Data Science

Pillars Project delivery, consulting, knowledge transfer and data lab shown in a circle.

IT-PS offers data science consulting, project delivery and knowledge transfer.

With data science, you can draw important information from large amounts of data that can help your company gain a decisive advantage over competitors. Experienced data scientists derive recommendations for action from the data available in your company, which help you to set up your company more efficiently. Data science also helps you to proactively maintain machines and systems and avoid malfunctions (predictive maintenance). With the help of so-called predictive analytics, it is possible to predict future events based on historical data.

Deep learning, machine learning and artificial intelligence (AI) have become reality and are at the heart of every company that takes digitalisation seriously and sees it as an opportunity. Neural learning no longer only takes place in the human brain, but also belongs as artificial neural learning in the success concept of a data-driven company. Neural networks and artificial intelligence (AI) are sustainable, new approaches for your corporate strategy.

IT-PS advises and supports you in every phase of your project: we analyse the data science potential of your company, implement the project with you or consult you on the implementation. And we are there when it comes to establishing the know-how within your company. As an Austrian IT system house, however, we also keep an eye on the strict rules of the European General Data Protection Regulation (GDPR) and always act in accordance with it.


Strategy Workshop Data and AI

Our Data & AI strategy workshop represents the first step for a successful path into the world of data, machine learning and AI for your company. Here, management learns the most important basics of data science as well as the vast possibilities of data & AI in their industry.

Likewise, it covers how you can best embed the topic organisationally and thus successfully embark on the path towards becoming a data-driven company.

Progress in this phase:

  • Establish fundamental know-how in management
  • Develop a data & AI strategy for your company
  • Plan your first steps

Use Case Roadmap Workshop

The Use-Case Roadmap Workshop is an important building block for a successful data & AI strategy of your company.

In this workshop, the findings from the strategy workshop Data & AI are brought to an operational level and a prioritised list of projects or use cases is derived, which can henceforth serve as a roadmap for the company's own strategy.

Here, your employees learn how data science works and how you can use your company data in new ways. We identify possible use cases with you, evaluate them and help you decide which cases have priority. In this way, after a short time you already have a solid plan in hand from which you can take further meaningful steps.

Progress in this phase:

  • A structured creative process to find use cases in your organisation
  • Evaluation of the use cases found according to criteria that have been tested in practice
  • Creation of a use case catalogue for your company
  • Roadmap with prioritised use cases

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Knowledge transfer


Coaching and Peer Review

Data scientists are in high demand on the labour market and often difficult to find. That's why companies go down the path of upskilling existing staff, for example from controlling or IT. We train your employees directly on real and relevant use cases, which we implement together with your employees. In this way, you learn how to identify, design, plan and implement concrete and company-relevant issues, and the knowledge is transferred to your company.

If your data science team is already implementing use cases, your company can benefit from coaching and peer review.

Coaching is the selective support of existing project teams in order to offer your own data science team a professional exchange with external experts. The coaching forum offers the opportunity to question existing solutions, discuss new ideas and approaches, and examine specific topics in more detail. Coaching enables existing teams to think outside the box and find new, innovative and promising approaches in the field of data science, machine learning and AI through professional exchange.

Peer review is specifically designed to check ongoing projects with external experts. The tool is known from the field of science and is used in journals for quality assurance. But unlike in academia, our focus is discussing analyses or partial steps from it together, informally, in a friendly atmosphere, and providing feedback and suggestions.

Coaching and peer review are formats from which both career starters and experienced people can benefit. In addition, there are also advantages for management, because coaching and peer review serve the quality assurance of AI projects.

Project delivery

Data science, machine learning and AI projects are very different from other projects: solutions have to be developed iteratively, in close consultation with the customer and end user. We offer a three-stage model for this.

Grafische Darstellung einer Data Science Projektumsetzung.


For larger or more complex projects, it makes sense to have a dedicated concept phase before the actual start.

In this step, we work with you to create a concept for a selected use case, in which we describe one or more possible solutions, their requirements and the expected costs, risks and chances of success in more detail. Possible organisational implications in the company are also addressed.

A definition of measurable success criteria rounds off the concept and subsequently enables a targeted implementation of a prototype.

In this phase...

  • you develop a deep understanding of the use case
  • we work out the exact requirements for the individual case
  • we describe possible solutions with costs, risks and chances of success
  • we discuss data protection aspects
  • establish success criteria and KPIs


Before a final productive version is implemented, it makes sense to develop a prototype solution to test the suitability of the data for solving the use case with machine learning.

First necessary data quality for achieving the defined success criteria is checked in a prototype. In addition, the architectural requirements of a final data product are considered.

The prototype is already being implemented using container technologies in order to be able to run later on all platforms - whether on-premises or in the cloud.

Progress in this phase:

  • Data cleaning and preparation, feature engineering
  • Selection and testing of machine learning algorithms
  • Training and testing of the models
  • Prototypical visualisation of the results

Data product

After the prototype has been implemented, IT-PS Data Science can support you in porting the solution to a productive environment.

Progress in this phase:

  • Refactoring for production use
  • Deployment into scalable container environments in the cloud and on-premises
  • Connection to productive data sources and target systems
  • Scheduling of training and inference cycles or streaming connectivity
  • Integration into the security and compliance context of the company
  • Completion and productive deployment of user interfaces and visualisations

Call us, we'd love to answer all your questions!

IBM Power-Plattform

When it comes to infrastructure, our data science team focuses on the IBM Power platform on Linux as well as AIX and IBM i, in addition to the classic Intel systems. Our close cooperation with the IBM labs is unique and enables us to apply the latest technologies in the areas of machine learning and deep learning on these platforms. Together, we use the synergy effects of your existing infrastructures. In this way, we keep investment costs low, especially at the beginning of your path to become a data driven enterprise.

IT-PS Data Lab

The IT-PS data science team usually implements its projects with the IT-PS Data Lab. The Lab provides a uniform project environment with all important open-source frameworks for Python and R for all platforms*. Through the consistent use of container technologies, the IT-PS Data Lab can be set up very quickly in customer environments on premise, in the cloud, or deployed in our data center.

* IBM Power on Premise and in the Cloud, x86 on premise and in the Cloud

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