GWPrime

Data in Context

Just like oil needs refining and gold has to be extracted from ore, knowledge needs to be extracted as a commodity from data before being useful in decision support.

Data has often been described as the “new gold”. Sometimes, the metaphor uses ‘oil’, though having a choice, gold for sure would carry a stronger message in today’s time. But there are limits to these comparisons — oil as well as gold are limited, non-renewable resources, and their value is driven by demand and scarcity.

To the contrary, there are no limits to the volumes of data, with the value increasing with supply — it even grows with sharing. Does a lot of data make us rich? Not really, and certainly not immediately. As a final parallel with natural resources, just like oil needs refining and gold has to be extracted from ore, knowledge needs to be extracted as a commodity from data before being useful in decision support — just like energy from oil. Data science, which has become a hugely popular profession in recent times, promises to help with that. But do we get the full potential value from data by a “refining process”, by pattern recognition, categorization, condensation and visualization?

Adding value from context

To extract not only implicit and latent meaning, but also add real value to data, it needs to be placed in context. Measuring weather, or customer behavior, or health or biological production will lead to interesting insights, but only when properly related to its respective environment and subjected to temporal and spatial comparisons.

Data as such, therefore, is as useless as a table of numbers without row and column headings. Insights derived from analyzing data in full context are pure gold, though. It so happens that Geography is the most established “context discipline”. It does not come as a surprise, then, that Spatial Data Science is increasingly recognized as a leading contributor to evidence-based decisions. Environmental management, Climate Change mitigation, public health and virtually all other domains of policy and business decisions are based on insights from spatial analytics.

If this is the key to adding value by extracting information and insights from all the data collected today, how do we put data into relevant contexts? It is as simple as using location as a key for joining otherwise independent datasets. Connecting by location is the basic principle behind the fundamental concepts of Geographic Information Science and Geoinformatics as its methodology.

Pervasive location services tag all measurements with location, enabling full contextualization and subsequently analysis-in-context. Positioning services, whether through GNSS or indoors, therefore, are one of the key enablers for creating new insights. The second pillar of in the foundation of Geoinformatics are Cloud services, facilitating the live confluence of data streams. Identity management serves as the number three requirement, e.g., connecting a series of position readings into a movement trajectory. Not surprisingly positioning, Cloud services and identity today are core domains of standardization, enabling connecting and contextualization of data.

To extract not only implicit and latent meaning, but also add real value to data, it needs to be placed in context. Measuring weather, or customer behavior, or health or biological production will lead to interesting insights, but only when properly related to its respective environment and subjected to temporal and spatial comparisons

The other side of the coin

This enormous power of creating insights through context at the same time has led to perhaps the biggest risk paralleling the undeniable positive potentials. The dragnet of individual data analytics, whether for commercial or political reasons, is a very real threat to a liberal and democratic society. Fully connected open and private data enable participation and communication, but also require precautions when using the power of connecting by location.

Clearly, legitimate uses and potential abuses always come along with progress and innovation. The geospatial paradigm has contributed to an important step across multiple scientific and business interests: the separation of semantic from spatial domains, of tabular records from maps is being overcome by linking all data by location to their respective relevant contexts.

Today we are tempted to illustrate this principle of “location first” for contextualizing data by looking at pandemic dashboards. Soon, hopefully, other spatiotemporal dynamics will be at the center of attention, like mobility and travel, business and trade, and individual happiness from social interaction!