Data Sciences Group

Analytic Blueprint

Data Integration

Advanced Analytics

Data Sciences Group

Focused on turning data into information that drives innovation and fuels competitive advance.

The Data Sciences Group (DSG) is a discipline developed to bring clients a data-focused experience. Data Sciences encompasses what we would typically consider traditional Business Intelligence and Data Integration; but much more. Our mission is to take client’s data and turn it into information that leads to knowledge, innovation and competitive advantage.

Analytic Blueprint
Analytic Blueprint

It is proven that successful projects are achieved by establishing a foundation in planning that drives implementation. New Resources Consulting’s Data Sciences Group supports this philosophy by offering an organization their proprietary ‘Analytics Blueprint’ to more effectively and efficiently implement a customized analytics system.

The detailed strategy allows an organization to engineer an analytics system utilizing best practice approaches and available technologies, tailoring the system based on their desired priority of features.

The resulting ‘blueprint’ summarizes the current state of the organization and its systems and articulates the goals and processes that will be used to develop the new analytic system.

Benefits include:

  • Aligns the organization’s business processes and information systems.
  • Defines a clear business and technology strategy, incorporating system design, architecture, and implementation.
  • Incorporates industry best practices into the analytic system.
Data Integration
Data Integration

DSG offers complete data integration strategies and solutions; the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information. Data integration strategies include common structures, such as operational data stores, and also extends into the big data space and data lakes utilizing common approaches such as Hadoop, Casandra and MongoDB.

Benefits include:

  • Improves overall business efficiencies.
  • Increases data integrity.
  • Provides ease of data collaboration. 
  • Allows more educated business decisions.
Advanced Analytics

DSG also embraces multiple advanced analytics, including dimensional data warehousing, data marts and analytic sandboxes for the purpose of contextually binding data, deriving measures and facts and integrating third party data for analysis. Through dimensional structures and sandboxes, data visualization and exploration is greatly simplified and opens the door to self-service business intelligence strategies. Also, dimensional and sandbox structures provide a perfect platform for visually interacting with data, performing descriptive statistics, discovery and situational analytics.

Analytic sandboxes provide an avenue for data scientists to perform advanced analytics including statistics and biostatistics, data mining, and predictive analysis. Such advanced techniques may be accomplished via common statistical tools, such as SAS and SPSS, as well as specialty tools that also visually represent mathematical models.

Success Stories

Throughout the website we describe our offerings. What it is. How we differentiate ourselves. And how it all benefits you. Sometimes, no matter how clearly we try to spell it out, it’s best communicated—not by us—but by the clients we helped succeed.

DEAN HEALTH SYSTEMS
Data Sciences 

NRC professionals provided real-time replication from EIPC Clarity to an operational data store and then into a dimensional data warehouse.

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NRC professionals provided real-time replication from EIPC Clarity to an operational data store and then into a dimensional data warehouse. The data warehouse was patient and provider-centric, as well as multi-organizational (regional) and included other sources such as HR and finance.

All regulatory compliance and organizational analytics were calculated within the warehouse, including chronic disease, KPIs, Wisconsin Collaborative for Healthcare Quality (WCHQ) measures and analytics-based third party data. Data marts were created for each subject area where visualization tools (Qlik & Tableau) allowed users to interact with the information.

The solution also included four (4) Accountable Care Organization (ACO) data marts. As the company was multi-organizational (regional), due to Centers for Medicare and Medicaid Services (CMS) regulations, each region required their own ACO and, subsequently, their own unique ACO data mart. Each data mart was completely secured and isolated to the information for its designated ACO. Information from the data warehouse, as well as third party data, was supplemented into each ACO data mart for enhanced analysis capabilities. Analytics were calculated in each data mart and presented to end users via visualization tools. CMS ACO guidelines stipulated that each ACO data mart had to be secure (i.e. not backed up to any central repository) and it had to be demonstrable that all information could be destroyed permanently if required.

AEGIS SCIENCES CORPORATION, TN
Data Sciences 

NRC was responsible for the real-time replication of data into an operational data store and then near real-time into a dimensional data warehouse.

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NRC was responsible for the real-time replication of data into an operational data store and then near real-time into a dimensional data warehouse.  Data from other systems was integrated into the system including patients, physicians, claims and financials.

The dimensional model permitted multi-context views of the information including sample, patient, physician and claim. Sample data, captured in near real-time (every 15 minutes), provided the information necessary to track sample progress at any given point in time.

Multiple analytics were calculated within the warehouse and delivered to data marts for visualization. Third party data was integrated into the warehouse so it could be utilized in analytic derivations as well as be delivered to analytic sandboxes. Analytic sandboxes, utilized by data scientists, were constructed to produce predictive models that would be used for data monetization purposes.

The NRC Difference?

We actually get to know your business,
people, tools, and technology.

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