Entity-centric data products: AI-automated modeling and creation

Entity-centric data products package multi-source data by business entity. K2view entity modeling is now easy and quick thanks to AI automation. 

How AI turns entity-based data modeling into a simple, speedy process 

Enterprise data is still fragmented across apps, databases, and formats. That’s a big problem for analytics, but it’s even a bigger problem for GenAI. If your customer service chatbot or data agents can’t get a complete, current, governed view of a customer, it will stall, guess, or hallucinate. 

Entity-centric data products resolve this issue by organizing data around the way the business thinks – in terms of customers, accounts, orders, claims, devices, etc. So, instead of shipping raw tables, you’re delivering a reusable product that includes the data plus the rules, policies, and access methods needed to use it safely.

Once considered complex and time-consuming, entity-based modeling can now be largely automated. Powered by AI, the K2view Data Product Platform discovers contributing sources, infers relationships, recommends the entity root, and generates meaningful metadata automatically. So, now business SMEs can provision the data they need quickly and easily – without deep technical expertise. 

Why model data products by business entity? 

When you model data products by business entity, you: 

• Hide source complexity 

  Data consumers do not need to know which system has the billing address, which one has the latest status, or how to join 40 tables. The data product shields consumers from the underlying source complexity. 

• Create a common language between business and IT 

  The business asks for a customer or an order. IT often starts with tables. Entity-centric modeling aligns both sides on a shared contract: what the entity is, what complete means, and how it is served. 

• Ensure completeness and referential integrity 

  Entity assembly pulls all contributing records for that entity, across systems, and keeps relationships intact. This matters for testing, compliance, and AI grounding, where missing related records can break scenarios or produce wrong answers. 

• Improve security through isolation 

  When each entity is isolated, you can apply granular access controls and masking at the entity boundary, not just at the table level. The K2view approach also maps naturally to entity-level storage via its Micro-Database™ technology, where each entity is managed in its own isolated store.  

Which use cases fit entity-centric data products? 

Entity-centric data products deliver exceptional value in several high-stake operational areas, including: 

1. Customer 360, for support, sales, field service, etc. 

2. Fraud detection, for identifying suspicious financial activity 
3. Data migration, from legacy to modern systems 
4. Data preparation and pipelining, for analytics in data lakes and DWHs 
5. Customer data protection, in lower environments – with data masking tools
6. Test data provisioning or synthesizing, for compliance with privacy laws – with test data management tools and synthetic data generation tools 

How to model entities quickly and easily with K2view  

A data product only becomes reusable when its model is correct, complete, and easy to maintain. That’s where automation comes in.

K2view makes it extremely easy and quick to model entity-centric data products via AI automation. Its K2studio is a visual, drag-and-drop environment for creating data products. 

A practical implementation flow might look something like this: 

1. Auto-discover contributing sources
   K2view automatically scans your data landscape to find the systems, schemas, and tables that contribute to the business entity. The K2view data catalog (K2catalog) is designed to keep an always-current inventory of data assets, driven by data source crawlers and AI-automated discovery.  

2. Identify the entity root in the graph
   The platform recommends the root table (the anchor for the entity schema) using graph-based scoring, so you don’t have to start by guessing where the entity begins. The suggested entity root is pictured in orange below. 
   
   
   
   
   
   
3. Auto-build the entity model, then refine it
   K2view leverages AI to fully automate the creation of the entity data model. It analyzes the metadata of the relevant databases, SQL queries performed on the data, as well as the data itself, to determine the relationships of the data model. 
4. Generate rich metadata that both humans and AI can use
   The model needs more than column names. It needs meaning, in the form of descriptions, classifications (PII or not), allowed values, and relationship context. K2view leverages AI for generating contextual metadata and confidence scoring, so the entity semantic layer becomes useful for retrieval, governance, and GenAI grounding. 

How the K2view data catalog accelerates accurate implementation 

If your catalog is separate, stale, or incomplete, your data products will be too. An embedded catalog is what makes the motto “model once, reuse everywhere” realistic. 

The K2catalog classifies the data once, and then uses this classification to auto-generate the data model with greater speed and accuracy 

Key capabilities that matter for entity-centric data products include: 

• Data source crawler and plugin framework
  Discovery is extensible through out-of-the-box and customizable plugins (relational and non-relational). 

• LLM-based classification and tagging
  Use LLMs to enrich metadata, tag sensitive fields, and improve semantic search relevance. 
• Automated schema drift handling, versioning, and impact analysis
  The catalog generates versions so you can compare changes over time. It also includes catalog artifact splitting and combining, so multiple teams can work on separate platforms and schemas, and then merge their results. 
• Graph database backbone
  K2catalog uses the Neo4j graph database to visualize and analyze a company's data assets and the relationships between them in an interconnected knowledge graph. 

How relationships between tables and entities are auto-discovered

 K2view auto-discovers relationships between tables and entities using LLM-powered: 

• SQL query analysis 

• Field name semantics 
• Data pattern recognition 
• Primary/foreign key analysis 

The K2catalog visualizes static and active metadata for the data products to support ongoing optimization of data architecture and data services. It also tracks and manages data product performance and usage metrics such as the consumption and response time of a given web service. 

From schema to data product – automatically 

Once the entity model is in place, the K2view platform operationalizes it as a reusable data product by: 

• Auto-generating ingestion and synchronization flows
  Ingestion flows is auto-generated from the data product model, and the platform supports policies to keep data fresh (not stale) for AI, operational, and analytical workloads.  

• Deploying Micro-Databases per entity for low latency
  K2view automatically organizes data into 360° views of business entities where each entity is managed in its own Micro-Database (a data lake of one) designed for split-second responses at scale, with isolation that supports security guardrails.  
• Using the same formula for data masking and synthetic data generation
  Because the entity graph is explicit, data masking and synthetic data generation can preserve relationships and business rules. The K2view platform uses LLM-driven functions (for example, invoking an LLM and composing prompts) that can support AI-driven enrichment and automation patterns within flows.  

Conclusion and key takeaways

A business entity approach to data products isn’t just a nicer model. It’s a practical way to make real-time enterprise data AI-ready without turning every use case into a custom integration project. 

Key takeaways: 

• Entity-centric modeling hides multi-source complexity while ensuring completeness and referential integrity. 

• A graph-based, embedded catalog is what keeps models current, discoverable, and governed as schemas drift. 
• Micro-Databases plus sync policies give GenAI the low-latency, always-fresh entity context it needs automate discovery and recommendations. 

Competitors like Tonic.ai focus on table-first, “no-entity-model” de-identification, but that approach doesn’t keep pace when you need AI-automated data discovery across many systems, continuously versioned metadata, and governed, low-latency entity views – for enterprise-scale test data management, data masking, and synthetic data generation. 

As of the Data Product Platform 8.1 release in Q4 2024, more AI-driven and catalog-driven automation is built into the K2view platform.

So, if you want business value from day one, rely on K2view to auto-discover contributing sources for each entity, generate the model, and deploy it as a governed data product that can power test data mandagement, data masking, synthetic data generation, and more. 

Experience the power of an entity-centric
data product platform in this free demo.