Operational intelligence

Abstract: The transformation of intelligence architectures, particularly in the West, is no less profound than that of the weapons, platforms, warfighting systems and governments they are designed to support and inform. Moreover, the cumulative weight of the changes in prospect will redefine the way in which intelligence is used and conceived. The old demarcation lines between strategic and operational intelligence and between operations and intelligence, once starkly differentiated will blur. Decision-makers will have better access to intelligence as a result of advances in ‘pull’ technology which have made possible intelligence on demand while open source intelligence will enrich and add value to national intelligence databases. Although information will become more plentiful and less of a privileged source in the global information environment of the twenty-first century, paradoxically the demand for timely, high quality strategic and operational intelligence will intensify rather than diminish. What will distin...

Abstract: An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today’s IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects. Monitoring and managing high-load data projects require new approaches to infrastructure, risk management, and data-driven decision support. Key difficulties that might arise when performing IT Operations are high error rates, unplanned downtimes, poor infrastructure KPIs and metrics. The methods used in the study include machine learning models, data preprocessing, missing data imputation, SRE (site reliability engineering) indicators computation, quantitative research, and a qualitative study of data project demands. A requirements analysis for the implementation of an Operational Intelligence solution with Machine learning capabilities has been conducted and represented in the study. A model based on machine learning algorithms for transaction status code and output predictions, in order to execute system load testing, risks identification and, to avoid downtimes, is developed. Metrics and indicators for determining infrastructure load are given in the paper to obtain Operational intelligence and Site reliability insights. It turned out that data mining among the set of Operational Big Data simplifies the task of getting an understanding of what is happening with requests within the data acquisition pipeline and helps identify errors before a user faces them. Transaction tracing in a distributed environment has been enhanced using machine learning and mathematical modelling. Additionally, a step-by-step algorithm for applying the application monitoring solution in a data-based project, especially when it is dealing with Big Data is described and proposed within the study.