## 3 Business Intelligence and Analytics in Facility Management  
Facility managers perform many activities to organize interaction between stakeholders, management activities, and works. Depending on information and communication systems, massive data are produced in daily operations. These data resources provide an opportunity to increase or improve performance of FM activities [24]. However, data (raw numbers and symbols), which need to be converted into knowledge to take action in the building environment, cannot be used in FM because of some hindrance factors [40]. For example, [PERSON] et al. [25] stated that the managers, who are responsible for energy management, have a lack of energy background. To overcome the lack of background, BI&A and their simple use with user interfaces gain more importance to derive more profound knowledge and decisions from data sources [25]. Business intelligence (BI) enables the information-oriented decision-making process by transforming raw data into useful information with applications of \"a set of methods, processes, architectures, applications, and technologies\" [4,41]. Recent BI depends on data analytic activities. Therefore, it was combined with data analytic, and it has been called business intelligence and analytics (BI&A) [42]. Data analytic is an umbrella term to refer to different analytics and analysis [43]. Therefore, the data analytic component of BI consists of: (i) descriptive analysis; (ii) operational queries; (iii) multidimensional analysis such as online analytical processing (OLAP); (iv) statistical analysis; (v) data mining techniques including predictive modeling; (vi) visual analytics such as scorecards and dashboards; (vii) text mining such as natural language process; (viii) BDA; (ix) network analytics such as link mining between stakeholders; (x) web mining; (xi) sentiment analysis (revealing feelings); and (xii) social media analytics [27,30,31,32]. Some of the components have an intersection with each other. As a limitation, BI&A data mining techniques, visual analytics, BDA, and text mining, which are frequently used in BI&A, were investigated within the context of this study [41,44,45]. * Data Mining: Data mining activities are performed to produce useful information from obtained data sources such as databases [46]. Data mining (DM) in the FM industry is essential to generate actionable information in the facility environment [47]. [PERSON] et al. [3] and [PERSON] et al. [15] stated that data mining activities can be classified under supervised learning and unsupervised learning. While supervised learning aims to produce predictions by using data, unsupervised learning is used in group identification and to discover new knowledge from historical data. [PERSON] et al. [46] and [PERSON] et al. [43] mentioned five commonly used DM categories; \"anomaly detection/outlier detection, association rule learning, clustering analysis, classification analysis, and regression analysis\". In anomaly detection, the outliers are identified by comparing expected patterns. In association rule learning, the relationship between variables is discovered within the large database. In clustering analysis, similar objects are grouped under the same group. In the classification group, observations are categorized by considering trained models. In regression analysis, the relationship between variables is discovered. However, the size of variables and data volume is, respectively, very small against association rule learning. In the DM activities, the DM models are used to reveal the mathematical relationship between observed factors. These DM models are trained with historical data and predictions for similar problems are revealed [47]. Historical data also help to improve and optimize operations in the facility. * Visual Analytics: Visual analytics such as histograms can be used in data exploration [48]. Visual analytics combine analysis techniques with visualization tools.