Data science and artificial intelligence are gaining importance in all areas of the economy and our daily lives. Many business entities are just taking the first steps in this direction, while others are already beyond the pilot project phase and are facing major rollouts. What is common to most of these business entities: Expertise in this complex field is not yet sufficiently available internally, and the relevant experts are very difficult to find on the labour market.

This is where KPMG’s Lighthouse data science offering comes into play.

The Lighthouse Team thus covers all aspects of data science and artificial intelligence. Most of our data scientists have a degree in mathematics, computer science, physics or similar subjects and thus bring with them a deep understanding of data science methods. Together with the professional expertise of colleagues from other KPMG departments, solutions are created that combine technical excellence and practical relevance.

Data Science Academy and Data Science Factory

The Data Science Academy covers a wide range of advanced training offerings in data science. These start with general introductions to the subject and also include deep dives into special topics, such as deep learning and natural language processing for technical mathematical target groups. Here, we offer our own courses from our experts as well as curate the offerings of the numerous online service providers and compile them, customised to the individual needs of our clients.

In addition to these more practically oriented offerings, the C-level seminars provide an overview for decision-makers. We classify the various technologies in terms of their market maturity and practical relevance and discuss the impact on different industries and their business models.

The Data Science Factory represents the industrialisation of data science. The aim here is to organise in a standardised way the individual phases in the life cycle of a data science or AI application, from the specification of the use case to the operational implementation in a software system, and to enable the customer to outsource this process (completely or partially). Our experts take on the roles that cannot be filled by the client and integrate seamlessly into the overall process. This can also include cyclical iteration or long-term operation.

Standard solutions

From the experience of numerous data science projects, for certain issues, largely standardised solutions have been developed that can be adapted to the client’s respective situation with little effort and expense. These include, for example:

  • Intelligent Pricing, a system for optimising pricing in B2B,
  • Geo Analytics, a tool for analysing traffic flows and evaluating locations,
  • ScanDoc, a system for extracting information from (partially) structured documents, which, for example, is used to automate invoice recognition and entry.


In addition, projects to solve highly specialised tasks are a focus of the data science team. These relate to almost all industries, such as insurance, wholesale and retail, logistics, production, etc., and deal with problems such as

  • optimising spare parts logistics for technical services,
  • supporting the work in the call centre through the use of artificial intelligence,
  • predicting customer behaviour,
  • analysing sensor data to support predictive maintenance in the field of Industry 4.0,
  • building a semantic search for large amounts of documents,
  • extracting information from running text,

to name a few. Depending on the complexity of the task, techniques from statistics, mathematical modelling, as well as machine learning and deep learning are used. We strive to select the most appropriate technique that optimises the cost-benefit ratio.

Our data scientists and developers are proficient in all common tools and languages and are able to respond to the customer’s specific requirements regarding the system environment.

Data science in KPMG’s Lighthouse thus represents a comprehensive range of services including training, the use of standardised tools, the implementation of specialised projects, the development and operation of intelligent systems, auditing and the development of AI governance.

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