The Data Science Consulting Alexander Thamm GmbH has developed a new process model, the Data Journey Model, with which every company can derive real added value from its data in three steps.
Data is the future and, in addition to large companies, is also increasingly permeating the Medium-sized companies. In order to Digitisation engine to switch on and gain real added value from the data, interlocking processes must be integrated into the company. Alexander Thamm GmbH has developed a new approach for this: the Data Journey Model.
This arose from the experience of Over 400 Data Science projects. "We create real added value for the company with the data. In order to achieve this, we develop the appropriate data strategy based on the status quo, test the use cases in the Data LabWe build prototypes for this and finally scale them up to a real data product or data service," explains Managing Director Alexander Thamm.
With such a Use Case it could be, for example, predicting customers who want to switch to another car brand. In the Data Lab, the data scientists would use a Algorithm develop for a specific vehicle model in a country, for example the USA. In the Data Factory this would then be scaled up and extended to further vehicle models, brands and countries, until in the end the algorithm would be used throughout the entire automotive group.
With the Data Journey Model, Alexander Thamm GmbH wants to get German companies to do this, regardless of size and industry, generate competitive advantages from data. "This is the only way we can be successful in Germany in the long term and hold our own against American companies like Facebook or Google," says Alexander Thamm.
Data Strategy as the start of the Data Journey Model: Status Quo and Benchmarking
The Data Journey model starts with the development of the Data Strategy. This is the basis for generating real added value and benefits for companies from data. To this end, the Munich-based Data Scientists of Alexander Thamm GmbH was the first Assessment through. Together, the status quo is assessed, a location determination is carried out and a benchmarking of the current Big Data and analytics capabilities.
Based on this, the data professionals develop an individual data operating model that is based on five pillars: Organisational structure, processes, roles, Data governance and system landscape. The operating model is, so to speak, the "digitalisation engine" for creating real added value from data. Subsequently, a Roadmap for relevant use cases of the customer. For this purpose, a comprehensive use case list is generated, prioritised according to benefits and feasibility and the customer's use case library is filled for the first time.
The aim of the Data Strategy is to ensure that the client has a clear idea of data success factors, is familiar with best practices from various industries and has a customised data operating model with clear objectives. Once the strategy phase has been completed, the Data Lab Phase the use cases are implemented.
Data Lab: From concept to prototype with real data
In the Data Lab, the Data Journey model continues. The goal is to test use cases as quickly as possible - from the concept to the prototype with real data. In joint workshops, the use cases are implemented step by step in the Data Lab. The first step is to create a use case concept: The data scientists generate hypotheses for the use case and check the necessary data.
In the subsequent exploration, a Proof-of-Concept and a test environment is built with the data. In this way, it is quickly assessed whether the use case can be implemented in reality or not. After successful exploration, the first prototype is programmed with real data from the customers. At the end of the Data Lab, the prototype and the Basis for scaling the use cases is created.
Data Factory: Creating added value - with the Data Product
The Data Journey model goes into the Data Factory further. Here, the use case is industrialised into a finished product. The absolute focus is on scaling and the sustainable generation of added value - which is why the focus here is also on the user.
First, a Scaling plan with prioritisation of markets, functions and brands. Then, based on the scaling concept, the next expansion stage of the pilot begins and the prototype becomes a Minimum Viable Product (MVP). Continuous testing in the development pipeline turns this version into a marketable data product or service. By means of DevOps further development and operation of the data product merge.