Amit Sinha, Lead Artificial Intelligence and Machine Learning Engineer at TRC
Over the past three decades, there has been a quiet revolution in the business world as geospatial technology has taken on a bigger and bigger role in shaping how companies manage their operations, empower their teams, mitigate risk, plan for the future, and much more. GIS systems have become irreplaceable for organizations across every industry, enabling companies to derive insights from their location-based data that solve business challenges that were previously intractable.
These geospatially-driven insights are so strategic and impactful that our team refers to it as location intelligence. My colleague Todd Slind has been writing a series of columns for Fast Company educating the broader business community about location intelligence, but every reader of GIS User understands the power of this technology. We know that the world has only begun to tap into the power of geospatial technology and location intelligence. The limiting factor has always been the number of geospatial professionals who can help companies maximize the value they can get out of their investments in GIS systems. That is why Artificial Intelligence and Machine Learning (AI/ML) are a gamechanger for the growth of geospatial.
AI/ML is a gamechanger because it provides automation that acts as a force multiplier. With the training, guidance and management of experienced geospatial professionals, AI is capable of dramatically increasing the volume of work that can be accomplished with location intelligence systems. This is the key to unlocking next-gen geospatial at scale, and it will be an enormous positive for GIS professionals and the companies for which they work. Through the automation that AI provides, GIS will be able to take on an even larger role in how companies operate.
Everyone in our industry should energetically take on the role of evangelist for the powerful combination of AI + location intelligence, but we also need to be messengers for an important piece of advice for the companies we work for and with. AI and location intelligence runs on data, and if you don’t have high quality data, the results from your AI investments will suffer.
Organizations need a data strategy that ensures that data is truly AI-ready. And the best data strategy is one that builds processes that achieve this not just at a single point in time before a certain AI project, but continuously over time. Here are key questions that we should all be asking to ensure that our data sets us up for success:
- Is Your Data Accessible and Available When It Is Needed?
This is critical because so many organizations have data siloed within poorly connected systems and departments across operations. Conducting a comprehensive data audit to identify silos and break them down is a must. But it is not enough just to make sure data is accessible. It needs to be accessible exactly when you need it, without latency and delays. This is because so many legacy systems were not built for real-time reporting, creating a situation where you may be able to access a given batch of data but it will be old and dusty by the time you get it. In utility OT systems, for example, data that is only periodically reported can sit in limbo for 12 hours, an entire day or even a full week awaiting batch reporting and processing. Similar dynamics exist in the data reporting of legacy systems in many industries. This is sub-optimal for AI, which operates best when the data it is processing is as close to real-time as possible. For this reason, eliminating data silos and modifying data flows to reduce data latency should be pillars of making your data AI-ready
- Does the Data Exist?
One of the benefits of conducting a comprehensive data audit like the one I describe above is that organizations will often discover that some of the data they need simply does not exist. Despite major investments in data creation and aggregation, there may be blind spots where vital data that is necessary for the success of an AI initiative does not yet exist. In the utilities industry, for example, substations are often a “black box” that does not deliver data about the equipment it contains, the operation of that equipment, etc. This is not uncommon in industries where organizations manage large-scale infrastructure. Shining a light on those black boxes may be important for having the data that you need for the AI use cases being planned.
- Is Your Data Accurate and Rich Enough?
Even if data exists and is accessible in a timely way, it still may not be AI-ready. That is because not all data is high-quality. It can fall short in terms of accuracy. Or it can lack the depth it needs. Or both. Every geospatial professional knows that data accuracy and richness can vary greatly based on how the data was collected. For example, a data point captured by a field worker using a mobile tablet with 3D scanning might check every box for accuracy and precision. But another data point may be from a legacy system based on handwritten notes on paper maps and reports that are years or decades old. That second data point may fall far short of the threshold for accuracy and richness, which makes it a liability for AI initiatives. For that reason, organizations need a data strategy that assesses quality and augments accuracy and richness. With the latest technologies and techniques, you can rapidly improve the quality and richness of both existing data and new data on an ongoing basis. One of the ways to do this at scale is through AI-driven data conflation, which automates the process of identifying and enhancing specific data that needs improvement.
- Is Your Data Compliant?
After you make sure it exists, is accessible, is accurate enough and rich enough, your data strategy must also address compliance. Regulations like GDPR in Europe and state-level regulations in the U.S., such as California’s privacy law, make it critical to track ownership of data, which determines whether it can be used in AI initiatives. Large language models that mistakenly use private data will need to be revised or even destroyed – leading to costly setbacks in AI initiatives. To mitigate the risk of using data that should not be fed into AI models, organizations need a data strategy that makes it possible to not only track the provenance of data but also manage that data over time.
By understanding the critical role of data quality and these best practices for making data truly AI-ready, geospatial professionals can be effective advocates for steps that will help organizations ensure the success of their AI + location intelligence initiatives. We are poised for a new golden age for geospatial technology, and each of us has an important role to play in helping it become a reality.
About the Author:
Amit Sinha is the Lead Artificial Intelligence and Machine Learning Engineer at TRC, a global professional services firm providing integrated strategy, consulting, engineering and applied technologies in support of the energy transition. In this role, he develops innovative applications of AI and ML technologies for gas, electric and water utilities as well as a range of other client companies. His entire career has been devoted to extracting deep insights from data to solve business challenges, including his prior roles at Esri and DoorDash. He has a BS/MS in Engineering and a Ph.D. in optimization, simulation and automatic differentiation in Artificial Intelligence and Machine Learning from the Indian Institute of Technology in Bombay.
