bigRing Augmented Analytics

Make better decisions with augmented intelligence

bigRing Augmented Analytics module allows your organization to cover the Analytics Continuum. Business intelligence and advanced analytics capabilities enabled by AI-assisted data exploration, insight generation and explainability of Knowledge Elements, including their causality (cause & effect) relationships, metrics, measurable properties, and transfers. Expert users and business analysts access the corporate knowledge and intelligence at their fingertips, allowing Descriptive, Predictive and Prescriptive Analytics functionalities to support their knowledge work, informed decision-making, implement optimization and achieve operational excellence.

bigRing Augmented Analytics

Descriptive Analytics

(Under development)

Big Data Analytics and Business Intelligence functionalities, that provide the answers to “What happened and why?” questions

Supported for highly interactive dashboards, the exploration of data through the manipulation of chart images. Included: visualization options beyond pie, bar and line charts, such as heat and tree maps, geographic maps, scatter plots and other special-purpose visuals
AI-assisted insight generation and explainability based on your corporate Knowledge Deep Graph allowing corporate analysts and business users to explore and analyze enterprise data
Visual analysis of operational data in compliance with designed business processes in real-time and understanding of how processes and outcomes comply with expected targets or optimal values

Predictive Analytics

(Under development)

Data Science with Prediction, Forecasting and Simulation functionalities, answers to “What will happen?” questions

Predictive Analytics utilizes bigRing Causality Model with captured corporate knowledge, in combination with bigRing ML techniques to identify the likelihood of future outcomes based on historical data (experience)
Simulation can be performed using the causality modeling based on simple classification models, more complex decision trees or sophisticated multi-layer knowledge modeling with complex causal relationships among the elements
Causal forecasting and modeling can provide predictions that represent the probability of the forecasted variables such as revenues or costs based on estimated significance from a set of input parameters (Knowledge Elements)
Regression modeling and analysis can be used to predict numerical values based on the estimates of relationships among the elements or to determine how much specific factor, such as price, can influence the movement of financial asset
What-If Analysis can perform AI-assisted simulations with any driver defined and future values changed such as price change, promotion discounts, marketing spending budget, macroeconomic indicators, or even weather forecasts

Predictive Analytics

(Under development)

Decision support and optimization functionalities, that provide the answers to “What will happen and what should be done?” questions

Prescriptive Analytics empowers organizations with suggestions and recommendations of optimal actions to achieve the desired business objectives
Decision-making systems supports and improves decision-making processes in context, organize approval, simulate impact, take action, run workflows and track the corresponding progress
Rule-based systems use optimization functionalities to solve complex decisions with hundreds of decision variables, constraints and tradeoffs
Protect and automate processes with a new kind of decision-making agreement and management components for business or project controlling
Reuse knowledge and create new standards of best practices for next iteration and optimization round
Use heuristic methods to speed up the process of finding a satisfactory solution, if and where finding an optimal solution is impractical or impossible