Advanced Operational Analytics
Advanced Operational Analytics represent the next significant lever for achieving data-inspired outcomes in Big Data environments. Through a combination of technologies and tools such as machine learning, natural language processing and process mining, organizations now have the ability to operationalize at scale highly sophisticated analytical models that continue learning over time. Traditionally, descriptive and diagnostic analytics could provide structured visibility; however, their heavy reliance on manual interpretation and execution often means that systematic issues remain undetected or unresolved. By contrast, the output from advanced analytics is a predictive and prescriptive engine that accelerates the operationalization of outcome-driving insights.
Foundational Operational Analytics
Foundational Operational Analytics are aimed at communicating what has happened and why. Even though relying purely on descriptive and diagnostic analytics can lead to a highly reactive organization, the reason why they are considered foundational is that more advanced analytics will be built on top of the former, not in isolation. As a result, the most relevant lever here is not technology, but business domain expertise. Understanding the business objectives and workflows will enable a more effective calculation and representation of data, for example by choosing the relevant units of measurement (i.e., counts, quantities, dollars), the measurement context (i.e., absolute values or percentages), or the level of aggregation (i.e., categorical hierarchies and relevant time periods). Additionally, these analytics should serve as a way to validate data completeness and correct interpretation, before those same inputs are also consumed by the advanced analytical models.