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GIS and Data Science Don’t Speak the Same Language
In recent discussions with industry leaders and as outlined in our recent thought leadership piece “Evolution of Real Estate Network Planning in the Age of AI”, the lack of collaborative synergy among Analytical Teams - particularly between geographic information science (GIS) and data science (DS) teams - separates organizations that have an AI strategy and have invested in “Centers of Excellence” around data and machine learning Operations (MLOps) from the rest. Other than AI High Performers, this emerges as a pronounced gap in most organizations we spoke with pertaining to their AI and advanced analytics capabilities and is recognized as a significant hurdle to progress.
Analytical teams, even within some enterprise setups with a stated AI strategy, often function in isolation when a “Center of Excellence” is absent. DS teams concentrate on optimizing temporal (time series or cross-sectional) data like loyalty or online orders for customer insights that drive marketing strategy efforts, supply chain and inventory optimization, and pricing and promotion strategy. On the other side, GIS teams predominantly own the spatial data sets and are regularly deployed by Real Estate departments to furnish store site reports and trade area analyses to support site acquisition and development approvals.
The collaboration between the two groups of technical specialists, for those organizations that have both, remains limited - with the exception of the AI- High Performer segment. The communication between the two groups is usually limited to “data requests” by DS teams from GIS teams and vice versa. For example, retail DS teams who support marketing or pricing need general demographic data to merge with customer loyalty data at the postal code level to target non-customers with similar spending power and preferences with direct marketing programs to drive traffic into the stores. It is rare that GIS teams and DS collaborate on holistic problem-solving partnerships that would optimize the use of ALL data and talent.
This may, in part, be due to the fact that GIS Teams are specialized and typically don't speak the same language as MLOps analysts. They have been working with geospatial data for years, sustained by specialized tools (e.g. ESRI/ArcGIS and a host of others), and have not evoked a sense of urgency for AI integration. The business case has not been made!
In an interview with Dr. Wendy Keyes Weniger, Principal Data Scientist: Spatial Science and Big Data Analytics Team at ESRI, highlights the point that GeoAI is the product of these paradigm shifts mentioned above. In this interview, Dr. Keyes Weniger highlights this challenge:
“....the lack of intersection <of data science and GIS> tends to be the problem. GIS professionals now have access to analytical tools well beyond the classic capabilities they trained on. And most data scientists haven’t been trained to understand spatial analysis. To make things more challenging, each group uses a different language, and even when their vocabulary overlaps, the words often mean different things.”
This divide is rooted in the methodologies employed— traditional GIS leans on structural regression models, while the new wave of DS practitioners rely on Machine Learning tools for leveraging temporal or cross-sectional datasets. Some have admitted that even the language used within the two teams is unique, adding to the intellectual and communication divide. One factor contributing to this divide stems from universities and colleges that traditionally compartmentalize education in these fields, hindering the cross-pollination of technical proficiencies. Educational institutions predominantly focus on GIS training in sectors like urban planning, environmental studies, and government sectors, neglecting its applications in a business context, like retail goods and services, franchising in Quick Service Restaurants (QSRs), and financial institutions where the necessary business acumen would be acquired. Consequently, as is often the case in more technical subject areas, graduates often lack the business knowledge required for these sectors, necessitating extended on-the-job learning periods and further complicating the bridge-building required to succeed. This educational gap leads to challenges in recruiting fresh talent equipped with the amalgamated skill sets demanded by the industry.
In addition, organizational silos further exacerbate this by separating the functions of GIS and DS, prolonging the learning curve and necessitating deliberate investments in on-the-job training and interdisciplinary knowledge transfer. As GeoAI gains traction, GIS and Data Science teams will be forced to work together more directly and invest in technologies and cross-education within a business context to build and enable network scenario planning to inform strategy. We heard from several executives, who we called “AI High Performers”, who have taken it to the next level by building a “Center of Excellence for GeoAI” as part of their AI strategic imperative to support growth and optimize brick-and-mortar networks. Over time, the barriers to the adoption of GeoAI capabilities will come crashing down for other organizations. A few educational institutions (e.g. University of Southern California and PennState ) are already offering specialized Spatial AI programs demonstrating the need to build new skill sets for the modern world.
At PiinPoint, we have built a “center of excellence” around Data Science and Spatial Analysis. Our team works together hand in hand on GeoAI applications as the foundation of our software and go-to-market. We recognized early that providing an easy-to-use and intuitive spatial analysis platform that embeds GeoAI model systems for our clients was an unmet need. In fact, as a deliberate part of our implementations and delivery of custom modelling approaches, our Network Simulations engine allows clients to simultaneously string together time-based real estate actions such as opening a new store, closing, renovating, consolidating two or more stores and quickly predict the net impact on top line and profitability of the network. Client feedback is then incorporated into future rounds of simulation to ensure we are providing a good range of possible outcomes and promote strategic discussion by executives. This collaboration with clients is transparent and builds trust in the system and ultimately builds lasting relationships.
Check out PiinPoint. PiinPoint exists to support our clients' need for accurate and timely input to their real estate approvals discussions to increase their trust in their Real Estate market analysis, forecasting and network planning processes powered by GeoAI.
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