# A Robust Statistical Methodology for Measuring Enterprise Agility
## 3 Methodology
### Measuring Enterprise Agility
It encapsulates different perspectives, from the detailed project level to an aggregated client, business vertical, or country level, then extending back up to a regional or global level within TCS. The horizontal scalability of this tool is demonstrated as it is not confined to IT services, but also pertains to all service units TCS offers its clients, such as Cognitive Business Operations (CBO) ([[https://www.tcs.com/what-we-do/services/cognitive-business-operations](https://www.tcs.com/what-we-do/services/cognitive-business-operations)]([https://www.tcs.com/what-we-do/services/cognitive-business-operations](https://www.tcs.com/what-we-do/services/cognitive-business-operations)), accessed on 25 March 2023), Consulting, and Enterprise Solutions, among others. In recent times, the AgilityDebt(tm) index has emerged as an effective governance tool for C-level executives tasked with spearheading organizational change in large and complex entities. The AgilityDebt(tm) index can be conveniently customized to align with our client's organizations. It is calculated through a meticulously structured polynomial, contingent on its factors, and informed by variables that are constantly monitored through data input into TCS's information systems. To illustrate how AgilityDebt(tm) is measured within the IT realm, we can look at the following contributing factors:  
* The extent of agile training and competency levels among staff;
* The usage of agile practitioners;
* The agile maturity of projects/services;
* Customer satisfaction;
* The business outcomes generated by our delivery. In the CBO world, as a guiding example, we can mention the following factors to execute the AgilityDebt(tm) measurement:  
* People's training in agile and their levels of competence;
* The agile maturity of projects/services;
* Operational performance;
* Customer satisfaction. As an example, Figure 1 shows a dashboard with AgilityDebt(tm) values by country and global Latam, including a view of the performance of each factor in the polynomial equation. This dashboard is evaluated weekly and monthly with the leaders and C-Levels of TCS Latam. Proposed Methodology for Demonstrating the Statistical Significance and Power of the Improvement in AgilityDebt(tm)  
We used statistical analysis to objectively demonstrate that the change or improvement exhibited in the AgilityDebt(tm) after the actions implemented by the transformation team corresponds to a statistically significant finding and is not something that results from the randomness of the data and its sampling error [34]. For this purpose, the method employed to carry out the data analysis consisted of the following steps:  
1. Dataset capture (pre and post): Samples are extracted and their statistics are calculated (mean, standard deviation, variance, and size), along with graphs to improve data interpretation [34]. 2. Normality test of the samples: A normality test is performed on both samples with the aim of ensuring that the normality assumption is met [35], and to perform future parametric tests of difference of variances and tests of difference of means. 3. [PERSON]'s \(F\)-test for variance difference: If normality exists in step number 2, this test is performed to: (a) Evaluate if there are statistically significant differences in the variability of the samples [36; 37]. 1. Determine if the future test of difference of means to be executed in step number 4 will assume equality of variances or not. 2. Student's \(t\)-test for mean difference: If normality exists in step number 2, this test is performed to determine if there are statistically significant differences in the means of the samples [38]. 3. Power test: If there are statistically significant differences in both the variance difference test and the mean difference test, a power test is performed to evaluate the size of the effect [39; 40] obtained and the probability that the effect is true. 4.