When creating and administering regulations, regulators have to demonstrate that regulations accomplish intended societal outcomes at costs that do not outweigh their benefits. While regulators have this responsibility as custodians of the regulatory ecosystem, they are also required to create and administer regulations transparently and impartially, addressing the needs and concerns of all stakeholders involved. This is in addition to regulators having to deal with various administrative bottlenecks, competing internal priorities, as well as financial and human resource limitations. Nonetheless, governments, regulated parties, citizens and interest groups can each express different views on the relevance and performance of a piece of regulation. These views range from too many regulations burdening business operations to perceptions that crises in society are the results of insufficient regulations. As such, regulators have to be innovative, employing methods that show that regulations are effective, and justify the introduction, evolution or repeal of regulations. The regulatory process has been the topic of various studies with several such studies exploring the use of information systems at the software level to confirm compliance with regulations and evaluate issues related to non-compliance. The rationale is that if information systems can improve operational functions in organizations, they can also help measure compliance. However, the research focus has been on enabling regulated parties to comply with regulations rather than on enabling regulators to assess or enforce compliance or show that regulations are effective. Regulators need to address concerns of too much regulations or too little regulations with data-driven evidence especially in this age of big data and artificial intelligence enhanced tools. A method that facilitates evidencebased decision-making using data for enacting, implementing and reviewing regulations is now inevitable. In response to the above challenges, this thesis explores the use of a goaloriented modelling method and a data analytics software, to create a method that enables monitoring, assessing and reporting on the effectiveness of regulations and regulatory initiatives. This Goal-oriented Regulatory Intelligence Method (GoRIM) provides an intelligent approach to regulatory management, as well as a feedback loop in the use of data from and within the regulatory ecosystem to create and administer regulations. To demonstrate its applicability, GoRIM was applied to three case studies involving regulators in three different real regulatory scenarios, and its feasibility and utility were evaluated. The results indicate that regulators found GoRIM promising in enabling them to show, with evidence, whether their regulations are effective.