Throughout history, as well as in modern times, the energy sector represented a key factor for accelerating economic growth and achieving sustainable development. Nowadays, the energy industry constantly needs to seek new ways to achieve higher levels of energy efficiency in order to answer the ever-increasing demands of consumers, businesses and governments. The key factor in achieving optimum energy efficiency is surely the development and adoption of smart grid technologies [1]. Smart grid technologies bring many innovations to the electric power industry, as well as changes to market structure, business models and services. As operators strive towards implementation of smart grids and accompanying technologies, they are faced with various problems concerning the ever-increasing consumer needs [2]. In order to succeed and thrive in this constantly changing business environment, electricity market operators must constantly seek to expand their access to operational data, and more importantly, improve their ability to convert the huge amounts of data into intelligence relevant to the operation of the grid [3]. In turn, the adoption of smart grid technologies must lead to consequent changes in companies’ information systems. The dynamic nature of the energy business would serve as the perfect grounds for implementing analytical systems capable of meeting these requirements [4]. Business intelligence (hereinafter: BI) and knowledge management infrastructures have existed in business environments for many years, and their importance is an established fact. The necessity for such infrastructures in large energy systems has been recognized, as well. BI in smart grids is considered to be one of the essential mechanisms of maximizing the “smartness” of the grid. A business intelligence model suited for the needs of a smart grid must offer a way to generate immediate business value from the new disparate data sources, including modern metering and supervisory data. The focus on the utilization of newly acquired data implies that the grid and market operators that are still in the process of smart grid adoption could gain the most from the implementation of a BI solution. This provides an opportunity to influence the future development of the metering infrastructure, allowing the grid to evolve into an information-rich environment where any decision could be based on actionable intelligence [5]. On the other hand, the majority of electricity markets in the developing countries still do not operate in the smart grid ecosystem. In order to adapt to the expected changes, it is necessary to design the current projects in such a way that they can be easily adapted to future smart grid expectations. Taking this into account, this research aims at proposing a bottom-up approach for developing BI solutions that support future developments of the deregulated electricity markets in the developing countries. Energy markets in this new environment will need to adapt to the newfound flexibility in energy demand, as well as to the consumers, who will become market participants and take an active role in energy generation [6]. Under these conditions, the energy systems still need to remain stable, in the sense that energy demand must be equal to its supply. Stable energy systems require adequate management of energy supply, and to some extent, of the demand, as well, in order to meet the optimum operational plans. For this purpose, itis necessary to develop and use analytical and BI applications in the markets. However, the literature does not offer much information about methodologies and best practices for designing BI solutions that incorporate all the specifics of rapidly evolving energy markets.