Every decision, transaction, customer interaction, and financial report in a business is backed by data. But beneath every dashboard, every invoice, and every support ticket lies a complex patchwork of systems designed to create, manage, analyze, and synchronize that data. While each system plays a distinct role, together they form the invisible machinery that keeps modern companies running.

In this post, we outline the key components of the business data stack—from Systems of Record to BI tools—before diving into how they connect, overlap, and sometimes collide in ways that slow businesses down.

The Core Systems of Business Data

Here are the most common and critical systems found in a modern organization's data stack:

1. Systems of Record (SoRs)

2. Databases (Operational and Analytical)

3. Enterprise Resource Planning (ERP) Systems

4. Business Intelligence (BI) Tools

5. ETL / ELT Pipelines

6. Master Data Management (MDM) Systems

7. Data Governance and Lineage Platforms

8. Customer Data Platforms (CDPs)

9. Operational Data Stores (ODS)

10. Line-of-Business Applications (Transactional Systems)

11. Sales Systems (eCommerce Platforms and POS)

12. Payment Providers

13. Warehouse Management Systems (WMS)

Each of these systems addresses a specific set of needs, and many organizations implement several—or even all—to fulfill the requirements of different teams and departments. Yet as businesses scale, so does the complexity of maintaining harmony between these systems. Below, we break down what each one does and how they fit together.

1. Systems of Record (SoRs)

Systems of Record are the authoritative data sources for specific business domains. Each SoR is considered the "truth" for a specific type of information, such as customer profiles, employee records, or financial transactions.

How they work:

• SoRs are typically built around structured schemas with strict validation rules.

• They include version control, access auditing, and lifecycle management.

• They are not necessarily optimized for analytics, but for integrity and compliance.

Examples:

• Salesforce: Customer and sales pipeline data

• Workday: Employee lifecycle and payroll data

• NetSuite: General ledger and financial operations

Interaction with other systems:

• Frequently feed data into analytics platforms and data warehouses.

• Often linked with MDM systems for entity resolution.

2. Databases

Databases are the underlying storage engines of most digital systems. They can be tailored for transactional performance or analytical querying.

Types and use cases:

• Relational DBs (PostgreSQL, MySQL): Common for operational apps needing strong consistency.

• NoSQL DBs (MongoDB, DynamoDB): Handle unstructured or flexible-schema data, often at scale.

• Time-Series DBs (InfluxDB): Ideal for telemetry, metrics, and logs.

• Graph DBs (Neo4j): Best for representing networks, relationships, or hierarchies.

• Data Warehouses (Snowflake, BigQuery): Columnar stores optimized for large-scale aggregation and reporting.

Interaction with other systems:

• Serve as the backend for SoRs or ERPs.

• Power reporting systems via SQL queries.

• Feed BI tools either directly or through an intermediate warehouse.

3. ERP Systems

Enterprise Resource Planning systems unify internal business processes across departments. They attempt to centralize operational workflows.

How they work:

• Use modules to segment functionality: finance, HR, procurement, supply chain.

• Often run on centralized databases with shared business logic.

• Include workflow engines, approval chains, and transaction tracking.

Examples: SAP, Oracle ERP, Microsoft Dynamics

Interaction with other systems:

• Integrate with SoRs to push/pull data.

• Require strong data governance and reconciliation with finance systems.

4. BI Tools

Business Intelligence platforms make data understandable to non-technical users. They offer visualization, querying, and dashboarding interfaces.

Key functions:

• Define and standardize business metrics (KPIs).

• Query data warehouses or source systems.

• Enable self-serve analytics through drag-and-drop tools.

Examples: Looker (with LookML), Tableau, Power BI, Metabase

Interaction with other systems:

• Depend on curated data in warehouses or semantic layers.

• Often struggle with data consistency if upstream definitions change.

5. ETL / ELT Pipelines

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes form the data arteries of a company.

How they work:

• Extract data from source systems (APIs, DBs, files).

• Load into a destination (e.g. warehouse).

• Apply transformations to clean, join, or aggregate data.

Examples: Fivetran, Airbyte, dbt, Apache Airflow

Interaction with other systems:

• Serve as glue between SoRs, ERPs, warehouses, and BI.

• Critical for timeliness and quality of downstream metrics.

6. Master Data Management (MDM)

MDM systems reconcile inconsistent data across multiple systems. They establish a single, consistent "golden record" for key business entities.

How they work:

• Use rule-based or ML-based matching to merge duplicates.

• Track source lineage and change history.

• Often governed by human data stewards.

Examples: Informatica MDM, Reltio, Ataccama

Interaction with other systems:

• Feed cleaned data back into SoRs, warehouses, or operational systems.

• Crucial for analytics, personalization, and compliance.

7. Data Governance & Lineage

These tools offer transparency into the lifecycle and usage of data. They help teams answer: Where did this number come from? Is it trustworthy?

Key features:

• Lineage visualization (data flow diagrams)

• Data catalogs and tagging

• Role-based access and audit logs

Examples: Collibra, Alation, Amundsen, Monte Carlo, Great Expectations

Interaction with other systems:

• Integrated with pipelines and BI tools to surface context

• Ensure regulatory compliance and audit-readiness

8. Customer Data Platforms (CDPs)

CDPs are purpose-built to unify customer events and attributes from many systems into coherent profiles.

How they work:

• Collect behavioral data from web/mobile/apps

• Stitch it with CRM and transaction data

• Power segmentation and activation for marketing and sales

Examples: Segment, mParticle, RudderStack

Interaction with other systems:

• Pull data from SoRs, apps, and warehouses

• Push audiences to ad platforms, email tools, or analytics

9. Operational Data Stores (ODS)

An ODS is a high-performance, often real-time database that aggregates current-state data from various systems. It serves low-latency use cases.

Use cases:

• Dashboards that refresh every few seconds

• Internal tools needing up-to-date status

• Fraud detection, customer service portals

Interaction with other systems:

• Pulls frequently from SoRs and apps

• May backfill the warehouse with time snapshots

10. Line-of-Business Applications

These are domain-specific tools that create transactional data and are often core to day-to-day operations. Many of these overlap with other categories such as eCommerce platforms, POS systems, and warehouse tools, but they’re worth noting as a standalone category because of their operational centrality and variety.

Examples: Point-of-sale systems, inventory management, booking engines, service desk software, restaurant ordering systems, field service management apps

How they work:

• Capture real-time transactions like sales, bookings, support tickets, or inventory changes

• Often tied to physical locations or live customer interactions

• Usually purpose-built for specific functions with limited interoperability out-of-the-box

Interaction with other systems:

• May act as SoRs in narrow domains (e.g. booking history)

• Feed data into ETL pipelines for integration into broader reporting

• Synchronize with payment providers, inventory systems, or CRM for end-to-end visibility

• Provide operational metrics and audit logs that flow into BI tools

How These Systems Interact—and Where They Clash

Let’s now look at how these systems interact, where they overlap, and why the current model—though powerful—often leads to complexity, high integration costs, and fragmentation.

On paper, each tool solves a critical need. But in practice, stitching them together creates a labyrinth of dependencies and translations. One team might rely on Salesforce as a system of record for customer data, while another extracts that data into a warehouse and models it in dbt. Meanwhile, the finance team depends on NetSuite to calculate revenue, which uses entirely different definitions. Marketing is querying customer segments in a CDP, and operations is looking at real-time inventory status in a separate dashboard fed from yet another system.

The result? Every metric has multiple interpretations. Each integration is brittle and expensive to maintain. Any change to the source schema, business logic, or timing in one system can ripple through dashboards, reports, and operational tools. And worse, business users are left unsure which version of the truth to trust.

These systems are powerful. But without a unifying framework or consistent data logic, they often become sources of fragmentation—introducing silos, latency, duplication, and misalignment. Businesses spend as much time reconciling data and definitions as they do acting on them.

The Core Problem: Too Many Systems, Not Enough Clarity

This leads to a crucial question:

What if, instead of assembling a stack of disconnected tools, you could build your core business data infrastructure—fully customized to your workflows, your entities, your metrics—with just one platform?

One that combines the integrity of a System of Record, the flexibility of a database, the logic of an ERP, and the insight of a BI tool. One that integrates where needed but never forces complexity where simplicity would do.

Introducing Millentic: A Unified, Commerce-Ready Data Platform

That’s the vision behind Millentic. A single, unified platform designed not just to model your business truthfully, but to serve as a datahub with integrated operational capabilities. Millentic doesn't just store data—it brings it to life. It enables businesses to define their own entities, metrics, and workflows, and operate them in real time.

Millentic is especially designed with commerce businesses in mind—organizations that deal with highly transactional, rapidly changing, and operationally rich datasets. From orders and returns to inventory updates and payment flows, commerce data has unique requirements that traditional business systems often struggle to handle cohesively.

With Millentic, you're not tied to fragmented layers. It combines the authority of a system of record, the flexibility of a modern database, the coordination power of ERP logic, and the insight layer of BI—all within one platform. This means fewer brittle integrations, faster decision-making, and the ability to evolve your data and operations together, with full transparency and control.

Whether Millentic becomes your foundation or a connective layer within your stack, it empowers teams to own their data and workflows end-to-end, in a way no single-purpose tool ever could.

In future posts, we’ll explore how companies are adopting Millentic to simplify their data architecture—and what becomes possible when operational and analytical truth finally converge.