Your Business Processes Have Gone to the DOGs (And That's a Good Thing!)

TL;DR: It’s time to evolve from data-centric thinking to a business process-centric approach. The future is about quickly assembling AI Digital Twins—end-to-end business processes (flows, components, UX, governance, and more)—with just the data they need, while iterating rapidly in front of the business.

The Problem with Data-Centric Approaches

For years, organizations have tried to unlock value through data-centric strategies, but the results often look like this:

  • Endless Delays: Months (or even years) are spent building canonical data models and processes before new ideas can be tested.
  • Disconnected Efforts: Data programs stall because e2e business use cases are developed too late in the process.
  • Unfulfilled Dreams: The elusive "single customer record" often fails because different processes need different views of the same data.
  • Loss of Context: Efforts to homogenize "data assets" strip away business context and create brittle systems that break with new use cases.
  • Friction Everywhere: AI and Agentic models keep getting bogged down in their constant need to return for raw data.

Flipping the Script with Data Object Graphs (DOGs)

DOGs take a radical yet intuitive approach: Start with business processes, not data models. Instead of trying to build a "perfect" data layer first, we focus on the actual workflows and use cases the business cares about. Here's how:

  1. Business Processes as Connective Tissue: The process—not the data model—defines the requirements.
  2. On-Demand Data Assembly: Components (Data Products like ML models, metrics, and AI agents) dynamically assemble only the data they need.
  3. Virtual Schemas for Flexibility: Flexible views are created without physically moving data, preserving agility.
  4. Direct Source Integration: Business context is retained by integrating directly with source systems.
  5. Emergent Relationships: Data relationships naturally emerge via workflows, rather than being forced by static data models.

AI-Driven Rapid Prototyping

With AI and LLMs, the game changes entirely. We can now prototype e2e business processes in minutes instead of months.

  • UI + Data Flows: Quickly create UIs linked to real data and process flows.
  • Fast Feedback: Enable immediate feedback and iteration with business users.
  • Capture as You Build: Every asset (processes, components, and their relationships) is documented in real time.

Building a Living Knowledge Base

At the heart of this approach is the Knowledge Base, a dynamic system that captures:

  • Processes: Every workflow, fully documented and executable.
  • Components: Each ML model, data transformation, and metric is catalogued.
  • Data Relationships: Virtual schemas and dependencies mapped as part of the business flow.

This isn’t static documentation—it’s executable knowledge. Each DOG represents a complete business process with built-in data dependencies, dynamic virtualization, and the flexibility to evolve with the business.

The Result?

By using AI, UX canvases, and Data Object Graphs:

  1. Prototyping Becomes Instant: Test and refine ideas at the speed of business.
  2. Knowledge Becomes Actionable: Every process and relationship is catalogued in a living, executable system.
  3. Business-Led Change: Processes are driven by real business needs, not abstract data models.
  4. Innovation at Scale: Move from concept to implementation faster and cheaper than ever before.

With Dataception's Data Object Graphs, AI is just a walk in the park. 🐾