals, The World Bank
INTRODUCTION
This paper discusses a holistic approach to evaluation big data to help information professionals to
automate, accelerate and integrate the existing types of data in the organizations. Information
professionals are seen as an organizational community that assist leaders in the decision-making
process by developing and facilitating focused leadership (Wang & Swanson, 2007). They rely on
data to improve their decision-making to maximize the organization profit, find solutions to
problems and evaluate to what extent the organizational goals could be achieved (Izhar et al.,
2013). Even though there are many recent studies have been done on big data in the context of the
organizations (Berber et al., 2014; Galbraith, 2014; Hazen et al., 2014). There is still little debate
these days about big data in evaluating the organization goals. There is yet no consensus about how
best to incorporate big data in the organizations and how the process of incorporating the big data
can identify relevant data to assist information professionals with useful information.
The evaluation of big data is important in organizations in order to identify relevant data for certain
organization priorities. In order to achieve this aim, we identify the relationship between big data
and information spectrum using an ontology. The relationship is important to assist information
professionals to evaluate relevant data for decision-making process. By incorporating the big data,
an ontology make the process to identify the relevant data more easily consumable to address
which data from the datasets are important in evaluating the goals. The outcome of this paper will
assist information professionals with competitive edge to apply useful information in relation to the
specific organizational priorities.
METHODS
Despite the various existing methodologies to evaluate the organizational process based on an
ontology (Fox et al., 1998; Rao et al., 2012; Sharma & Osei-Bryson, 2008), this paper focus on
structuring the relationship between big data and information spectrum in relation to the
organizational goals. The process consists of identifying which data are relevant to be analysed into
useful information that can contribute to the decision-making process. In this paper, we used data
from The World Bank (see http://www.worldbank.org). We aim to analyse the level of the
Proceedings of the International Conference on Information Science (ICIS) 2015, SHAH ALAM
25
economic growth in South East Asia in 2013. We decide to analyse the development in Indonesia,
Cambodia, Malaysia, Philippines and Singapore. In order to achieve this aim, we analyse these five
indicators that we believe is important to evaluate the development level of the economic growth in
South East Asia. This is how we define the main goal in this case study. We are mindful of the fact
that information professionals might define the goals in a different way to the way we have
undertaken to define the goal, which would require a different approach to evaluate the goals.
Economic growth is central to economic development. When national income grows, real people
benefit. While there is no known formula for stimulating economic growth, data can help
information professionals to assist the policy-makers with better understand the economic
situations in South East Asia and guide any work toward improvement. Data here covers measures
of economic growth based on the value added for industry, agricultural, manufacturing, service and
household. In this paper, we defined five goals in relation to the economic growth as follows:
Goal 1: Development of services for wholesale and retail trade.
Goal 2: Development of agricultural production.
Goal 3: Development of manufacturing to supports industries growth.
Goal 4: Development of industry to supports sustainable growth.
Goal 5: Development of household value for goods and services.
In order to evaluate the goals, we assigned different weights of value to measure the level of
economic growth development. We are mindful that information professionals might want to
analyse data in a different way to the way we have undertaken the analysis in this case study, which
would require a different approach to define the metrics. After that, we assigned a rating scale to
rank different categories of economy development in relation to the goal. This rating is important
to identify which categories of economy development are important for the goal. Rank for the
categories is rating based on the following scale of 1 (strongly contribute) to 5 (not contribute). The
rating scale of 1 to 5 is set of rank design to elicit five different goals that contribute to the
economy development in South East Asia. It is a method that requires the rater to rank the
categories in relation to the goal. In this metrics, the rating can be assigned with the same rating.
After we assigned the rank, we analyse the weight to evaluate the economic growth. The metrics
calculate the ranking average for each goal to determine which goal is most preferred overall. The
goal with the largest ranking average is the most achieved goal. The ranking average is calculated
as follows, where:
w = weight of ranked position
x = rating for response count
RESULTS
After selecting data indicators and retrieving datasets, we summarized the datasets to evaluate the
overall contribution of South East Asia countries toward their economic growth. Based on Figure
1, we conclude that economy growth development for each goal is still low. The results show that
only goal 2 contributes more than 50% to the economy. Goal 1, goal 4 and goal 5 contribute lower
than 40% to the economy. This percentage is low in meeting the World Bank goals, which is to end
extreme poverty and to increase income growth. According to the World Bank Annual Report
2013, over the past three decades, the extent of global poverty has decline rapidly. The percentage
of people living in extreme poverty in 2013 is less than half of what it was in 1990. Based on this
trend, it is possible to envision a world in which extreme poverty has effectively been eliminated
within a generation. Therefore, countries in South East Asia must overcome any challenges to
Proceedings of the International Conference on Information Science (ICIS) 2015, SHAH ALAM
26
maintain the recent momentum in poverty reduction. This is because more than 1 billion people
worldwide are still destitute, inequality and social exclusion seem to be rising in several countries.
Figure 1. Economic growth development in South East Asia.
DISCUSSION AND CONCLUSION
A unique contribution of this paper is its perspective by examine the role of information
professionals that not just focus of managing the information but also responsible in capturing and
analyzing relevant data. The paper demonstrated the challenges to identify relevant data to be
processed into useful information to support decision-making process in relation to the data goals.
Evidence from the case study has shown that organizational goals ontology can be effectively
identify relevant data in relation to the goals. We found that despite the challenges in capturing
relevant data from large data collection, filtering this data using an ontology would be a better
solution to analyse this data as they could be relevant for better decision-making. The contribution
of this paper could benefit both information professionals and information management. In
conclusion, we conclude that the phenomena of big data really impact how organizations manage,
store and use their data. As a result, the role for information professionals are not just limits to
collect, store and disseminate information but having an ability to identify all different data which
organizations may have for better use.
REFERENCES
Berber, M., Graupner, E., & Maedche, A. (2014). The information panopticon in the big data era.
Journal of Organization Design, 3(1), 14-19.
Fox, M. S., Barbuceanu, M., Gruninger, M., & Lin, J. (1998). An organization ontology for
enterprise modelling Simulation organizations: Computational models of institutions and
groupsAAAI/MIT Press (pp. 131-152).
Galbraith, J. R. (2014). Organization design challengers resulting from big data. Journal of
Organization Design, 3(1), 2-13.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data
science, predictive analytics and big data in supply chain management: An introduction to
the problem and suggestions for research and application. International Journal Production
Economics, 154, 72-80.
Izhar, T. A. T., Torabi, T., Bhatti, M. I., & Liu, F. (2013). Recent developments in the organization
goals conformance using ontology. Expert Systems with Applications, 40(10), 4252-4267.
Rao, L., Mansingh, G., & Osei-Bryson, K.-M. (2012). Building ontology based knowledge maps to
assist business process re-engineering. Decision Support Systems, 52(3), 577-589.
33%
63%
47%
36% 30%
0
10
20
30
40
50
60
70
Development of
services for
wholesale and
retail trade
Development of
agricultural
production
Development of
manufacturing to
supports industries
growth
Development of
industry to supports
sustainable growth
Development of
household value for
goods and services
Economic growth development in South East Asia
in 2013
Goals
Proceedings of the International Conference on Information Science (ICIS) 2015, SHAH ALAM
27
Sharma, S., & Osei-Bryson, K.-M. (2008). Organization-ontology based framework for
implementing the business understanding phase of data mining projects. Paper presented at
the International Conference on System Sciences, Hawaii.
Wang, P., & Swanson, E. B. (2007). Launching professional services automation: Institutional
entrepreneurship for information technology innovations. Information and Organization,
17(2), 59-88.
