Data Journey begins with the development of your Data Strategy. This is the basis for generating real added value and benefits for your company from data. The first step is to carry out an assessment. Together we assess your status quo, determine your current situation and benchmark your current Big Data and Analytics skills. Based on this, we develop your individual data operating model based on five pillars: organizational structure, processes, roles, data governance and system landscape. We will then work out a roadmap for your use cases. For this purpose, we generate an extensive use case list, prioritize it and fill your use case library for the first time.
Data Journey continues in DataLab. The aim is to test use cases as quickly as possible. To this end, we first create a use case concept: We generate hypotheses for the use case and check the necessary data. In the subsequent exploration, we perform a proof-of-concept and build a test environment with your data. In this way, we can quickly assess whether the use case is feasible in reality or not. After successful exploration we program the first prototype. This is the alpha version of your analytics or Big Data App.
We are continuing the Data Journey at Data Factory. Here your use case is industrialized to a finished product. The absolute focus is the scaling and sustainable generation of added value - therefore the user is also in the focus here. First we create a scaling plan with prioritization of markets, functions and brands. We then use the scaling concept to move on to the next stage of the pilot's development and turn the prototype into a Minimum Viable Product (MVP) - the ß-version of your analytics or Big Data App. Through continuous testing in the development pipeline, we are turning this ß-version into a marketable data product. DevOps merge further development and operation of the data product.
The Data Journey continues. We operate and maintain your platforms and machine learning algorithms in our Data Ops. Today's IT projects require a different approach and methodology with regard to maintenance and operation. The reasons for this are agile methodologies and completely new technologies. Current data products based on machine learning or artificial intelligence no longer automatically end with the proof of concept. Thus, AI algorithms are more and more integrated into the permanent operation of software solutions. Therefore new expertise, different know-how and agile working methods are in demand.