Nick Tan Shiang Yen


A non-stop learning data scientist, software engineer, and learning facilitator with a demonstrated history of working in the higher education industry and joint research with government agencies.

Experienced in data-centric software design and development. Highly passionate about developing software applications that empower end users with advanced analytics techniques, such as machine learning and statistical modeling, while taking away the technical complexity. Capable of developing and configuring software solutions in each architecture layer: business logic, user interface, API, database, and hosting environment. Familiar with Python, C#, SQL, JavaScript, and VBA. Keen and capable of learning new languages and frameworks.

Inspired to be a full-stack data scientist. Experienced in data analytics, machine learning, data visualization, and statistical modeling. Familiar with Scikit-Learn, Tableau, IBM SPSS and common data analytic tools. Keen to learn new data science technologies.

An education professional with a Doctor of Philosophy (Ph.D.) focused on a Visual Data Analytics platform that combines the strengths of human intuition and machine learning techniques to derive actionable insights from complex data, graduated from Queensland University of Technology (QUT), Australia.







Data Science & Analytics

Full-Stack Software Development



Workplace Personality



Machine Learning

Modeling Techniques


Web Development

WindowsSoftware Development


Database Development

Graphics Design

Institute: National University of Singapore (NUS)

Sector: Tertiary Education

Field: Data Science & Analytics

Department: The Office of Provost

  • Responsible for Times Higher Education (THE), Quacquarelli Symonds (QS) and other university rankings related data analysis and simulation
  • Analyze institutional-wide data to support decisions making of President and Provost of the university
  • Analyzed and forecast the impacts of Continuous Education & Training (CET) on revenue, student enrolments, and teaching resources
  • Simulate the effects of new university policy on student intakes and tuition fee structures
  • Lead data-related initiatives to make the department analytics-ready

Institute: Queensland University of Technology (Australia)

Sector: Tertiary Education

Field: Information Systems

  • Developed a model for analyzing student feedback which can be used to improve teaching performance and student satisfaction
  • Designed a course structure visualization to visualize course prerequisites and flow in a intuitive way for staff and students
  • Worked closely with the teaching & learning director to produce routine and ad-hoc reports
  • Held the role of data analyst and model developer in a software develop team that consists of 6 other multidisciplinary staff
  • Led and supervised teams of master and undergraduate students in data analysis software development projects
  • Delivered oversea analysis-related lectures (e.g. data analysis, database, Á cloud computing)

Institute: universiti Sains Malaysia (UMS) (Penang | Malaysia)

Sector: Tertiary Educaton

Field: Information Systems

  • Planned and conducted research for multiple Malaysia government agencies
  • Designed and executed data collection at national level
  • Analyzed data and generated reports for the government agencies
  • Conducted showcases and exhibitions at national IT events
  • Managed projects and resources

Institute: universiti Sains Malaysia (UMS) (Penang | Malaysia)

Sector: Tertiary Educaton

Field: Information Systems

  • Executed data collection and data analysis
  • Collected business requirements
  • Converted business requirements into technical specifications
  • Conducted user-training workshops
  • Prepared documentations for end-users
  • Published findings in articles and book chapters

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 :
  • Designed and developed a data analytics platform to support the blend of both human intuition and machine-driven analytics for achieving actionable insight from complex data
  • Designed and developed a stock analysis software for visually analyzing stock prices, financial statements, and news
  • Designed a social network analysis (SNA) platform for visualizing and analyzing social network data

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:
  • Developed and deployed a measurement model for measuring information quality of ERP systems of SMEs
  • Adopted structural equation modeling (SEM)

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:

  • Operation Management
  • Management Science
  • Productivity & Quality Control
  • Material Management
  • Business Statistics
  • Project Management
  • See full courses

Minor - Information Technology:

  • Technology Management
  • Web Engineering & Technology
  • Information System Theory
  • Principle of Programming
  • Database Organization & Design
Major Courses (Specialization)
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
MicroeconomicsPrinciple of Marketing
MacroeconomicsPrinciple of Finance
Financial AccountingManagement Accounting
Business StatisticsManagement Information System
EnterpreneurshipBusiness & Communication English
Business Law

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