## 2 Practical Issues Related to Smart Decision Support  
A system for evidence-based decision support in industry should be able to transfer the information from the manufacturing process to the end users effectively and effortlessly. This includes the handling of process data streams, the integration of prediction models and analytics to the system, and easy access to resulted decision support by end users. In a quality monitoring tool for the steel industry, the system architecture contains four levels: data acquisition, data storage, information analysis, and information delivery [10]. The system enables timely access to the needed data sources, data preprocessing and prediction model integration, and model analytics-based decision support with a web-based user interface.  
Data collection from industry, as well as the data analysis itself, are demanding tasks, because the amount of data can be extremely large and they can come from many sources and in different formats. Main challenges with big data are the volume, velocity, variety, and veracity, which are related to the amount of data, the speed of the data coming in and out, the range of data types and sources, and the uncertainty of data [16]. Lately, the value has also been considered as a challenge; the data may be a considerable asset to an organization, but only if value can be derived [17]. In some cases, there are no data available at all, or it is difficult to measure the property due to the challenging industrial conditions. For example, in the hot rolling mill, the conditions are extreme due to the heat, surface scaling, water, dampness, and the speed of the rolling process. In addition, measuring can be very expensive or time consuming. As a result, the relevant data sets can be too small and the data analysis methods invaluable. However, it is sometimes possible to derive new more informative variables from the measurements as well.  
Reliable data are a necessity for successful modeling tasks. The accuracy of the model is dependent on the reliability of the training data, and it is possible to improve the data quality with careful preprocessing. Typically, the main data preprocessing tasks are data cleaning, such as eliminating the noise and handling the inconsistent data, data integration from different sources, data transformation, and consolidation into a suitable form for data mining methods and data reduction, including the selection and extraction of features [18]. Missing data and data imputation are also essential topics in data preprocessing.  
When prediction model-based tools are applied in the industrial environment, it has to be taken into account that the real-life process data during the operation need to be preprocessed similarly as in the training phase. Otherwise, the decision support that the tool offers is based on unreliable information that may be inherently incorrect. In practice, the full automation of the preprocessing may be impossible, but it should not be neglected either.  
Co-operation between the domain experts and data analysis and modeling experts is essential in applied research. Domain experts give the need for the research, understand the industrial process, and select the data as well. Data mining experts, in turn, excel in data analysis and machine learning methods. An iterative data mining process enables the interaction between the experts during the tool development. As a result, the actual implementation process of the tool is less complicated, and the users are more motivated to use it.