Data Mapping

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Problems we solve:

  • Organizations often store their data in formats that are incongruent with modern business operations and needs
  • As an enterprise evolves – and its data needs and usages change – on-staff technical experts may have difficulty maintaining a deep understanding of data structures and access mechanisms
  • Newer applications are often incompatible with older data structures and formats, which must be translated (or mapped) in order to be used by modern applications
  • To use new applications, organizations need assistance organizing their data pipelines and transforming them into modern, consumable structures

What we do:

  • Evaluate existing systems to thoroughly understand their data models and make recommendations for modernization
  • Document the data mappings necessary to transform the data into modern formats
  • Recommend data architectures and designs to improve time-to-market and presentation of healthcare data
  • Implement mapping logic using an organization’s existing tools or a custom solution


  • Quicker ability to onboard new sources of data
  • Improved interoperability between internal and external systems
  • Better visibility into and understanding of data
  • Ability to analyze and correlate for business analytics

Our expertise:

  • Conceptual, logical, and physical data modeling
  • Databases and data lakes, catalogs, warehouses, and vaults
  • Manual, schema, and automated data mapping
  • Deterministic and probabilistic data matching
  • Health data formats (FHIR, HL7, CCD)

Technologies we use:

  • Programming: SQL, Python, PySpark
  • Data catalogs: Alation, Collibra, Informatica
  • Data management platforms: Palantir, SEMOSS
  • Integration tools: Dell Boomi, Informatica, Talend
  • Data modeling: ER/Studio, Erwin

What we build or enhance:

  • Data models
  • Analysis software/microservices
  • Harmonization engines
  • Dashboards, reports, and visualizations
  • Data dictionaries and business glossaries
  • Decision support tools and applications
  • Transformation services (ETL)
  • Mapping specifications for data rules, transformations, and mediations between source and target elements

Our Approach

Amida’s data-mapping process transforms medical data from Disability Benefits Questionnaires (DBQs) into the inputs required by the Veterans Benefits Management System Rating (VBMS-R) Evaluation Builder. Our Dynamic Medical Data Transformer (DMDT) automates manual data transformations that were performed manually.

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