AI has moved past novelty status. It is now fundamentally altering how organizations engage with data, and location data is no exception.
For engineering, product, and analytics teams, it’s no longer a question of if AI will influence workflows, but how deeply it will be embedded into core systems. This shift isn’t just about usability for non-technical users: it’s about reshaping how data is structured, queried, governed, and productized.
From SQL to Semantic Layers: Data Must Speak the Model’s Language
At its core, generative AI allows stakeholders to interact with data through natural language, expanding access beyond traditional dashboards and SQL. But for technical teams, this abstraction introduces new demands. Data must be modeled with clarity and context, structured in ways that language models can reliably interpret. It elevates the importance of metadata, naming conventions, and semantic modeling while introducing stricter demands around access control, lineage, and explainability.
Plugging an LLM into a dataset may yield quick wins, but building scalable, trustworthy systems requires disciplined work behind the scenes. Engineers and analysts must ensure that underlying datasets are clean, well-structured, and monitored for drift or bias. The semantic layer becomes not just a convenience but a critical interface between raw data and AI-driven applications.
Case in Point: Structuring Location Intelligence for AI
Unacast uses AI with these challenges in mind. That means surfacing human mobility patterns, like dwell time or trade areas, in context-rich formats optimized for AI systems. The objective isn’t just faster answers, but deeper, AI-assisted insight generation that can drive decisions in retail, real estate, and site selection use cases.
A core part of Unacast’s product offering is its aggregated foot traffic data, which is derived from GPS signals captured via privacy-compliant mobile SDKs. For this specific product, however, any signal identifier is removed, meaning all the data modeled is 100% anonymous. Because raw signals alone aren’t actionable, Unacast applies a series of machine learning models to classify visits, infer dwelling locations at the census block level, distinguish passersby from true visitors, and stitch patterns of movement into meaningful behavioral metrics.
This enriched foot traffic data becomes the foundation for analytics like store visitation trends, cross-shopping behavior, and trade area mapping. By layering machine learning models on top of deterministic visit data, Unacast enables its customers to understand not just where people go, but when, how often, and why. As generative AI interfaces make this data more accessible to non-technical users, the underlying accuracy and sophistication of these machine learning processes become even more critical to delivering trustworthy insights.
Governance and Responsibility at Scale
Generative AI also accelerates a long-standing trend: business users gaining more direct access to analysis. This places additional pressure on technical teams to enforce governance while enabling exploration. It also shifts analytics workflows from reactive (build a dashboard, wait for interpretation) to interactive (prompt, interpret, refine)—bringing human judgment and iteration to the forefront.
Some immediate technical implications include:
- LLM-Ready Data Design: Dimensions, metrics, and relationships must be modeled with clarity and described in terms models can parse—often requiring richer metadata and documentation.
- Governance Reinforcement: Row-level security, permissioning, and data lineage are more important than ever as more users engage with sensitive data via plain language.
- Bias and Hallucination Safeguards: Outputs must be stress-tested, especially when used in strategic decision-making or regulated environments.
The Future: Human-AI Partnerships in Analytics
For data professionals, generative AI isn’t about displacement—it’s about amplification. Teams that invest in clear data modeling, governance, and semantic readiness will not only enable better insights, but they’ll unlock entirely new forms of interaction between data, models, and users. The stack is evolving—and the teams that evolve with it will define the next generation of analytics.
