Xomnia is a word combining the letter ‘X’ – the unknown – and “Omnia” – Latin for everything. Our team of data scientists and big data engineers are trained to find the undefined – X – in all the relevant data sources – Omnia. This unknown – X – is untapped business value. Combining the X and Omnia you get the Xomnia spirit. Eager, curious and dedicated people, who have the belief that the future is big data.

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Xomnia supports your organization in becoming data-driven by delivering your big data projects. Xomnia’s projects range from lightweight proof of concepts to fully-fledged product development and implementation.

Proof of concepts aim to answer data-driven questions. An example of such a question is ‘What is causing phenomenon X and how can we predict the value of X?’. And if that seems to be possible, what is the underlying business case, how can we make the right impact, can we actually generate additional business value?

If a case gets through this proof of concept phase the multidisciplinary team of both Xomnia and client members continues to the production phase. This production phase is all about developing and implementing a value-generating solution or platform to actually harvest the untapped business value.

To get there in a manageable way, Xomnia works with Agile project teams via the SCRUM method. The project team follows the CRISP-DM framework. Project management tool JIRA is often used to improve the communication and collaboration between project team members and the client. A Domino Data Lab environment is being used so that the client has full access to all that is being developed already during the project.

As soon as we go ‘live’ with a first version, you will start reaping the business benefits at full scale. Xomnia takes its responsibility regarding the maintenance on the deliverable to guarantee its functionality. Together we focus on further improvements and future innovations.

CRISP-DM cycle

1.Business Understanding

  •  determine business objectives
  •  assess situation
  •  determine data mining goals
  •  produce project plan

2.Data Understanding

  • collect initial data
  • describe data
  • explore data
  • verify data quality

3.Data Preparation

  • select data
  • clean data
  • construct data
  • integrate data
  • format data
  • dataset description


  • select modeling techniques
  • generate test design
  • build model
  • assess model


  • evaluate results
  • review process
  • determine next steps


  • plan deployment
  • plan monitoring and maintenance
  • produce final report
  • review project

Do you need personal advice?

Please email or call Tim

+ 31 20 772 84 25


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