In4Uptime

Purpose and goal

The primary objective has been to investigate benefits that increased usage of data analytics can bring to the uptime area, in particular by increasing the penetration and quality of service contracts and related services. In4Uptime aimed to analyse three lynchpins of this objective; The 1st focused on the data; understanding what was available, and what could be collected. The 2nd was to develop new algorithms for analysing the information and to evaluate their accuracy, requirements, etc. The 3rd was to analyse the business needs and opportunities related to Data Science.

Expected results and effects

In4Uptime has demonstrated that, in the automotive industry, potential improvements are possible in terms of generating more value by collecting and analysing more data. That applies to both analyzing internal data sources and by combining different data sources. As apps have revolutionized how we use our phones, bringing together vehicle on-board systems and external data can lead to integrated ecosystems with both reactive data flows supporting proactive measures. Examples show that data can become an important asset that can be reused for, originally unforeseen purposes.

Planned approach and implementation

The project followed a typical Data Mining process; Identifying available data sources, selecting those that were relevant, and collecting information within the analytics environment. Then data needed to be preprocessed, and finally analysed. Conclusions were drawn from the analysis which lead to investigations on new business models and services. Those steps were followed successfully. Most energy was spent on data collection, preprocessing and analysis. In4Uptime has taken a high level approach looking for methods that could be applied developing Big Data services.

External links

Animated three minute movie describing the project results and future potential applications.