MATURITY LEVEL MEASUREMENT OF IMPLEMENTATION AND CAPABILITY OF INFORMATION SYSTEMS BASED ON BIG DATA TECHNOLOGY ON GOVERNMENT STAKEHOLDERS OF INDONESIAN CONSTRUCTION INDUSTRY

• Main Author: Jonathan
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/68344
• Institution: Institut Teknologi Bandung

Big data has a crucial role in industry 4.0 which is prompted through the datafication phenomenon. Big data has been implemented in various fields such as logistics, retail, transportation, agriculture, and manufacturing, that can be seen through literature publications. However, the publication of literature related to the implementation of big data in the construction industry is still very limited, where most of the research conducted is still focused on the potential of big data, not about the implementation. Nevertheless, there are at least two government stakeholders of the Indonesian construction industry, namely BPS and DJBK who have made claims through articles or news that they have implemented big data in form of information systems. Considering that big data does not arise from the academic domain, it is necessary to measure capabilities related to the implementation of big data to avoid misconceptions about the use of the term big data. In addition, the number of critical success factors that determine the success of big data implementation encourages the need for research on measuring the maturity level of big data implementation which not only aims to provide information about the current existing conditions, but also information about what to do on various specific aspects in order to reach higher level of maturity. This research begins by identifying several validated maturity models, especially in the big data scope. It was found that each maturity model has different methods and aspects of focus. For this reason, it is necessary to select a maturity model using parameters and weighting factors obtained from other validated studies. The maturity model used in this study is the Hortonworks model. Then, the questions for data collection were developed based on the Hortonworks model into Indonesian language to measure the level of maturity, and also based on the big data 3 V's characteristics to measure capability. After that, a pilot survey was carried out regarding the questions that had been developed to ensure that there was no confusion when data collection process is conducted. In this descriptive and qualitative research, respondents from each BPS and DJBK will be inquired to participate in semi-structured interviews. Finally, validation will be carried out regarding the results of the maturity level to each corresponding respondent to ensure the interpretation reliability of the results. The results obtained are that the implementation of information systems at BPS and DJBK cannot be called big data for now based on the requirements of the 3 V's characteristics. This is because the data collected by both are not the same as the population and all the data is structured. In addition, both of them collect data only in a single format so that it cannot be said to meet the characteristics of variety. Associated with the results of the level of maturity, both can be said to be quite mature because they already at the level of exploring towards optimizing. However, both are different from aspects that must be improved, where BPS should focus on improving data management aspect as well as aspects of infrastructure and technology, while DJBK should improve on the aspects of sponsorship. Research on the measurement of big data implementation capabilities for other industries including construction industry has not been carried out in Indonesia, and research on maturity levels has only been found in one study in Indonesia, which using different maturity model and also not specific to the construction industry. Research related to measuring capabilities and maturity levels is very important, especially in prompting comprehensive information disclosure to other stakeholders such as academia and business, so that it can encourage broad adoption of big data and create sustainable innovation from big data implementation, especially in the Indonesian construction industry.