Spatial data infrastructure (SDI) is all about forming and utilizing a virtual representation of the real world. Advances in sensor and positioning technology allow for collection of data creating still more sophisticated, timely and accurate representation of real-world objects, and advances in communication, storage and processing technology let increased volume of data be utilised, support increased variety of data types being combined, and allow for increased velocity in creating, updating and analysing data. SDI is becoming location-enabler for a fully-fledged data infrastructure and a corner stone in data-driven innovation. This development represents unprecedented opportunities for SDI – and challenges competences and the ability to collaborate and partner with players in a wide variety of ecosystems.
Spatial data collection
Improved methods and tools for data collection are changing our ability to create a virtual representation of the real world. Global navigation satellite systems (GNSS), such as GPS and Galileo, make positioning universally available and simple to use, and sensor technology is the foundation for surveying technologies such as laser scanning (LIDAR) capturing 3D point clouds, and digital imaging cameras and scanners capturing information in a broad spectrum of wavelength ranges. Using drones, airplanes and satellites as vehicles to carry equipment allow surveying tasks to be scaled to purpose, and broadband communication conveys data fast from point of capture to point of use.
Said improvements in methods and tools for spatial data collection in combination with improvements in how data is stored, processed and made available to users, drive how the virtual model of the real world evolves to best fulfil user requirements.
Spatial data content
SDI includes a large volume of spatial data in a large variety of data types, and the content expands due to technology improvements and continuous data collection. Giving a full description of SDI data content is outside the scope of this article, the following is a few highlights that illustrate important developments.
Due to cost considerations 2D models are traditionally often used for virtual representation of the real world. This obviously sets undesirable limits for how the virtual representation can be utilized. With improved methods and tools for positioning and data capture, the third dimension is now much more affordable and 3D models are increasingly used.
The bulk part of data content in SDI has traditionally been vector features using points, lines and areas to represent location of real-world objects. Although the relative significance of vector features diminishes as more spatial data types gain popularity, they are still a key element in SDI and essential when it comes to SDI serving as location-enabler for other data describing real-world objects.
Georeferenced point clouds have become important elements in SDI and are source for SDI 3D developments such as improved elevation models holding matrices of elevation values describing terrain and surface, and 3D city models adding buildings and other landscape elements belonging to urban areas.
Imagery adds image sensor information to the virtual representation of the real world. With improved methods and tools to collect, store and process imagery, image data is becoming an increasingly popular part of SDI. Imagery can be both ‘map’ rectified images and sensor model images that can be used, for example, for precision measurement or to construct 3D models of ground structures. Examples of image types in the SDI are orthoimages, oblique images and panoramic images.
Spatial data handling
Spatial data comes from many sources and is used within many domains. A goal for SDI is that spatial data is stored, made available and maintained at the most appropriate level and that it is possible to combine spatial data from different sources and share them between several users and applications.
To reach this goal spatial data in SDI is provided as a service on demand to the user regardless of geographic or organizational separation of provider and consumer, i.e. in a network of connected players.
Data and the corresponding services must fulfil user requirements. This can only be achieved via strong governance combined with sound business incentives and close cooperation between the players in the SDI ecosystem.
Seamless sharing of data requires robust technology standards. Open Geospatial Consortium (OGC)  plays an important role in defining and continuously develop the standards used in SDI.
To align these standards with mainstream ICT standards, OGC and the World Wide Web Consortium (W3C) collaborates about ‘spatial data on the web best practices’ targeting ‘those who publish spatial data through SDI and want to better integrate that data within a wider web ecosystem’ . OGC is modernising its web service standards for retrieving, creating, modifying and querying spatial data on the web to adhere to these best practices.
Cloud computing is shared pools of configurable computer system resources and higher-level services that can be rapidly provisioned with minimal management effort, often over the internet. Cloud computing relies on sharing of resources to achieve coherence and economies of scale. Cloud computing is traditionally categorised in three models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Cloud computing based on these models is part of many SDI components.
Data as a Service (DaaS) is an emerging cloud computing model. With service-oriented architecture (SOA) and the widespread use of application programming interface (API) the actual platform on which the data resides doesn’t matter. With DaaS, any business process can access data for use and/or modification wherever it resides. The cloud computing DaaS model supports the SDI data sharing approach and represents a strong model to be used in SDI implementations.
SDI in the bigger picture
SDI is location-enabler for data in general, both with respect to geocoding objects that can be linked to georeferenced objects, and when it comes to provide the spatial dimension in a data space that can be used to analyse spatially related scenarios.
As such SDI is part of a common data space that forms the foundation for the data-driven society based on a seamless digital data infrastructure that enables the development of new products and services based on data.
Utilizing data to their full potential means that data can be embedded in the generation and use of huge volumes of data – commonly referred to as ‘big data’. According to Organisationfor Economic Co-operation and Development (OECD), big data is a core asset for data-driven innovation, and data-driven innovation forms a key pillar in 21st century sources of growth .
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Big data is often described via three defining properties or dimensions: volume, variety and velocity (the three Vs). Volume refers to the amount of data, variety refers to the number of types of data and velocity refers to the increased pace of incoming data. The three Vs together describe a set of data and a set of analysis conditions that define the concept of big data and label the data space in which SDI must fit.
Big data is used by organisations to optimise planning and operation. Big data utilizes spatial data and services from SDI and combine these with other data and services, potentially including streamed data coming from internet of things (IoT) devices. Big data analysis takes advantage of artificial intelligence (AI) such as machine learning and can examine large amounts of data to uncover hidden patterns, correlations and other insights.
SDI in digital transformation
Digital transformation is the transformation of business and organizational activities, processes, competencies and models to leverage the changes and opportunities of digital technologies and their impact across society.
As spatial data moves from a relatively closed environment into mainstream ICT and serves as location-enabler for a wider web ecosystem, collaboration needs to take place on a much wider scale. Where collaboration used to be limited to geo-savvy players, moving forward collaboration includes mainstream ICT players, and with the role of SDI in big data, collaboration also extends to environments around emerging IoT and AI technologies.
Collaboration is not only between people, but also between organisations in a wider web ecosystem. In digital business, organisations must partner in order to complete digital offerings and jointly fulfil customer needs. This contrasts traditional business that is supported by vendors that are often detached from their customers’ business outcomes.
Changes are profound and challenges the competencies available: new technologies need to be understood and mastered and business models change from a relatively narrow customer focus to a broader ecosystem focus. Substantial investments must be made in developing the human skills needed to drive the changes.
The changes and trends mentioned are drivers for one of the most significant changes happening in digital business: the digital platform revolution, where technology enabled business is developing around so-called digital platforms, disrupting traditional ways of doing business. 
Data infrastructure can benefit from the digital platform business approach  and SDI has an important role to play as location-enabler for a fully-fledged data infrastructure and a corner stone in data-driven innovation . Such digital business transformation can help release the potential of the described digital technology transformation.
 Platform Revolution: How Networked Markets Are Transforming the Economy—and How to Make Them Work for You. Parker, G., Van Alstyne, M.,& Choudary, S. (2016)