Big Data – More than just data

Data is growing. Relationships are becoming more complex. We create structure: ETL, metadata, MDR, and data models — so analytics and data exchange work reliably over the long term.

What we do for you

From raw data to reliable analytics — via standards and metadata

Service components:

  • ETL (Extract–Transform–Load): Bringing data together from different systems and making it available for analysis/reporting
  • Metadata & MDR: Definitions, ownership, versioning, quality requirements — the “glue” for stable exchange and analytics definitions
  • ISO-aligned structuring: Guided by ISO/IEC 11179 (MDR) and ISO 25964 (vocabularies/mapping) for long-term reusability
  • Data models & analytics based on requirements: KPIs, quality rules, exports — aligned with your domain definitions

MDR — valuable even with a smaller data footprint

Why a small MDR pays off early

An MDR is worth it early on because (1) data naturally grows, (2) dependencies become more visible as datasets expand, and (3) MDR makes data exchange easier to automate and better documented.

Three typical use cases

  • Use Case 1 – Data capture & structuring based on standards principles

    When new data needs to be captured or legacy data consolidated, we create a model with clearly defined data elements, value ranges, units, naming conventions, and versioning (aligned with ISO principles).

  • Use Case 2 – Data consolidation from heterogeneous subsystems

    Migration/mapping into a unified structure so analytics work cleanly not only retrospectively, but also with current data. (Experience from clinical data projects, including consolidation and “Clinical Data View” patterns.)

  • Use Case 3 – Semantic layer for interoperability (bridge to FHIR)

    Metadata + terminologies/ontologies as the foundation so interoperability doesn’t stop at “structure/transport.”

Approach

  • Clarify goals & analytics requirements

    (data catalog, quality criteria)

  • Set up ETL + model + MDR

    (terms, data elements, governance)

  • Validate & document

    (checks, examples, versioning process)

  • Handover & enablement

    (training/pairing — see IT Buddy)

FAQ (Data)

  1. Does an MDR really make sense even with “only a small amount of data”?

    Yes — among other reasons because data grows, relationships become more visible, and exchange/documentation becomes significantly easier.

  2. We use the following standards as guidance

    ISO/IEC 11179 (Metadata Registry) and ISO 25964 (thesauri/interoperability of vocabularies).

  3. What is your typical output?

    Data model + MDR entries (definitions/value ranges/versions) + ETL pipelines + documented analytics logic.

  4. Which technologies do you support?

    Java/Python/JavaScript/HTML5 as well as common web stacks and SQL/NoSQL — aligned with your system landscape.

Do you have further questions about ETL, metadata, MDR, or data models in general? Contact us by email.

Email

Contact