Savvy by Cohesion

Building a Data Product for Real Estate Decision-Making

Executive Summary

https://www.cohesionib.com/products/savvy

Savvy is Cohesion’s analytics and AI platform designed to turn fragmented building data into clear, actionable insights for real estate leaders.

I led the development of Savvy as a data product, defining its strategy, data architecture, analytics layer, and AI capabilities. This included building the semantic data model, designing analytics workflows, shaping the AI assistant, and driving go-to-market execution.

The result was a platform that:

  • Centralizes data across building systems, sensors, and business tools

  • Enables both self-service analytics and AI-driven insights

  • Helps customers make decisions around cost, occupancy, space, and operations

  • Establishes a foundation for digital twin, predictive analytics, and automation

What Savvy Is

Savvy is a unified analytics product that combines dashboards, AI, and automated insights into a single interface.

At a product level, it consists of three core components:

1. Customizable BI Dashboards

Interactive dashboards embedded directly into the Cohesion platform.

  • Portfolio, building, and tenant-level views

  • Metrics across occupancy, space utilization, energy, and operations

  • Configurable per customer, with dashboards enabled based on use case

2. AI Assistant

A conversational interface that allows users to query their building data in plain language.

  • Backed by a custom AI agent

  • Translates natural language into structured queries against the datamart

  • Returns contextualized answers combining multiple data sources

3. Automated Insight Summaries

Pre-generated insights that surface key patterns and anomalies without requiring user input.

  • AI-generated summaries embedded directly into the platform

  • Highlight trends like underutilization, cost inefficiencies, or unusual activity

  • Designed to proactively guide users toward decisions

Why this matters

This structure allowed Savvy to serve both:

  • Analysts → deep, self-service exploration via dashboards

  • Non-technical users → fast answers and guidance via AI and insights

Instead of forcing users to “go find answers,” the product meets them at multiple levels of sophistication.

The Problem

Real estate teams weren’t lacking data, they were overwhelmed by it.

Data lived across:

  • Access control systems

  • Occupancy sensors

  • Reservation platforms

  • Energy + utility systems

  • Financial and leasing tools

But:

  • It was inconsistent and fragmented

  • Required manual analysis

  • Was not accessible to decision-makers

Savvy’s core premise was simple: Bring all building and business data into one place, and make it usable for real decisions.

My Role

I operated as the Product Manager for “data as a product”.

That meant working across:

  • Product, engineering, data, and design

  • Executive leadership (CEO, CTO, VPs)

  • Sales, marketing, and implementation

  • Enterprise customers

Key responsibilities:

  • Defined data product strategy and roadmap

  • Designed semantic data layer + datamart

  • Led analytics and dashboard design

  • Contributed to AI assistant (prompt + query logic)

  • Drove GTM, demos, and customer adoption

Approach: Start With Decisions

Most analytics products start with data and build dashboards. My approach started with the user benefit:

Start with the decision (or automation) → work backward to the data

Example:

  • Decision: Should we reduce space?

  • Data Needed: Occupancy (badge swipes or occupancy sensors) + reservations + cost

  • Insight:

    • Department attendance

    • Cost per occupant

    • Underused space

    • Comparisons across office portfolio

Example:

  • Decision: Where are we overspending?

  • Data Needed: Energy and utility usage and spend + operational line items + occupancy

  • Insight:

    • Energy use per occupant (across portfolio)

    • Operational cost per occupant (rent, office investment, etc.)

    • Inefficiencies across portfolio normalized by occupancy

Building the Data Foundation

The hardest part of Savvy wasn’t dashboards or AI, it was building a reliable data layer underneath them.

We built a full analytics stack centered on Databricks as the warehouse, with structured pipelines, a governed data model, and a Power BI semantic layer on top.

Sources → Raw Tables → Datamart (Databricks) → Power BI → Dashboards + AI + Automation

Ingestion & Transformation

Databricks served as the central data platform.

We ingested data from:

  • APIs (access control, utilities, sensors)

  • IoT streams (occupancy, IAQ)

  • Internal systems + CSV uploads

We structured this using a medallion architecture:

  • Bronze: raw data

  • Silver: cleaned and standardized

  • Gold: analytics-ready datasets

This ensured data was consistent, traceable, and reusable across use cases.

Datamart: Structured for Analytics

On top of this, we built a Databricks datamart with two layers:

Canonical Tables

  • Raw + normalized fact and dimension tables

    • Examples: badge swipes, reservations, service requests

  • Joined via keys like Building_Code, Company_Code, Local_Date

This formed a snowflake-style model with shared dimensions across all datasets.

Aggregated Tables

  • Pre-calculated datasets (occupancy, utilization, visitors, etc.)

  • Designed specifically for analytics performance

This pushed heavy computation upstream and made dashboards fast.

Power BI: Semantic Layer & Measures

Power BI sat on top of Databricks and handled:

  • Table relationships (building, company, date)

  • Business logic via measures

  • Final aggregation and filtering

Key principle:

  • Databricks = heavy computation

  • Power BI = flexible analysis

For example, metrics like utilization were calculated as measures (e.g. total occupancy ÷ total capacity), so they scaled correctly across filters

Data pipelines ran daily overnight, with selective near real-time updates for specific use cases

Turning Data Into Insight

The system was designed to balance:

  • Standardization (shared datasets across customers)

  • Flexibility (custom analysis per client)

We combined:

  • Pre-aggregated datamart tables

  • Dynamic Power BI measures

Dashboards were modular and enabled per customer, but all built on the same core data model.

AI Layer: Making Data Accessible

Savvy’s AI assistant made the platform usable for non-technical users.

Instead of dashboards alone, users could ask:

  • “Which buildings are underutilized?”

  • “How can I reduce energy costs?”

  • “What’s driving high operating costs?”

Behind the scenes:

  • Structured prompt engineering

  • Example query training (200+ cases)

  • Context-aware SQL generation

  • Validation + retry logic

Go-To-Market & Adoption

A strong product alone wasn’t enough, we had to make it understandable.

I led and supported:

  • Product demos to customers

  • Early-access rollout + feedback loops

  • Sales training and enablement

  • Documentation, FAQs, and guides

  • Data storytelling for marketing

This ensured:

  • Sales could clearly explain value

  • Customers could quickly adopt the product

  • Leadership could position Savvy strategically

Impact

Product Impact

  • Established data as a core product pillar at Cohesion

  • Enabled cross-system analytics across buildings and portfolios

  • Created foundation for AI + digital twin strategy

Customer Impact

  • Reduced reliance on manual analysis

  • Enabled faster, better decision-making

  • Delivered insights across:

    • Cost

    • Occupancy

    • Space utilization

    • Operations

Technical Impact

  • Standardized data modeling + governance

  • Improved pipeline reliability and performance

  • Created scalable architecture for future analytics

Closing

This work sits at the intersection of:

  • Product strategy

  • Data architecture

  • Analytics

  • AI

My role was to connect all of those into a single, usable product.

And more importantly: Turn complex, messy building data into something people can actually use to make decisions.