We are delighted to announce Aishah Selamat as one of our UK Data Service Data Impact Fellows for 2017. Aishah is a second year PhD student with the Faculty of Science and Technology at Bournemouth University. Her PhD research is co-funded by Bournemouth University and County Coaches (UK) LLP (a company of Travelmanagement4u.com). Aishah’s research aims to develop an Intelligent Transportation Analytical Model for SMEs.
My research aims to develop an Intelligent Transportation Analytical Model for SMEs. I am based with County Coaches (UK) LLP, located in Luton, in order to conduct my research in the field. Taking the role of Data Scientist (Research) in the company, my key role involves in initiating and influencing the practice of establishing a data driven decision-making culture within the SME, particularly in the area of Digital Marketing.
I have close to 10 years of working experience with footprints in Singapore, Malaysia, and Indonesia. Prior to starting my PhD research, I had successfully led a team, in launching a micro-transaction payment system in Asia. My industrial background in IT operations, business process, and project management inspired me to pursue a research doctorate in the field of big data analytics. My passion for big data ignited during my Masters’ studies at University Bedfordshire, when I uncovered its immense potential. My academic achievement includes a special mention during my postgraduate graduation ceremony in July 2015 for founding Bedfordshire University’s MBA Global Association under the Vice-Chancellor’s Student Experience Projects (securing an award funding of £5,000 for the association) and my academic excellence. In April 2017, I have been honoured by my alma mater, Bendemeer Secondary School (Singapore) with an Outstanding Alumni Award for my continuous academic journey.
My aspiration is to create a bridge between the research community and industrial practitioners from my PhD research. I believe the close partnership between the academic and industrial partners would heighten innovation in the areas of big data. In my spare time, with the support form my peers; I have founded a DataDenizens.com platform, to gather inter-disciplinary data enthusiast to collaborate on solving data problems for small-scale organisation from various sectors.
Intelligent Transportation Systems (ITS) utilise advanced technologies and systems to provide efficient and safe transportation services while minimising the operational cost and environmental impacts. The ITS evolution has seen a dramatic development in the last two decades – whereby, from the 1970s to 1980s the primary area of` development was concentrated in curbing Traffic Congestion. From 1980s to 1990s, building Intelligent Infrastructure and Vehicles were the core focus of development. With the advancement of technology in the 21st Century, data is increasingly collected every hour, minute and second, causing a data explosion era. The International Data Corporation forecast that the volume of data is expected to grow up to 50 Zettabytes globally by the year 2020. This can revolutionise the development of ITS, by shaping a traditional technology- driven system into a more robust ITS ecosystem. The influx of data can only become an asset to organisations if they are implicitly intelligible to translate useful knowledge for small and medium enterprises (SMEs).
According to the European Commission, the key economic drivers of growth in the European continent are the SMEs – they contributed 3.9 trillion euros to the economy in 2015. This is twice as much in comparison to the large enterprises. The transportation and the storage enterprise made up of 5% of the 22.3 million of the non-financial business economy in 2012. SMEs can further reap two to three times growth rate through the exploitation of advanced technologies (such as social media, big data, cloud computing and mobile). Eurostat identified that less than 7% of the European SMEs have employed data analytics in their business; making a need for digital transformation as a high priority for the EU in this data explosion era. In an in-depth report by IDC on IDC European Vertical Market Survey 2012, it was ascertained that 48500 of the SMEs in the transportation and storage sector have yet to deploy data analytics in their business. The transport and storage SMEs have been relentlessly labelled as laggards in the adoption of Big Data technologies. SMEs can become 5-6% more productive through the utilisation of data analytics in their business – as evident in the larger transport companies. Despite the momentous potential benefits of utilisation of big data analytics, the transport and storage SMEs are still dawdling in their adoption efforts. In 2012, the adoption rate of big data analytics of SMEs in the United Kingdom (UK) stood at 0.2% compared to the large enterprise, with an uptake of 25%. This is indeed an alarming figure, as the fast adoption rate by the large enterprise may eventually implicate SMEs to become irrelevant and absolute. Therefore, there is an urgent need for SMEs to adopt big data analytics. As such, an Intelligent Transportation Analytical Model (ITAM) will be developed with the aim to conduct an intelligent analysis for the SMEs – with the objectives of churning out new insights, showing hidden patterns and relationships within the existing datasets to aid business decision-making.
A quantitative research method using an archival database as the data collection instrument is used in this research. The quantitative method gives an emphasis to objective measurement and the statistical analysis of the data collected. In this research, the subject focus is on coach operators, the company participation criteria are limited only to ground transportation industry in the private coach hire services business. The company participation has been limited within the UK for consistency and ease of end-user participation during the testing stage. The National Travel Survey data will then be integrated along with the acquired SME’s data set. Based on the research identified application gaps found, together with the full understanding of the dataset acquired, the researcher will embark on the model development of ITAM. The model development and pre-testing task will be an iterative process to identify, analyse and test the various machine learning algorithm. Not all learning algorithmss work the same, and the differences have consequences. The final stage of this research will consist of the end user testing and evaluation activities. The novelty of this methodology lies in the engagement of commercial practitioners throughout the research process to ensure the practical implementation of the research outcome. To date, three participating companies in the private coach hire service has been identified. I will use the National Travel Survey data from UK Data Service to integrate the data set with the participating companies’ data set. This will enable to churn out further unique insights of the UK’s residents personal travel behaviour. The usage of open data provided by the UK Data Service will provide an extended understanding of the traveller’s expectation and implication of the private coach hire demands in the UK.
The outcome of this research will be used by the SMEs to facilitate in its organisational strategic decision-making. This research is an inter-disciplinary study of business management and technology. In order to provide a holistic technological solution for the SMEs, it is critical to understand and connect the business elements to the research study. A successful application of the open data would set a new benchmark for other SMEs to start exploring in utilising open data for business advancement and innovation. In addition, this would provide the UKDS, a platform to advocate the use of open data in the commercial industry. The awarded fellowship fund will be used to deliver impactful and engaging public sharing or discourse to engage organisations to capitalise on the usage of open data and analytics – especially so, for the SMEs. At the same time, I am also looking for external public engagement opportunities through commercial networking meet-ups to extend my outreach. Advocating on the use of open data for SMEs is essential as the future architecture is a ‘smart city’ environment and open data is an integral component of it.