The management board of Empirisch Tech GmbH consists of a group of experts with more than a decade of experience in software development and artificial intelligent and have worked with various prestigious companies including McKinsey, BCG and T-Mobile. Its advisory board comprises of C-level executives from telecommunications and banking sectors.
After learning from industry challenges for so long, we have built up a team of the following types of experts:
The foundation of a date-driven culture is based on digitalization therefore, the first step is to implement comprehensive ETL/ELT (Extract Transform Load) pipelines. ETL pipelines serve to extract raw data from various sources such as Excel, CSV files, CDRs, PDF documents, etc., transform it into desired data structures and finally load it into designated locations such as DWH, cloud or on-premise data lakes. Therefor, we provide senior data engineers experienced in building and deploying complex ETL pipelines on both cloud and on-premise infrastructures.
The companies well ahead on their digitization journeys have established reliable ETL pipelines and their next natural transition is towards extracting actionable insights from tons of the transformed data which is saved on daily basis. We assist companies in this regard by providing experienced data scientists with skill-set spanning from traditional BI tools such as Tableau and PowerBI, to complex machine learning libraries and frameworks. We have expertise in numerous types of analysis such as descriptive, inquisitive, predictive, predictive and pre-emptive analysis, see details on analysis types. A few of implemented data science use-cases are listed here.
In addition to the above two roles, experience has taught us that many organization need a lot of time and trust before they begin their data-driven journeys. Such organizations seek quick yet comprehensive data science use cases as means to built their trust by testing their competence. In such cases, the end-to-end responsibility ‐ developing ETL pipelines and running data science code on top ‐ lays on the shoulders of the data scientist alone. This means that, in addition to the required data science skill-set, the expertise in data pipelining, model deployment and monitoring becomes a must-have. To meet this challenge, we support organizations with full stack data scientists to help them kick start their data-driven journeys and early business value attribution.
Our main focus is to maximize business value attribution which is sometimes not bounded by the complexity of the solution and can rather be achieved by a simple approach. However, we are no short of experts on complex topics such as natural language and image processing. Our data scientist help companies built complex chains of interdependent use cases to maximize their rate of return and help them materialize the true hidden potential of data-driven decisions.
