Data collections that are too large and unstructured to maintain with regular database management systems are referred to as Big Data. Data science, data analytics and data spaces are three terms often used in the field of data processing and analysis. While they have some overlapping aspects, there are also differences between the concepts. While data science focuses on developing models and algorithms to extract insights from data, data analytics focuses on exploring and interpreting data to obtain usable information. Data spaces (also called data ecosystems) is a relatively new concept and refers to the infrastructure and environment in which data can be stored, shared and analysed. The data ecosystem includes centralised and distributed databases and data sharing solutions. In these so-called federated (or distributed) solutions, data and tasks are shared across multiple systems, servers or organisations, with each part having its own responsibility and contributing to the bigger picture. This offers advantages in terms of scalability, resilience and privacy protection.
Data must be FAIR
For analysis and value creation, the data must be FAIR (an acronym for Findadable, Accessible, Interoperable and Reusable). This is a set of principles and guidelines developed to improve data quality and reuse. In addition, agreements should be made on use, access and value of the data. Data can be very heterogeneous, structured or unstructured, static or dynamic. Using the extracted values, predictions can be made, automated decisions can be taken and models and visualisations can be created that provide more insight into the data.
Key applications
Large, complex datasets impact virtually all sectors of society, such as business, government, education and healthcare. More and more smart devices at home, in the office, in the car, or on our bodies (via sensors and wearables) are generating huge amounts of data. In order to do something with that data, complex analysis must take place with the right tools. Organisations can thus predict and influence your behaviour increasingly easily. This offers significant advantages, but there are also risks, such as the possibility of privacy breaches or filter bubbles. Two European laws - the Digital Markets Act (DMA) and the Digital Services Act (DSA) - that came into force in 2023 impose stricter data collection and retention rules on large technology companies.
Making better use of opportunities
Data science, data analytics and data spaces play a major role in the innovation of services, products and work processes. Smart data collection and deployment will strengthen Dutch economic growth. Commercial companies will get more insight into their customers' (buying) behaviour and consumers will get, for example, customised healthcare, education or offers. Municipalities or the police can use this key technology to make policy or assess risks. Science and journalism can also make extensive use of data science, data analytics and data spaces. Acquiring data poses increasing challenges in terms of secure storage and sharing and analysis of data. These challenges apply to all sectors. Making the most of the opportunities offered by Big Data and making the transition to a smarter, data-driven economy requires new expertise.
Action Agenda
Under the coordination of Digital Holland (formerly Top Sector ICT) — and in close consultation with coalitions and representatives from the field — work is underway on the AI/Data Action Agenda, which stems from the National Technology Strategy (NTS).