## 3 Business Intelligence and Analytics in Facility Management
Therefore, the understanding and reasoning processes of decision-makers are easy and effective with the utilization of visual analytics [49]. Visual analytics contain informative answers for \"what is happening\", \"what happened\" and \"what will happen\" [45]. [PERSON] et al. [46] identified nine visualization techniques from literature; \"1D to 3D graphics, geometric techniques, pixel oriented techniques, iconographic techniques, topic-based methods, feature-based methods, graph layout techniques,clutter reduction methods, and combinations\". The authors expressed that the visualization techniques allow users to explore data more effectively and efficiently than DM results, which are very complex to infer information from for facility managers. * Text Mining: Text mining activities are frequently used on maintenance records because maintenance-related tasks are heavily based on text-based inspection reports, maintenance reports, and historical data [49]. The most significant difference of text mining activities from data mining activities is that unstructured data (text, speeches, etc.) are handled in text mining activities. Afterward, the text is converted into numeric attributes that enable the processing of text in data mining activities [13]. In the second step, the clustering activity is performed. Therefore, FM components, which are investigated in data mining, are identified. In the last step, associated words are selected [10]. After that, a more detailed analysis can be performed on retrieved data [50]. Naive Bayes, decision tree, logistic regression, and support vector machine can be used in text mining [50]. * Big Data Analytics: Big data analytics (BDA) is another BI&A term that enables one to analyze variability and its root causes from datasets [11]. With the application of IoT and smart meter devices in building environments, data obtained from these real-time data sources increase the necessity for the BDA applications [51]. BDA is used if the data have features like enormous volume, variety, and velocity. In other words, when datasets are difficult to manage with existing technologies, the creation, organization, analysis, and visualization of a dataset is called big data [52]. The success of BDA comes from the capability of processing structured, unstructured, and semi-structured data [53]. FM is one of the BDA areas in which BDA helps to analyze data obtained from FM systems to render possible effective management [11]. BDA is a more comprehensive approach. It involves \"Databases, Knowledge Discovery in Databases, Data Mining\" and \"text mining\". It also has an intersection with \"Statistics, Pattern Recognition, Machine Learning, Artificial Intelligence, and Computational Neuroscience\" [54; 55]. The use of BDA in the smart energy meter enables identification of energy-saving and potential savings, estimation of costs, and pattern recognition [56]. Identification of Information Requirements of FM Key Performance Indicators (KPIs)While the literature review was performed to discover FM KPIs, information requirements for the calculation and analysis of FM KPIs were revealed with the literature review. The findings showed that while data analytics are performed on data sources, many information systems are used and integrated to discover hidden values and performance metrics. Briefly, building information modeling (BIM), building energy performance simulations, camera systems, FM systems, healthcare patient database, account system, human resources database, inventory database, mobile devices, document systems, RFID systems, Wi-Fi-enabled devices, and regulations are required information resources to perform BI&A in FM KPIs. The studies related to BIM showed that BIM can be used as a host system for many data sources.