A data science practitioner and trainer who is always fascinated about how to use digital tools and data to get sh%t done right and efficiently.
Experienced in data analytics, statistical modeling, data visualizations, and quantitative research methods. Passionate to explore new or innovative methods that use data to approach practical problems differently.
Experienced in data-centric software development. Familiar with Python, JavaScript, C#, and VBA. Current focus is web application based on dockerization and microservice architecture
With a doctorate research focused on creating analytics platforms that synergize the strengths of a) subjective human intuition and b) machine learning algorithms to navigate complex problems and to derive at critical insights.
Institute: GovTech Singapore
Sector: Technology | Public Sector
Field: Data Science & Analytics
Department: Data Science & AI Division (DSAID)
Institute: National University of Singapore (NUS)
Sector: Tertiary Education
Field: Data Science & Analytics
Department: The Office of Provost
Institute: Queensland University of Technology (Australia)
Sector: Tertiary Education
Field: Information Systems
Institute: Queensland University of Technology (Australia)
Sector: Tertiary Education
Field: Information Systems
Institute: University of Science, Malaysia (USM)
Sector: Tertiary Education
Field: Information Systems
Institute: University of Science, Malaysia (USM)
Sector: Tertiary Education
Field: Information Systems
2012 - 2017
Research Title: Advanced Visual Analytics: A machine-augmented cognition approach to data analytics
Awarding Institute | : | Queensland University of Technology (QUT) |
Field | : | Computer Science / Information Systems |
Research Area | : | Data Analytics, Data Science, Visual Analytics |
Award | : | QUT Postgraduate Research Award (QUTPRA) |
The goal of this study is to develop a design of data analytics systems that can explicitly support the analyst´s problem-solving activities. This study theorized that when the problem-solving activities are supported, analysts are more likely to produce higher-level insights that can more readily inform decision-making. In order to achieve this design goal, this study asserts there is a need for 1) systematically understanding and defining actionable insight, 2) understanding the problem-solving activities and outcomes required to achieve actionable insight, and 3) proposing system features that can effectively support the problem-solving activities. See full abstract
Background: To solve complex analytics problems, data analysts often engage in a series of problem-solving activities, including extracting meaningful information from the data, synthesizing information to form higher-level concepts, creating a mental depiction of the problem, and imagining the impacts of possible scenarios. However, existing data analytics systems often focus exclusively on low-level data exploration and fail to effectively support these problem-solving activities. Consequently, the analysts have to contend with the gaps between the low-level technical analytic results and the high-level conceptual understandings which are required to solve the complex analytics problems. As the result, data analysts often find it is challenging to determine how the analytic results can be used to inform their decision-making and solve their real-world problems. In other words, existing data analytics systems often fail to deliver the actionable insight.
Objectives: The goal of this study is to develop a design of data analytics systems that can explicitly support the analyst´s problem-solving activities. This study theorized that when the problem-solving activities are supported, analysts are more likely to produce higher-level insights that can more readily inform decision-making. In order to achieve this design goal, this study asserts there is a need for 1) systematically understanding and defining actionable insight, 2) understanding the problem-solving activities and outcomes required to achieve actionable insight, and 3) proposing system features that can effectively support the problem-solving activities.
Method: This study employs design science research as the methodology to guide the overall design of this study. Through an integrated understanding of relevant theories, namely situation awareness (SA), sensemaking, and complex problem solving, this study conceptualizes actionable insight as a multi-component construct. Based on the way actionable insight is conceptualized, an explanatory framework is developed to provide a holistic explanation for the complex analytics task. This framework is specifically contextualized in the field of data analytics to explain the information processes, user behaviours, cognitive states, and information artefacts in different phases of a complex analytics task. More importantly, this explanatory framework provides systematic and theoretically-grounded design requirements which can be leveraged to improve user performance in the problem-solving activities.
A design framework was then developed to provide a set of prescriptive design principles for how the design requirements can be addressed. The design framework also acts as the blueprint for translating the conceptual design into tangible system features. To evaluate the effectiveness of the proposed design, a user study involving 30 participants was undertaken in a controlled setting. A prototype system was developed based on the design framework. The prototype system was evaluated against a conventional data analytics system in the user study. The user study requires the participants to analyse stock markets and to develop stock portfolios that will maximize the returns of investment.
Findings: This study categorizes the problem-solving activities into three phases: 1) data exploration, 2) information synthesis, and 3) knowledge actualization. The result shows that the proposed design is capable of enhancing the participants performance in the information synthesis and knowledge actualization phases, but not in the data exploration. Additionally, the proposed design was found to increase the perceived quality of the analytical result, implying that the results are more likely to be deployed into the physical world through decision making. Lastly, mixed results were found on the effects of the proposed design on actual decision performance. Specifically, the qualitative aspect of the participants decisions has been significantly improved, but the quantitative aspect of the decisions was not improved over the conventional data analytics system.
Overall, the findings suggest that the proposed design can support users to be more effective in integrating low-level technical findings into a holistic understanding of the problem situation and predicting and assessing the plausible impacts of the problem situation´s future development. Such understandings that are meaningful at the problem-solving level reduce the gaps between the low-level technical analysis and high-level understanding required for solving analytical tasks. In comparison with conventional data analytics systems, the proposed design enables the users to derive analytics results that can more readily be used to inform decision making and to solve the complex analytics problem. In other words, the proposed design improves the chance of deriving actionable insight from the data.
Conclusion: The contributions of this study include the explanatory framework which provides systematic understanding of analyst’ workflow, behaviours, and information needs, as well as other design considerations, in three different phases of the data analytics process. The framework can be used by data analytics researchers to understand the design considerations and requirements without repeatedly integrating the scattered knowledge from different domains. Additionally, the design framework can provide useful guidelines for practitioners to build the data analytics systems that can effectively support the problem-solving activities of the users. As a further implication, it is hoped that the proposed data analytics system can help practitioners to harness greater value out of their data, and thus can turn their IT investments into value-creation assets.
Outcomes | : |
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2009 - 2012
Research Title: Impacts of ERP System Misfits on Information Quality: A moderate model of alignment strategies
Awarding Institute | : | Universiti Sains Malaysia (University Science of Malaysia) |
Field | : | Strategic Information Systems |
Research Area | : | Data Quality, System Analysis, Enterprise Systems |
Award | : | USM Fellowship Scheme, Postgraduate Research Grant (RU-PRGS) |
The objective of this research is to examine the impacts of ERP misfits on the information quality of ERP systems and to examine the effects of the two alignment strategies, namely system modification and organizational adaptation on the impacts of ERP misfits. In this study, ERP misfit is decomposed into three specific misfit variables, namely input misfit, process misfit, and output misfit. The purpose of such decomposition is to enable the data analysis can provide more detail information about ERP misfit. A total amount of 305 sets of questionnaire are collected from the ERP system users in manufacturing sector in Malaysia based on purposive sampling. Respondents are sampled from the industrial areas in Klang, Shah Alam, Petaling Jaya, Johor Bahru, and Penang where most of the manufacturing companies are located. See full abstract
Outcomes | : |
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In the last decade, businesses have moved away from "in-house-developed" software systems to packaged systems that are developed to be used by most of the businesses in general. One of the most prevalent packaged systems is enterprise resource planning (ERP) system. ERP systems are now recognized as the enabler for businesses to achieve data integration, improve operational performance, and attain strategic advantage. However, more than half of the ERP system implementations are reported as failed and did not achieve the expected benefits. Researchers have asserted that the failure of ERP system is mainly attributed to ERP misfits, which are the misalignments between the ERP system functionalities and the organizational requirements. Current research on ERP misfit is still inadequate to comprehensively understand the phenomenon. Modification of the ERP system and adaptation of the business processes have been posited as the means to enable better system-process alignment. But very little empirical evidence exists to demonstrate that the potential of these alignment strategies have been realized.
Thus, the purpose of this study is to provide empirical and generalizable findings to fill the gaps in the existing body of knowledge. Specifically,the objective of this research is to examine the impacts of ERP misfits on the information quality of ERP systems and to examine the effects of the two alignment strategies, namely system modification and organizational adaptation on the impacts of ERP misfits. In this study, ERP misfit is decomposed into three specific misfit variables, namely input misfit, process misfit, and output misfit. The purpose of such decomposition is to enable the data analysis can provide more detail information about ERP misfit. A total amount of 305 sets of questionnaire are collected from the ERP system users in manufacturing sector in Malaysia based on purposive sampling. Respondents are sampled from the industrial areas in Klang, Shah Alam, Petaling Jaya, Johor Bahru, and Penang where most of the manufacturing companies are located. The data are then analyzed using Structural Equation Modeling (SEM) approach.
The findings of this study reveal that each component of the ERP misfit carries different weights in influencing the information quality of ERP systems. Specifically, process misfit is found to have greatest negative impact on ERP system information quality, followed by input misfit. However, there is no evidence to prove that output misfit negatively affects information quality. With regard to the effects of the alignment strategies, system modification is found to be more effectively in reducing the process misfit and input misfit which occur at the deeper layers of system architecture such as data layer and application layer. In contrast, organizational adaptation in the form of changes in business process and practices are found to be effective in resolving output misfit that occurs at the surface layer such as presentation or interface layers. As the implications of this study, practitioners such as ERP and IT managers would be able to prioritize the ERP misfit problem solutions according to their severity. More importantly, the study provides information for the manager regarding which alignment strategies suit the particular kind of misfit problem better.
2006 - 2009
Qualification: Bachelor Degree with First Class Honor
CGPA: 3.88 /4.00
Specialization Courses:
Minor - Information Technology:
Major Courses (Specialization) | |
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Management Science | Material Management |
Productivity & Quality Control | Business Statistics |
Project Management | Business Research Method |
Core Courses | |
Introduction to Management | Strategic Management |
Business Communication | Organizational Behavior |
Microeconomics | Principle of Marketing |
Macroeconomics | Principle of Finance |
Financial Accounting | Management Accounting |
Business Statistics | Management Information System |
Enterpreneurship | Business & Communication English |
Business Law |