Datawarehouse Modernization

Traditional data warehouses were built with online transaction processing (OLTP)-centric technologies and architectures that are old technology. These data warehouses were never designed to handle the volume, variety and velocity of today’s data-centric application. ElogicSquare’s current technology for Big Data allows organizations to dramatically improve return on investment (ROI) from their existing data warehouse environment.

Why do you require to modernize your data warehouse?

  • Costs-Users seek to save money in some areas (storage, CPUs, upgrades, admin) so they can invest in others (new data platforms, analytic tools, and developing new solutions).
  • Speed-User need the data warehouse and related systems to operate faster because speed contributes to scale, supports agile development and discovery analytics, and brings analytics closer to real-time business operations.
  • Advanced analytics-Many users have invested heavily in reporting and OLAP, but now they need to invest in advanced forms of analytics to leverage big data, find new customer segments, and stay competitive.
  • Scale- Users need to accommodate growing numbers of concurrent users, reports, analyses, and data structures.
Dataware Modernization

Data Migration

The ElogicSquare Data Warehouse Workload Migration solution is a proven, cost-effective, and low-risk solution to offload traditional data warehouse to Big Data warehouse. Benefits:

  • Save time and money. Our automated process is up to 75% faster and 70% cheaper than manual offloading.
  • Achieve faster parallel and scalable SQL processing using Hadoop and real time streaming .
  • Maximize your ROI and tools reuse.
  • Reduce risk in your Hadoop journey with automated migration of all legacy workloads: data, metadata, schema, views, Role based access control, & other scripts

The warehousing strategy of ElogicSquare allows transformation of data-sources from multiple tenants into a harmonized representation in a Data-Hub that supports standard analysis, one model to support multiple use-cases. We ensure that our solution also factors where a hospital data may be at different sites with its source specific-identifiers and nomenclature. Solution hinges on transforming to a standardized OMOP/CDM vocabulary to codify clinical domains of interest (Visit, Drugs, Condition, Procedure, Measurements). Such Patient-centric, 360-degree multi-model warehouse yields evidence OR inference abilities within and across hospitals, such as:

Such Patient-centric, 360-degree multi-model warehouse yields evidence OR inference abilities within and across hospitals, such as:

    Clinical Characterization

    • Quality Improvement
    • Natural History

    Population-Level Estimation

    • Safety Surveillance
    • Comparative effectiveness
    • Design, build and analyze Cohorts of Interest
    • Health outcomes of interest

    Patient-Level Prediction

    • Precision Medicine
    • Disease interception