Mapping How AI, Graph and Location Technology Trends Are Converging in 2023 and Beyond

Geolocation technologies are evolving at an increasingly swift pace, driven by an uptick in spatial data and analytical tool usage. In fact, the spatial data and analytics market is expected to grow to $119.9B by 2027. 

As geolocation usage expands, so do the adjacent technologies that are both benefiting from and enhancing spatial data. Last year, we wrote a series of predictions about location technology trends that we expected to emerge in 2023. Each of these predictions is deeply rooted in the ways we will expand our use of spatial data to better understand the world. 

Here is an update on where each of these predictions stands today. 

1: We’re going visual with data.

Prediction: Advancements in data visualization will increasingly disrupt how companies approach decision-making, using new tools to derive quick and actionable insights.

2023 Update: We’re seeing the data visualization market growing rapidly and it’s expected to reach nearly $19.2 billion by 2027. One of the engines behind this growth is a focus on data literacy, with data leaders empowering their teams to tap into self-service tools. In fact, roughly three-quarters of organizations currently have a self-service program for data visualization. 

Visual analytics is driving data science workbenches like Amazon SageMaker, making it possible to explore billions of rows of data as easily as directions on a map. It also helps identify clusters of data as easily as points of interest, and build hypotheses quickly. Teams are using new collaboration capabilities to create and share visualizations to solve problems effectively. 

And community-driven initiatives are encouraging the use of data visualizations. For example, we see different sites offering weekly challenges to reverse-engineer a data visualization or to build visualizations with a new dataset each week. 

Read more to understand how data visualization unlocks location analytics

2: AI is advancing, but needs more data to do so.

Prediction: Artificial intelligence (AI) models will continue to eliminate manual tasks that previously took hours, days or weeks, accomplishing those same feats in minutes.

2023 Update: AI is gaining momentum rapidly – both in a technology that powers consumer tools like ChatGPT and in investment among companies. In fact, references to AI during earnings calls with investors are up 77% year over year. Big data processing and machine learning (ML) are becoming much more accessible to smaller or less technical companies thanks to services that allow teams to select, train and tune models in the cloud, then immediately provision a model behind an API. 

Watch this webinar to learn how location data and AI can work together to help unlock insights in financial technology

3: Organizations will evolve from regulation-compliant to privacy-forward.

Prediction: Businesses will use techniques like differential privacy and clean rooms to unlock the power of first-party data while protecting consumer privacy.

48% of data strategy leaders say they are not using location intelligence as effectively as they could be because “complying with privacy regulations is hard.”

2023 Update: The privacy landscape continues to evolve with new regulations that require consent for data collection and restrict data sharing. In 2023 so far, several states have already announced new, more stringent data privacy laws. 

Luckily for developers, more robust, privacy-preserving methods are emerging in parallel, which will allow organizations to comply with new regulations. For example, enterprises are now using clean rooms to query conjoined data sets without revealing their data to others. They’re also using differential privacy, injecting noise into a data set and enforcing a privacy budget on queries to protect the privacy of individuals while detecting patterns in aggregate. 

Using homomorphic encryption, which preserves the consistency of mathematical operations over encrypted data, enterprises can build useful applications with encrypted data at rest, in transit, and in memory. These techniques are all becoming key components of most consumer applications as data strategy leaders place more of a focus on protecting data privacy. 

Foursquare’s recently launched geospatial knowledge graph grounds location intelligence in spatial units. This approach will help address vital business questions using aggregated data, rather than data tied to a phone’s movements, further protecting consumer privacy.

Read more about how Foursquare protects privacy in location data

4: Graph technologies will continue expanding. 

Prediction: Enterprises will increasingly invest in new ways of representing data relationships in order to accelerate time to insights and drive innovation.

2023 Update: Knowledge graphs are network-like databases that are really good at modeling relationships between entities and helping uncover insights and patterns in your data. By 2027, the global graph database market is projected to reach $6.2B.

Graph’s predecessor, relational databases, have relied on firsthand knowledge of how datasets relate to each other. To gather insights, data science teams have to do a series of joins, which are costly and slow. In a graph database, the relationships are baked into the model itself. Now, more than ever before, we are seeing enterprises invest in representing data relationships as a network of nodes, links, weights, and conditions, unlocking insights that would not be evident otherwise. 

Here at Foursquare, our geospatial knowledge graph will transform how companies capture value from location intelligence, extracting patterns and relationships from the movement of people across space and time.

Unifying our primitive datasets and serving as the underlying backbone to Foursquare’s full product suite, FSQ Graph will improve data quality, enable higher velocity innovation, and unlock critical insights. 

Looking Further Ahead

Amidst this expanding technology landscape, more complex and advanced use cases for geolocation technology are emerging. Buyers of geolocation technology report that top use cases include predictive analytics and data science — using the data to power analytics and build ML and AI models to give better predictions and maximize the utility of the data. According to Forrester research, 75% of data strategy leaders say location technology will be equally or increasingly important for customer analysis and segmentation in the next two years.

Learn more about how location technology can play an increasingly prominent role in your organization by clicking here

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