GISCafé Industry Predictions for 2023- Korem

By Jonathan Houde, CTO, Korem

Korem’s Top 3 Key 2023 Trends for Enterprise Geospatial

Jonathan Houde

There is no shortage of evolution in the geospatial industry. It has grown tremendously in the past years, even creating subsegments including data acquisition, the use of more advanced drones, advanced immersive geospatial data visualization that leverages digital twins integrated with geospatial game engines, and even AR/VR technology.

However, today, we will focus on 3 key trends related to the increased momentum and evolution of geospatial within the enterprise ecosystem: data privacy, cloud-native, and the death of stand-alone GIS platforms.

New data privacy considerations with the increased use of big data and AI

There is an increase in companies specializing in the productization of derived data, enabled by big data processing and advanced AI capabilities. This can include feature extraction from imagery data, vehicle traffic data gathered from connected cars, or foot traffic data acquired from cellphones.

This not only leads to rapid market changes with new players, mergers, and acquisitions but also to other types of market disruptions, such as reinforced data privacy concerns and regulations. Indeed, the collection, sale, purchase, or use of external data, but also internal data from loyalty programs, for example, are not always compliant with data governance.

Some are able to de-anonymize anonymous data, while others go so far as to collect data illegally without obtaining user consent. And this can happen even to the biggest companies. Think of Google or Tim Hortons, which have illegally collected data on the whereabouts of their users, even after they refused to share their location.

It’s no surprise to say that not respecting data privacy considerations has a direct impact on a company’s reputation and can potentially result in legal risks. Hence the importance of dealing with a neutral geospatial partner who can provide sensitive data advisory and ensure compliance with the terms and conditions of each data-sharing agreement.

Enterprise geospatial truly becoming cloud-native

We strongly believe that the derived datasets that will be in regulation with data privacy will bring a lot of value to businesses. However, these typically involve massive amounts of data that change at a high velocity. It also involves highly complex processing to integrate the data into a data model to ultimately make business decisions.

To alleviate that complexity and increase the adoption of these types of datasets, companies will need to either migrate to a next-gen cloud data warehouse or use a new form of Data as a Service (DaaS) that can transform, model, and integrate these massive datasets into tailored and fit-for-use datasets that are ready to answer specific business problems.

Many companies are already familiar with or looking into next-gen cloud data warehouse technologies such as Snowflake, Databricks, Redshift, or BigQuery as a new way to implement advanced data analytics and, ultimately, data-driven decisions.

Unfortunately, geospatial still often comes as a second thought in digital transformation programs. Even if the idea of running geospatial workload into the Cloud for increased agility and scalability has been around for several years, the adoption level is under our expectations. This can be explained by many factors like the lack of expertise and manpower to address all business priorities, the relatively high cost of running data and compute-intensive solutions in the Cloud, and the level of maturity of the geospatial cloud-native technologies.

Considering that around 85% of business data have a geospatial component, using a cloud data warehouse as a centralized data repository implies that it becomes, to some extent, a geospatial data repository. Therefore, the data warehouse needs to support geospatial capabilities traditionally found in GIS platforms and servers.

Built-in geospatial cloud data warehouse capabilities are becoming more common but are still limited and not on par with the true geospatial capabilities needed. This prevents companies from fully realizing the potential of these platforms and many are stuck in the evaluation or POC stage because they are not able to provide the ROI required to move beyond small initiatives to full fledge enterprise solutions.

On the bright side, we’ve seen a significant increase in focus from both commercial geospatial technology vendors and the open-source community that recognize the importance of cloud-native GIS. There are new platforms based on Kubernetes, geospatial add-ons for cloud data warehouses and cloud big data technologies, as well as other forms of cloud-native integration or connectivity.

To name a few:

• Carto Platform and Carto Analytics Toolbox for cloud data warehouse
• HERE Technologies new Location Platform based on Kubernetes
• FME Server Kubernetes Deployment
• ArcGIS Enterprise for Kubernetes
• Precisely suite of cloud-native SDKs, both on Kubernetes and big data environments such as Spark Databricks, and its new Data Integrity Suite
• Alteryx Cloud Designer based on Trifacta technology
• PostGIS increasing geospatial capabilities, broadly available in cloud environments
• Many ongoing open-source projects, such as Apache Sedona or Mosaic

While this is great news for the adoption of geospatial in the Cloud and will allow an acceleration in the full-scale adoption of these technologies, this also means, in the upcoming years, navigating this rapidly changing technology landscape will be a challenge for customers.

The death of stand-alone GIS platforms and the rise of geo-enablement for all

Several years ago, we started to see BI tools integrating geospatial capabilities such as spatial operations and map visualization as standard features.

Since then, native geospatial capabilities have become a key component of so many technologies whether it is commercial real estate platform, 5G network planning platform in telco, or core policy systems for the insurance industry. Even skills will be altered, now needing spatial and location analytics competency in data science and analytics practices, or ability to manage geospatial data for data integration software and specialists. The same is also starting to apply to other areas such as Big Data as well as cloud data engineering.

All of this will collide with the old paradigm of centralizing all geospatial data into a single platform and the geospatial expertise into a single department and will rather push company to adopt a decentralized geospatial model, while making sure to preserve a coherent geospatial ecosystem that include governance as well as data and system integration. Ultimately, the access to geospatial technology in any type of system and the need for geospatial skills in many professions will continue to expand.

Accelerated adoption of geospatial

These trends will impact the way geospatial technologies are used within the enterprise ecosystem. The driver for these is not only the technology hype but is also related to heavy macroeconomics trends that geospatial can address.

Whether it is labor shortages that require an enhanced level of process automation, 5G qualification, insurance risk exposure that calls for a higher level of accuracy, fleet management that needs further optimization to reduce CO2 emission and counter the inflated fuel and labor cost, or retail market post-pandemic disruption, this will increase the need for geospatial technology and data. Those should accelerate the adoption of geospatial for business across several industries.

About the author

Jonathan Houde is CTO at Korem.

Jonathan is responsible for technical leadership and innovation, further developing the company’s technical community, and aligning its software strategy, architecture, and partner relationships to solve business challenges and deliver the best customer value. With his extensive experience across many different technologies, Jonathan and his team are able to build technology solutions for complex projects across different industries. He is known for his ability to quickly identify customer requirements and translate them into recommendations and realizations that can range from simple “out of the box” technologies and data integration to very advanced custom developed solutions.

Tags: Geospatial

Category: Industry Predictions

This entry was posted on Monday, January 23rd, 2023 at 10:37 am. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.