Analytics

 

We help business capturing all the value from their and our data
and transform it into a sustainable competitive advantage

Analytics is the discovery, interpretation, and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modelling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modelling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (Big Data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Descriptive & Diagnostic Analytics

Visualize and analyse your data for better understanding and decision making

Hindsight: “What happened?” Oversight: “What is happening?”

• GIS
• Databases
• Dashboards
• Business intelligence
• Web data extraction
• Data cleansing
• Problem structuring
• Data and model visualization
• Descriptive statistical analysis
• Data exploration and pattern detection

 

Predictive & Inquisitive Analytics

Learning: “Why does it happen?” Foresight: “What will happen?”

Diagnose: ‘Why did something happened at a certain moment in the past ?”. Use a variety of statistical, modelling, data mining, and machine learning techniques to study recent and historical data in order to make predictions about the future.

• Inferential statistical analysis (controlled experiments, non-parametric techniques)
• Data mining & Machine learning (classification, regression, factor analysis, clustering, deep learning, reinforcement learning)
• Forecasting and Prediction
• Simulation, Risk, Uncertainty, and Scenario Planning

 

 

 

Prescriptive & Optimization Analytics

Insight: “How should it happen?”

• Optimization & planning (heuristics, mathematical programming, constraint satisfaction, solvers)
• Multicriteria evaluation
• Portfolio optimization & resource allocation
• Project planning and resource scheduling
• Behavioral sciences & nudging
• Negotiation, conflict, and game theory

 

 

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