Model-driven decision making

The fictitious company FastChangeCo has developed a possibility to manufacture Smart Devices. With each of these devices, a large amount of (sensitive) data is generated. Based on this data, FastChangeCo aims to make forward-looking decisions. On the one hand, this is intended to encourage customers to make targeted purchases, but also to significantly improve the quality of existing products and develop future products. In this presentation, the speakers will show how FastChangeCo has achieved its goals by rapidly building the required computing capacity in its Hybrid Cloud Data Warehouse architecture using existing enterprise technologies.

Target Audience: BI-manager, CTO & CIO, Data Engineers, Data Scientists, DWH Architects, Data Modelers
Prerequisites: Basic knowledge in: BI, Data Science, Data Modeling, DWH architecture
Level: Basic

Extended Abstract
'The fictitious company FastChangeCo* has developed a possibility not only to manufacture Smart Devices, but also to extend the Smart Devices as wearables in the form of bio-sensors to clothing and living beings. With each of these devices, a large amount of (sensitive) data is generated, or more precisely: by recording, processing and evaluating personal and environmental data.
FastChangeCo's business model is not primarily aimed at licensing the technology and selling it profitably. On the contrary, the goal is to use low prices to position the technology as broadly as possible in the market in order to earn money later with a large database.
Based on this data, FastChangeCo aims to make forward-looking decisions and develop innovative services. On the one hand, this is intended to encourage customers to make targeted purchases, but also to significantly improve the quality of existing products and develop future products.
In order to combine the data into a logical overall view, FastChangeCo has committed itself to a consistent implementation of the information landscape with methods of data modeling. A logical data model maps the necessary business objects as well as their attributes and properties independently of the technology used by a company.
Where, how and with which technology the data of the data warehouse is persisted is not relevant in the logical data model. Only with the development of the physical data models does the technology used become important and thus the physical modelling method. In this constellation, the modeling method Data Vault, in which the data can be modeled across all technologies, and a virtualization technique that unites all the technologies involved as a central instance, are ideal. The central aspect of virtualization is that it is irrelevant to the user where the data actually lies, but the user finds a fully integrated data landscape.
Another aspect is that the data for predective modelling is already well structured and of high quality. Thus, FastChangeCo is able to use the newly gained knowledge to implement its goals, namely the future-oriented decision and innovative services. Large amounts of data from the data warehouse as well as an enormous temporary computing capacity are required for the creation and training of the required prediction models.
However, FastChangeCo is not prepared to permanently maintain the high costs for the required computing capacity, as this drives up costs without economic added value.
In this presentation, the speakers will show how FastChangeCo has achieved its goals by rapidly building the required computing capacity in its Hybrid Cloud Data Warehouse architecture using existing enterprise technologies. The elasticity thus achieved contributes significantly to a cost-efficient solution.
* There is no permission to name the customer, hence the fictitious company FastChangeCo.