We use a wide range of analytics technologies and techniques to help companies solve business challenges across functional areas, applying more than a decade of domain business expertise in Financial Services, Insurance, CPG ,Retail and Technology, as well as new sectors supporting healthcare and government.
|Technologies & Techniques||Marketing ||Risk Management||Supply Chain, Channels||Visual Story Telling|
|Reporting, Dashboards & Visualization|
|Descriptive Modeling|| |
|Predictive Modeling|| |
- To cleanse and structure the data: Involves evaluation of Big Data and harmonization across data sets for integrity and accuracy. Includes transformation, parsing and summarization of standard elements to integrate data from disparate sources. Includes database structure requirements, parameter loading into database, documentation, data quality checks and exploratory data analysis.
- Tools Used: SAS, SPSS, R, RStat
- Databases : SQL Server, ORACLE, DB/2
Reporting, Dashboards & Visualization
- For business insights: To enable executives and business users to understand the state of the business. User interface is fully customized to facilitate interactive navigation of Big Data across the enterprise by function, business unit, region, and any other segmentation required.
- Method Used: Univariate and multivariate techniques
- Tools Used: Spotfire, Tableau , Xcelsius, Webfocus, Microstrategy, Visual Basic
- Identify homogeneous groups/clusters: Identify segments or clusters that are homogeneous (similar) within themselves and heterogeneous (different) across each other are identified using various statistical techniques.
- Identify heterogeneous groups/clusters and outliers: Identify unique profiles and behavior for specialized treatment or consideration.
- Statistical techniques: Cluster analysis techniques(K-Means, hierarchical clustering), factor analysis, principal component techniques, segmentation techniques, Discriminant Analysis Techniques, Bayesian Belief Network, Random Forest and Decision Tree
- Tools Used: SAS, SPSS, R, RStat, Treenet, Answer Tree
- Predict and model 'what if' future outcomes: Based on historical data, statement on the future event is estimated using qualitative and quantitative statistical techniques.
- Statistical techniques: Time series and regression methods, exponential smoothing, ARIMA, ARIMAX Techniques, structural models, dynamic regression, Markov Chain, Monte Carlo Simulations
- Tools Used: SAS (Base SAS, Forecast Studio), E-Views, R, RStat
- Defines key features in data: Provides summary descriptions of datasets such as population distributions, central tendencies or demographic profiles.
- Statistical techniques : Structured Equation Modeling (SEM), market basket analysis
- Tools Used: LISREL, SAS, KXEN
- Estimate predicted outcomes: Assess the likely probabilities of a particular outcome based on historical data. Typically includes out of time validation to demonstrate usability across alternative data time periods and populations.
- Statistical techniques: Linear regression, logistic regression, generalized regression, conjoint and discrete analysis, hierarchical modeling, semi-parametric techniques, Bayesian techniques, neural network, gradient boosting, Random Forest, Support Vector Machine, Monte Carlo Simulations
- Tools Used: SAS, SPSS, R, RStat, Treenet, Emblem
- Identify likely best action sets: Provides the set of best action based on scientific predictions given constraints to maximize or minimize a specific function/problem by systematically choosing input values among specified parameters.
- Statistical techniques: Linear programming techniques, Non-Linear Programming techniques, Stochastic Programming techniques
- Tools Used: CPLEX, SAS, R
Quality of Analytics:
Quality checks are completed at data input steps as well as on deliverables prior to delivery:
- Accurate deliverables (both numerically and grammatically)
- Proper aesthetics (appropriately Formatted outputs)
- Supports desired business relevance
- Project charter - client needs assessment, objectives, intended uses and scope
- Project design – solution requirements, general timelines, data requirements, skills, domain knowledge, system/functional integration/deployment requirements
- Project team creation – assignments based on skills and domain experience
- Project kick-off – deliverables, detailed data definitions, dependencies and risks, communication and status approach
- Planning – project action items, milestones, target timeline
- Development – data harmonization, analysis, modeling, insights, deliverable generation
- Implementation/deployment – guideline plan on integration to support intended uses
- Project feedback – project team input on what worked well and what can be improved leveraging Net Promoter Score and proprietary client satisfaction survey
- Process improvements - Client teams and leadership regularly review client feedback to develop and manage ongoing process improvements