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Value chain for data analysis Selection Integration and Transformation Mining Interpretation
processing

Custom Full Service

High Quality Data Service
Business Model Vocabulary Generation Configurable Model

Care Service

Workflow as a Service

Figure 4. Appropriation strategies of companies along the data analysis value chain

These openly available resources are mostly Depending on what stage(s) a company operates
combined with proprietary resources created along the value chain, it can employ different
18
by bioinformatics ventures themselves or their appropriation strategies . A firm can either
clients in the private sector. Outputs of those focus on one or more core activities or include
companies can vary considerably and range from the whole value chain in its business strategy.
drug discovery or diagnostic tests, to shaping new Particularly at early stages, our survey has shown
technology solutions or even domestic products that bioinformatics ventures tend to provide a full-
such as washing powders. service model for clients on a custom and hardly
scalable basis. The majority of mature ventures
With more open and proprietary life science data rely on technology to either provide a product at a
resources available, the bioinformatics industry particular stage, for instance, to provide access to
has brought forth companies on multiple stages of curated repositories of open and proprietary data,
the value chain. Figure 4 provides an overview of or generate tools for creating ontologies that will
the value chain of businesses making use of open integrate new data resources and answer specific
life science resources: research questions.

1. Collecting, selecting, and providing data To provide a scalable product along the entire
resources. value chain, some companies created platforms
and private infrastructures that help customise
2. Integrating data and pre-processing these
otherwise automated workflows to process raw
resources for later use either by the company
data into visualisations and insights.
itself or its customers.
3. Transforming data into models such as
vocabularies or ontologies.

4. Enabling customers to create their own
insights from these models through mining.

5. Providing interpretations from the data on
behalf of a client, for example in the form of
reports.

18 Rothe, H., Jarvenpaa, S. & Penninger, A. How do entrepreneurial firms appropriate value in bio data infrastructures: an exploratory qualitative study. Res. Pap. (2019).