## 5 Discussion  
The DDA model complements rigid, traditional CFKPIs by validating data for correlations and adequacy; creating decision engines; enabling predictive analytics; discovering hidden data relationships, trends, and patterns; prioritizing CFKPIs; and consequently, creating SKPIs.  
Discovering the properties of the CFKPIs enabled efficient troubleshooting through the adoption of the 5 WIMPs root cause analysis technique. The root causes for performance predicaments were able to be traced from enterprise functions to business functions, processes, process steps, and resources. This enabled the mine to locate and optimize the components of the CFKPIs that caused underperformance. The mine was also able to determine the components of the CFKPIs that underpinned good performances and to pinpoint those components that required optimization. The properties were also used for predictive analytics by monitoring the performance of the CFKPIs' components and subsequently predicting the future outcome. This allowed the mine to act swiftly and optimize or replace the problematic components to avoid underperformance.  
Creating fewer groups of CFKPIs reduced data complexity by revealing which CFKPIs were correlated and the nature of their correlations. The application of the ID and CLD techniques provided clarity on how the CFKPIs affected each other and how their correlations and interdependencies contributed to the performance of the business strategy.  
Determining the significance level of each CFKPI to performance management helped the mine prioritize and pay appropriate attention to the right areas of the business. The conversion of CFKPI groups into SKPIs enabled each group to be treated and managed as a smart KPI that uses its variables and properties to manage performance. The association of enterprise functions to SKPIs provided deeper insights into the correlation between the dominant enterprise functions and respective variables. This ensured that the dominant enterprise functions shared common goals with the collaborating variables of SKPIs. The evolutionary nature of SKPIs enables the model to produce new SKPIs as business evolves and data change. This evolution of SKPI dynamically reveals the most influential variables and key business drivers. This would allow the business to align decisions to the actual, as opposed to supposed, business needs.  
The performance of the model does not rely on human influence to discover the most influential variables and key drivers of the business; the model is purely data-centric. The  
Figure 15: Evolution of SKPIs from the year 2015 to 2017.  
elimination of human influence jettisons the dependency on individuals' experience and preferences in defining, prioritizing, and managing KPIs, which is the main purpose of this study. The creation of SKPIs prompts for a closer look at the main influencers of business performance. This study observed that logistic activities were pivotal during the start-up of the business. Financial management activities played a big role as more resources were acquired and utilized by the mine. Lastly, production operations contributed significantly to realizing efficiency, reduced cycle times, and enhanced customer satisfaction when operations had matured and resources were utilized effectively.