INFINITECH aims to target business problems and challenges faced in the financial industry. For that purpose, User Stories from pilot partners were gathered, describing and providing the necessary information to analyse the most important problems, gaps hindering the exploitation of available data assets, and the limitations of existing data services. For this task, was used only a sub-set of the Business model canvas, dedicated to the Value Proposition. The Value Proposition Canvas Methodology has the purpose to collect information and in a very simple way identify the characterisation of the user within the story, by identifying 3 things: Jobs to be done, pains and gains. This helps INFINITECH Stakeholders identify their objectives, the reasons behind these objectives, and how these can be achieved.

INFINITECH User Stories The structure of the methodology provides a visual advantage by facilitating the association of the value propositions with the stakeholders' requirements. Based on this Value Proposition Canvas methodology all INFINITECH stakeholders answered to a questionnaire following 2 steps.
  • The 1st step of the process is the User Characterization, meant to identify jobs to be done, wanted gains and experienced pains.
  • The 2nd step is the definition of User Stories, which must be a short and simple description of a feature told from the perspective of an end user and were defined in the form:
As a <type of user>, I want <some goal> so that <some reason>

    Currently, among the different 5 categories, we have a total of:

  • “Jobs to be done”: 39
  • “Gains”: 54
  • “Pains”: 37
  • “User Stories”: 103
  • We can see the distribution of User Stories gathered in the D2.1 by category in the following chart:

    INFINITECH User Stories

    The INFINITECH consortium will continue to work on the User Stories and release an update document (Deliverable D2.2) which will provide further details related to the User Stories and a set of Stakeholder Requirements. Stay Tuned…


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    logo europe This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 856632.
    The content reflects only the authors’ views, and the European Commission is not responsible for any use that may be made of the information it contains.

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