Unveiling the power of data: Part I – Navigating challenges and harnessing insights in data-driven projects

By Michele Bolla (ERNI Switzerland)

In this series of articles (three in total), we look at data-driven projects and explore seven common challenges that arise during their execution. To illustrate these concepts, we will focus on one of ERNI’s latest project – GeoML, dealing with the development of a machine learning algorithm capable of assessing road accident risks more accurately than an individual relying solely on their years of personal experience as a road user, despite limited resources and data availability.

Data-driven projects have become instrumental in various industries, enabling organisations to leverage the power of data for informed decision-making and valuable insights. In this article, we delve into a compelling case study that exemplifies the utilisation of data in a data-driven project, focusing on the integration of georeferenced data and the development of a machine learning (ML) algorithm. Through this case study, we highlight the broader applicability of these techniques beyond the specific domain of road safety.

The importance of georeferenced data

Georeferenced data plays a critical role in accurately assessing risks and understanding spatial patterns. It encompasses a range of information, including geographical locations of events or phenomena, road networks, and relevant contextual data. By georeferencing data, patterns can be identified, and risks associated with specific areas can be evaluated. In our case study, high-resolution satellite imagery serves as a valuable data source, enabling the analysis of various spatial factors and their impact on the ML algorithm.

Challenges in data integration

Data integration poses significant challenges in data-driven projects, especially when dealing with diverse and heterogeneous data sources. Data quality and consistency are paramount, and data from different sources must be harmonised and aligned to ensure accuracy and reliability. Additionally, considerations such as data volume, variety, velocity, and veracity come into play when integrating data from various sources.


Large volumes of data can be overwhelming, leading to storage and processing challenges. In our case study, managing the substantial volume of high-resolution satellite imagery requires efficient storage solutions and appropriate data processing techniques to handle the data effectively.


Data variety is another challenge in data-driven projects, as data may come in different formats, types, and structures. In our case study, the integration of diverse data sources, such as satellite imagery, sensor data, and road network data, necessitates careful data harmonisation and transformation to ensure compatibility and relevance to the question at hand.

Data-driven analysis and insights

Once the data is integrated and processed, the next step is to perform data-driven analysis and derive actionable insights. This involves applying appropriate ML algorithms to the integrated dataset to extract patterns, correlations, and predictive models. In our case study, the ML algorithm is trained using the integrated georeferenced data to generate meaningful insights and support decision-making processes.

Applicability in diverse contexts

The insights and methodologies gained from this case study extend beyond road safety applications. The integration of georeferenced data and the utilization of ML algorithms can be applied in various domains, such as urban planning, environmental monitoring, logistics optimization, and resource management. The ability to analyse and interpret spatial data effectively empowers organisations to make informed decisions and optimize their operations.


Data-driven projects offer immense potential in leveraging the power of data for valuable insights and informed decision-making. Through the case study of integrating georeferenced data and developing an ML algorithm, we have highlighted the broader applicability of these techniques beyond road safety. Overcoming challenges related to data integration, volume, and variety is crucial for successful data-driven projects in any context. By harnessing the potential of georeferenced data and ML algorithms, organizations can unlock valuable insights and drive innovation in diverse domains.

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