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[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] The field of business intelligence (BI), despite rapid technology advances, continues to feature inadequate levels of adoption. The attention of researchers is shifting towards hu-man factors of BI adoption. The wide set of human factors influencing BI adoption con-tains elements of what we call BI culture – an overarching concept covering key managerial issues that come up in BI implementation. Research sources provide different sets of features pertaining to BI culture or related concepts – decision-making culture, analytical culture and others. The goal of this paper is to perform the rview of research and practical sources to examine driving forces of BI – data-driven approaches, BI agility, maturity and acceptance – to point out culture-related issues that support BI adoption and to suggest an emerging set of factors influencing BI culture.

The emergence of the notion of Big Data has created substantial value expectations for organizations with huge collections of data. Banks, as all the other organizations, recognize the value potential contained in big data. For financial services companies with mostly information circulating in their value chain, data—the source of information—is one of arguably their most important assets. The current issue is to which extent these data assets may be leveraged to produce value and gain competitive advantage. The goal of this chapter is to discuss issues, challenges and important dimensions in the activities of banks and other financial institutions that need to be understood to produce a framework for efficient utilization of the potential of Big Data approaches. The authors argue that, although the banks have much in common with other businesses regarding customer and operations management, a priority feature of analytics in banking is risk management. No less important than technology issues are managerial implications, concentrating on issues like acceptance of Big Data approaches and intelligence culture.

Darbo tikslas yra įvertinti įvairias investavimo strategijas pagal jų pelningumą realiose ir virtualiose fi nansų rinkose. Darbe aprašytas atnaujintas fi nansų rinkos modelis, analizuojami eksperimentinių skaičiavimų rezultatai. On the experimental investigation of investment strategies in the real and virtual fi nancial marketsJonas Mockus, Igor Katin, Joana Katina SummaryThe optimal fi nancial investment (Portfolio) problem was investigated by leading fi nancial organizations and scientists. The aim of these works was to defi ne the optimal diversifi cation of the assets depending on the acceptable risk level.The aim of the paper is to evaluate different investment strategies in the real and virtual fi nancial markets. This aim is the new element of the proposed simulation system since optimization is performed in the space of investment strategies; both daily and long-term. A number of different investment strategies are presented, including the ones based on the Modern Portfolio Theory (MPT).The simulated investment procedures include different prediction methods. The methods that minimize the mean absolute error (MAE) are added to the traditional ones that minimize the least squares error (MSE). The results of the virtual fi nancial market are compared with historical data.The model is designed as a tool to represent the behavior of an individual investor which wants to predict how the expected profi t depends on different investment strategies using different forecasting methods of real and virtual stocks.

A simple Stock Exchange Game Model (SEGM) was introduced in (Mockus, 2003) to simulate the behaviour of several stockholders using fixed buying-selling margins at fixed bank yield. In (Mockus, 2010, 2012; Mockus and Raudys, 2010), the theoretical description of the updated model USEGM was presented and illustrated by the results of experimental investigation obtained by the software developed for the early version SEGM. The new elements of the model is the evaluation of the transaction costs and addition of the minimizer of absolute residuals representing the risk-neutral users. Experimental investigation of the updated USEGM model produced new results. In this paper, we discuss them. The results were compared with real data. They show that the traditional estimators of the minimal prediction errors, such as MSE or MAE, do not necessarily provide maximal average profits. However, contrary to the traditional stock rate prediction models, the main objective of USEGM is not forecasting, but simulation of financial time series that are affected by predictions of the participants.