TechZavy

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Traditional BI platforms require IT-produced analytics content, specialized tools and skills, and significant
upfront data modeling, coupled with a predefined metadata layer to access their analytics capabilities.

Enterprise data quality from disparate sources, losing analytical context in relationship to each other, and lacking  single source of truth about data continue to pose challenges with operational and transformative outcomes to uncover real value of the analytics 

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Develop self contained architecture that enables nontechnical users to autonomously execute full-spectrum analytics to develop use cases, measure KPIs and derive business value by enabling business users to access, ingest, and prepare data for interactive analysis and collaborative sharing of insights.

Create unified data model for the enterprise to ensure correct and complete aggregation of data and its quality. ​​For example in healthcare, a holistic view of the patient experience over time and across all sites and episodes of care will require connectivity to hundreds of data sources, including EHR, unstructured text, imaging, audio scribes, billing and claims, etc., in order to establish a longitudinal patient record with 360-degree view of the care episodes. We develop adequate proof of concept, proof of technology, and proof of value for analytical models ahead of the analytical planning and execution.

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We have expertise reducing exponential computation time required for big data analytics to linear times

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Proprietary variable selection engine to identify statistically significant variables

Profiling and predicting algorithms with ensemble techniques for predictions from different models 

Descriptive analytics to classify data into clusters, and filter correlated variables 

Machine learning techniques to refine  variable list further to arrive at the final model​

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