ADAPTIVE data analytics - process algorithms for engineers

When analysing and reporting on data, professionals can be faced with the challenging task of choosing what to use in analysing site-specific data, situation-specific data, and time-specific data they have not encountered before. Encountering new data, unique data, addressing rapidly emerging and morphing situations, assessing, and quickly adapting to new developments, timely dealing with emergencies and meeting tight deadlines may not afford the time needed, the opportunity to set up. configure, deploy, or customize mainstream products such as SAS. SPSS. Tableau. TM1 or other such analytical tools. Time, circumstance, opportunity, and cost may limit and even preclude the use of these tools. Many condition monitoring and engineering management professionals have a vital requirement to analyse very large datasets from which to draw insights and make decisions, many times under severe time constraints and pressures, and thus require a capability to do so without having to first gain experience in complex BI tools before even getting to the analytics and interpretation of data. The growing ICT skill shortages that have plagued the economy for years is likely to get worse over time. To minimize on the draw-down on scarce and sometimes difficult to access ICT resources, this paper discusses how an Adaptive Analytics Model can mitigate such draw-down on scarce ICT resources and lead to ICT independent self-serving analysis and reporting. Since 2013-2020. the author has been actively engaged by Universities. ASX listed companies, the Australian Federal Government. State Government, Australian Government Agencies, private companies, businesses, and individuals. These engagements shared one widespread problem: Reporting and Analytical requirements which could not be delivered by timely and economical deployment of ICT resources. Those insights and learnings gained during that time have been used in guiding this research.