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Three Ways Graph Databases Can Revolutionize Geospatial Data

Graph Databases Geospatial Data

Graph Databases Geospatial Data

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise business software. In this feature, Foursquare‘s VP of Engineering Ankit Patel offers commentary on the ways graph databases can revolutionize geospatial data.

In today’s competitive business landscape, leveraging data effectively is critical to success. The vast amounts of data generated can offer key insights and drive informed decision-making, but only if businesses are able to cut through the noise and reveal the meaningful patterns hidden within.

As the digital landscape evolves, businesses must stay agile and adapt to new technologies that can help them stay ahead of the curve. The ability to identify and understand the connections between data points is essential for businesses seeking to harness the full power of their data. This is especially true when dealing with geospatial information, as the relationships between locations, time, and events can reveal critical trends and patterns.

One such technology is the combination of graph databases and geospatial intelligence, which allows organizations to gain deeper insights into customer behavior, supply chain efficiencies, and market trends. This potent fusion empowers businesses to make data-driven decisions with greater speed and accuracy, ultimately translating into improved customer experiences, increased revenue, and sustainable growth. By embracing this innovative approach to data analysis, companies can unlock the full potential of their data assets and create a strong foundation for continued success in an increasingly data-driven world.

By integrating graph databases and harnessing the power of geospatial intelligence, businesses can uncover new opportunities, optimize operations, and gain a competitive edge in their respective industries. Graph databases change the game because they view data points as relationships, rather than individual tables and documents. By intuitively calling attention to relationships in seemingly unrelated datasets, graph technologies help data scientists spend less time cleaning data, and more time discovering invaluable insights.

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Graph Databases & Geospatial Data

Transform Your Business Strategy with Geospatial Intelligence

Businesses across industries are using unique insights derived from geospatial data to make smarter, more efficient business decisions and to improve customer experiences.

One example is the analysis of social media posts in combination with GPS data. With the right analysis and interpretation, a business can gain insight into customer behavior and preferences when joining the sentiment from public social media posts with the location they are geotagged in. With a graph database, these data points are intuitively linked together to increase accuracy and enhance attributes. Consumer insights extracted from the data can then be used to make decisions, such as where to invest in a new storefront location or what inventory to stock up on for the season.

Leaning on graph technologies can help businesses unlock the power of geospatial data in three important ways:

Streamlining Geospatial Data Analysis

Often, 80 percent of a data scientist’s time is spent searching and cleaning data, while only 20 percent is actually performing analysis and discovering insights. In addition to this time and personnel commitment, analyzing complex geospatial data traditionally requires specialized resources and tools, such as GIS software.

With a graph database, teams can leverage the power of graph analytics to perform complex queries and analysis, without the need for specialized resources. This significantly reduces the time it takes to gain insights from geospatial data, and minimizes the laborious hours spent aggregating and cleaning datasets, thus freeing up strategic work on initiatives that impact the business bottom line.

Take telecommunications and network planning as an example. Graph databases can examine the relationship between communication nodes, network infrastructures, and geographic locations to optimize network performance and coverage. This enables more efficient planning and deployment of network resources, saving time compared to traditional relational databases that require extensive data preparation and manual queries.

In solving for the stringent preparations that go into data analysis to free up a data scientist’s time for value-focused work, the graph database accelerates time to insights.

Unlocking Geospatial Data Insights

The potential value of what geospatial data could deliver to every department within an organization is massive. However, historically, business users have had to rely on highly-skilled data science and engineering teams to derive and understand insights relevant to the decisions they’re making.

By simplifying complex data processes and making interpretation easier, a graph database allows non-technical users to gain insights from data without needing to rely on specialized data analysts or engineers. With a graph database, employees at every skill level and in every department are empowered to make more informed, data-driven decisions in their daily work.

Lowering the barrier to entry for geospatial data analytics also reduces strain on engineering and IT teams, lending to improved agility and responsiveness in a competitive business environment.

Breaking Down Data Silos

One of the biggest challenges in working with geospatial data is the fact it is often stored in disparate systems or databases, making it difficult to access and join effectively. Data silos slow down the analysis process, and lead to insights lacking accuracy or that may not paint a comprehensive enough picture by the time they’ve reached fruition.

Real estate is one industry where having geospatial data locked away in silos can be detrimental to business success. Real estate companies often deal with data from various sources, such as property listings, market trends, demographics, and geographic information. By congregating this data in a graph database, real estate firms can access a holistic view of the market. This enables more informed decision making and provides better services, leading to happier clients and overall business success.

Any business, from real estate to retail and supply chain management, can use a graph database to consolidate different geospatial datasets into a single, interconnected data model that can be accessed by multiple teams. This facilitates collaboration, enables deeper insights and ultimately supports common business goals. While traditional databases dilute data into separate documents and files, graph databases break down data silos to foster connections between data points.

The Future is Graph

By streamlining the data analysis process, democratizing access to valuable insights and breaking down data silos, graph databases enable organizations to harness the full potential of their data assets. As the digital landscape continues to evolve and the importance of data-driven decisions intensifies, the adoption of graph databases and geospatial intelligence will become increasingly essential for staying ahead in an ever-competitive business world. Embracing this cutting-edge technology is a strategic move that will propel businesses forward, driving growth, innovation, and long-term success in the era of big data and beyond.

In fact, Gartner predicts that graph technology will be used in 80 percent of data and analytics innovations by 2025. It’s clear to see that the future of data analytics is being shaped by technologies like graph databases.

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Ankit Patel
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