Anti-crisis analytics

Anti-Crisis Analytics is a methodology based on data science, statistics, and business analytics aimed to decrease impact of an economic crisis on large and mid-size companies.
Methodology
The anti-crisis analytics includes set of methods to be used before, during, and after a crisis. The methodology uses three stages of business analytics implementation:
# Monitor & Predict
# Optimize & Redesign
# Survey & Simulate
Before the crisis
Before the crisis, business owners and leadership have to take efforts on monitoring the company performance and predicting it. At the Monitor & Predict stage the following methods and technologies are used:
* Data Warehouse
* ETL
* KPI Dashboards
* Predictive analytics
* Data mining
* Process mining
* Process control
* Reporting Services
* Alerting Systems
During the crisis
During the crisis, companies have to work hard on optimizing resource allocations and redesigning business process. At the Optimize & Redesign stage the following methods and technologies are used:
* Project scorecards & ranking
* Portfolio optimization
* Value stream mapping
* Value-added analysis
* Business process analytics
* HR analytics
* Decision support system
* Visual analytics
After the crisis
After the crisis, the management team has to focus on studying new market conditions by conducting surveys and interviewing experts. With a handful of knowledge on the new business environment, the best way to start is to make simulations and test what-if scenarios. At the Survey & Simulate stage the following methods and technologies are used:
* System dynamics
* Agent-based modeling
* Monte Carlo simulations
* Bayesian inference
* Cognitive map analysis
* Structured surveys
* Conjoint analysis
* Data clustering
* Market segmentation
History
The term Anti-crisis analytics emerged from a research on unconscious consumer choices at stock markets conducted by Scandinavian Institute of Business Analytics.

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