System of record vs. source of truth: What's the difference?

The difference between SORs and SOTs is less of a technological one than a functional one. They’re not so much different kinds of information systems as they are information systems that play different roles in an organization.

• A system of record’s primary function is supporting business operations by capturing business data in a particular domain. For example, a customer relationship management (CRM) tool often serves as the system of record for an organization’s customer data, capturing information such as name and contact details. 

• A source of truth’s primary function is correlating data elements from different systems to create a holistic picture of a data object, such as a customer, product or process. Often, stakeholders use this holistic picture for data analytics and decision-making. For example, a data warehouse might bring together data from multiple business apps—a CRM, an enterprise resource planning (ERP) tool, an inventory management system. This aggregation allows business analysts to identify broader trends, such as which types of products perform better with different customer segments. 

Put another way, SORs and SOTs address different pain points:

• SORs create an authoritative data store for each business domain, so that individual team members, business units and apps don’t need to maintain their own duplicative and inconsistent datasets. 

• SOTs help prevent data silos and enable organizations to get a comprehensive view of their data objects across domains. 

Systems of record and sources of truth can take many forms depending on a business’s needs and data architecture: specialized software, a basic database, a cloud file system or another storage system. Most organizations maintain both SORs and SOTs, as they serve distinct purposes. 

A system of record (SOR) is an authoritative source of original business data. SORs can contain data on customers, employees, products, suppliers or other assets and entities related to everyday business processes. 

A system of record is the place where core business data is first created and subsequently maintained. Each SOR usually focuses on capturing data that pertains to one particular business operation or domain. 

For example, many organizations use a human resources information (HRIS) system to manage employee records. Typically, these records are created first in the HRIS system, not ported over from elsewhere. If another system needs access to employee records, it sources them from the HRIS.

Some SORs capture real-time data. Others use batch updates.

A system of record is a source of authoritative data. This means two things:

1. The SOR is the data source that feeds other systems. Stakeholders might access data through some other portal—such as a data warehouse or data lakehouse—but the data that flows into that portal ultimately comes from the relevant SOR.
    

2. If any questions or discrepancies arise regarding a piece of information, the SOR’s version is treated as the correct version.

Having a designated authoritative source for each type of data helps ensure data consistency. Everyone working with the data uses the same dataset, instead of their own personal datasets, which can lead to silos, fragmentation, errors, visibility gaps and other problems.

Because of the focus on data quality, many SORs have built-in automation workflows for data validation. For example, a CRM might check any data element categorized as a phone number to make sure that it follows the appropriate format of XXX-XXX-XXXX. 

SORs also tend to restrict write privileges to a narrow group of people or systems. This helps ensure data integrity by preventing malicious or negligent tampering.

Because they maintain stores of reliable data, SORs often play a role in data governance and compliance efforts. SORs can help ensure that only the right people can read or write data for the right reasons. They can also help maintain audit trails and access records to help prove compliance and spot violations for correction.

A system of record can be a simple SQL database or a cloud file storage system. Usually, however, organizations use specialized apps and software, as these tools have built-in functions that help ensure data accuracy and consistency.

Common examples of SORs include customer relationship management (CRM) databases, human capital management (HCM) software, enterprise resource planning (ERP) software, manufacturing execution systems (MES), configuration management databases (CMDB) and project management platforms. 

A source of truth is an information system that aggregates or harmonizes data flows from different sources in an enterprise data architecture to create a complete view of an organization’s data objects across domains. 

A “data object” is the thing to which the data refers—a customer, a product, a process or some other entity or asset. 

Systems of record are very good at maintaining stores of accurate data. However, each system of record is scoped to a single domain. As a result, no single system of record contains a complete picture of a data object that exists across domains.

Sources of truth help solve this problem by taking data from multiple systems, reconciling it and correlating it to produce a holistic view of an organization’s data.

For example, consider a customer. The organization’s CRM tracks identifying data such as their name and the associated account owner. Accounting software tracks all the customer’s invoices and payments. A support ticket system tracks how much support time this customer has required  and for what issues. 

Each individual SOR—the CRM, the accounting software, the support ticket system—can offer insight into only one facet of the customer. But a source of truth uses these SORs as source systems, aggregating data from all of them to create a single customer record that includes identifying information, financial data and support needs.  

In creating a holistic view of data objects, a source of truth helps break down data silos and makes it possible for analysts, business users and other stakeholders to use the organization’s data to make informed decisions, predictions and projections.

For example, consider that holistic customer record uniting CRM, accounting and support data and metrics. Armed with this complete view, a data analyst can now answer a question such as: “Is this customer profitable?” 

It is worth noting that not all SOTs act as analytics portals or platforms.

Like a SOR, a SOT is a role that a particular information system plays, not a distinct technology category. Therefore, SOTs can take many forms. Common examples include:

• Data warehouses and data marts, which aggregate data from various sources into a central data store optimized for querying and analysis. 

• Master data management (MDM) platforms, which consolidate key enterprise data assets such as customer information, product details and location data. 

Data lakehouses, which combine the flexible data storage of data lakes with the high-performance analytics capabilities of data warehouses.

Whatever form they take, most sources of truth are built through a common process:

1. Identify relevant systems of record that need to be unified.
    

2. Establish MDM rules to dictate how data is structured, reconciled and related.
    

3. Integrate SORs into a common repository through an extract, load, transform (ELT) pipeline or virtualization.

A single source of truth (SSOT) is roughly the same thing as a source of truth: A reliable source of information that unites business data from across the organization to create a holistic view of data objects. 

But the addition of the word “single” emphasizes a particular approach to designing data architecture: the idea that there should be only one source of truth that every stakeholder and app references. 

Some organizations might have multiple sources of truth, depending on their needs and architectures. But an organization with a single source of truth aims to unify all these disparate SOTs into one centralized, reconciled view of data across the entire organization.

For as similar and complementary as they are, SORs and SOTs are treated as distinct components of a data architecture because they perform different roles with different requirements.

SORs own the creation of data and its integrity within its domain. SOTs own the consistency of data across domains. A SOR doesn’t need a holistic view of every data object. It only needs to record the data that matters in its domain. But other users and systems might need a holistic view—and that’s where the SOT comes in.