## 2 Methodology and Data Selection  
Composite indicators can assist decision-making and monitoring processes, due to their ability to obtain a simplified concept by aggregating several dimensions of a single process. These indicators need to be robust, since they rely on statistical parameters. In this paper, we follow the methodology that was proposed by the Competence Center on Composite Indicators and Scoreboards (JRC-COIN) [24], coordinated by the Joint Research Center (JRC) from the European Commission. The JRC is a service that provides independent scientific evidence in order to support the European Commission policies. The JRC-COIN and the Organization for Economic Co-operation and Development (OECD) published a Handbook [25], with the aim of guiding the construction and use of composite indicators. Basically, the Handbook recommends following an iterative process of 10 steps, which are summarized in the following. **Step 1: Theoretical framework. **: It establishes the basis for the selection and combination of variables. It is important to develop a clear definition of the multidimensional process to be measured, as well as the added-value of the composite indicator. **Step 2: Data selection. **: This step covers the analytical correctness, the coverage, relevance and how quantifiable are the indicators with respect to the process under measurement. **Step 3: Multivariate analysis. **: It checks the structure and coherence of the data set and supports the choice of method for data aggregation. This analysis is usually done by applying techniques, such as principal components analysis, Cronbach coefficient alpha, and cluster analysis. **Step 4: Imputation of missing data. **: Missing values can distort the outcome of the composite indicator. Methods that are used to deal with this issue are data deletion, mean substitution, regression, multiple imputation, nearest neighbor, or ignore them, among others. **Step 5: Normalization. **: Normalization is the step that ensures that indicators are using a common basis. The data properties and the objective of the composite indicator should be carefully considered. Some methods apply truncation of extreme values or functional transformations before applying the actual normalization technique. Ranking, standardization, min-max, methods that are based on distance, categorical scale, indicators above or below the mean, or cyclical indicators are some of the methods that are used at this step. Step 6: Weighting. There is no standard methodology for weighting the individual indicators before aggregating them in order to obtain the composite indicator. Some of the methods that are commonly used are the equal weights option or methods based on statistical models (principal components analysis, data envelopment, regression, or unobserved components models) or based on expert opinion (budget allocation, public opinion, analytic hierarchy process, or conjoint analysis). Step 7: Aggregating indicators. In the aggregation step, the most applied techniques are based on additive methods. Other aggregation methods can be applied, such as geometric aggregations or non compensatory multi-criteria analysis. Step 8: Sensitivity analysis. This step tries to increase the transparency of the composite indicator. The sensitivity analysis measures the dependency of the composite indicator with respect to the available information, and it is closely related to uncertainty analysis. Step 9: Link to other measures. In this step, the correlation with other indicators or indexes is measured. Step 10: Visualization. The final step consists of presenting the results of the composite indicator in such a way that clear messages are delivered for decision-making processes. In our case, basic variables will be KPIs, which are usually derived aiming at a given network optimization target that is defined by MNO. Several inputs are available to build KPIs, such as cumulative counters, constant values, time counters, percentages, or even previously stored KPIs.