A digital twin is a virtual model of a real-world object or space. Within the built environment, a digital twin is a digital or a web-based representation of a physical building, system, or sequence, scaling all the way up to a city or utility grid. A digital twin can represent everything within a building and can offer realtime data about how those assets are performing. In a context of energy communities (ECs) in city environment, the digital twin can be used to sustain community activity, accelerate public engagement in the energy transition, improve energy self-consumption, reduce carbon emissions (CO2) and perform optimally coordinated decision-making. The process of creating a digital twin can be divided into three stages. The first stage is data acquisition and processing, the second stage is data analysis and modelling, and the third stage is data visualization. At the core of any digital twin is the existence of data. The amount of data depends on many factors, such as the purpose of the digital twin, the desired accuracy of the correlation between the digital twin and the real object, the number of sensors, the availability of historical data, etc. Sensors, smart meters, external databases like historical weather data, electricity and gas market data, as well as various programs for data modelling can be used as data sources. As sensors become more accessible, opportunities to collect processes at the community level and pass this information on to a data platform are increasing. The sensors provide the community with both historical and realtime data. New data is added to historical data to continuously generate new information and adapt to changing realities. Often a key requirement for sensors is the ability to install and maintain them in properties that are already built and occupied. Historical data is needed to train machine learning algorithms to make future predictions and identify the parameters that most influence community performance. Also, historical data can be used to predict community disruptions that are likely to occur in the future. Before creating a digital twin, it is necessary to answer the following data acquisition and processing management related questions: who owns the data and who owns the digital twin itself: is it owned by the developer or by the EC; what is the role and main responsibilities of each involved party in the development and operation of the digital twin; who is responsible for data recovery, content quality, data storage and sharing; what are the possible risks of data acquisition, accumulation, analysis and use; who is responsible for the consequences caused by data errors, damage or incompleteness.