What is a system of record?

SORs help ensure that different systems within an organization can reference the most up-to-date, verified versions of important data elements. 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.

SORs can also have cybersecurity applications. For example, organizations might use a secrets vault as a source of record for important credentials, or an identity and access management (IAM) system as a source of record for network users and their permissions. 

Systems of record are important pieces of an organization’s master data management (MDM) strategy. MDM refers to a business’s comprehensive approach to managing critical data across the enterprise. 

In the context of MDM, each system of record provides an authoritative source for a specific type of data—such as customer data or product data. MDM systems consolidate data from all of an organization’s SORs to create a “golden record,” or the most up-to-date and validated version of an organization’s data. The golden record allows everyone to work from a shared “single source of truth” (SSOT), which helps eliminate silos and discrepancies.  

For example, a sales representative might use a CRM system to look up the last time a customer engaged in a financial transaction. The CRM is the system of record, and the customer’s validated transaction history is a golden record.

Systems of record can offer stakeholders across the organization reliable, real-time access to a consistent, validated set of data. In the absence of an SOR, different teams and applications can end up working with inconsistent, incomplete or inaccurate datasets, leading to errors, inefficiencies and suboptimal decision-making.   

At the same time, significant investments in data governance tools and processes are required to maintain data quality and data integrity within a system of record.

A system of record requires a database to store data, a process for validating and synchronizing that data and a way for users to access that data. 

A database is a digital repository for storing, managing and securing organized collections of data.

SORs often use relational databases, which organize data in rows and columns. Stakeholders can use structured query language (SQL) to work with data elements. 

Relational databases are advantageous for building a system of record because they use a table format, which can make data normalization easier. Normalization is a way of organizing data into specific table structures to improve data integrity, prevent anomalies, minimize redundancy and improve query performance. 

A system of record must be continuously updated to help ensure users across the organization can access accurate, verified data. 

In a customer relationship management system, for example, customer data can change at unpredictable times based on customer behavior, such as making a new purchase or updating their profile on the company’s website. When changes happen, the new data must be validated, or else it can introduce errors into the SOR, undermining its authority. Think: A transaction date being listed as happening in the future, or a phone number with too many or too few digits.

Additionally, changes to customers records must quickly propagate to the CRM, the MDM or golden record and the multiple systems that rely on this information (such as a marketing app that uses contact information from the CRM to send offers). 

Many SORs have built-in automation workflows for data validation. Workflows can be as simple as ensuring that data matches an expected format (for example, that a date is written as MM/DD/YYYY) or as complex as comparing data to reference sets or applying business logic (for example, a purchase cannot be listed as occurring at a future date). 

Synchronization requires large-scale integration across information systems and business apps. SORs often integrate with other tools and software through application programming interfaces (APIs) or other connections so that newly entered or updated data can flow to the systems that need it. 

Users need some way to access the data in a system of record. In the case of an enterprise application, such as a CRM or ERP tool, users can often access data directly through the app. 

Users might also access data through a connected business application or tool, such as a data mart or data warehouse, which cleans and organizes data for business intelligence (BI) and other data analytics use cases.  

Tools that primarily serve to make data accessible for use in business processes and decisions—such as by aggregating, normalizing or otherwise preparing the data—are sometimes called “systems of reference.”

Because SORs are meant to ensure that high-quality data is readily accessible, they place an emphasis on high availability, and many are designed to withstand outages, downtime and other potential problems.  

A system of record is not to be confused with a single source of truth, a phrase commonly used in business to describe a centralized data architecture.

The SOR is a discrete application, system or network that contains current and accurate information about business assets, entities and functions. An SSOT is more of a principle or practice for designing data systems, which strives to create one central repository for authoritative data for all users.  

For example, an automobile factory’s MES would be the system of record for data documenting the progress of a vehicle on the assembly line. However, the factory’s single source of truth might be an MDM platform that takes in and correlates data from the MES alongside employee data stored in HCM software and a CMDB that governs its IT operations to create a single authoritative record of all company data. 

SORs help meet the data needs of various stakeholders by bolstering data integrity, data quality and data accessibility.

Data integrity is the overarching completeness, accuracy and consistency of an organization’s data. A system of record helps maintain data integrity by ensuring that each piece of information has a validated, authoritative version readily available to the apps and users who need it. 

By regularly validating data and pushing changes to the rest of a business’s systems, SORs help ensure that different applications and users have access to identical, up-to-date data, preventing confusion around important financial or business records.

Data quality is how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness and fitness for purpose. SORs help protect the quality of data by centralizing it, making it easier to flag and correct discrepancies than if it were stored across various systems. 

Centralizing data can also make it easier for governance teams to catch problems in critical records, such as duplicated data, invalid or spam responses, or out-of-date entries. 

SORs make it easier for users and applications to find the data they need when they need it. They do not need to hunt through various databases or compare divergent datasets to determine which is correct. They can simply turn to the SOR as a reliable data source. 

For all their benefits, systems of record can also bring challenges around regulatory compliance, data governance and system complexity.

Because they organize and interact with such a large volume of data, systems of record are often subject to stringent compliance regulations around governance, privacy and security.

Regulations such as the Payment Card Industry Data Security Standard (PCI DSS) and the Sarbanes-Oxley (SOX) Act require accurate records and valid audit trails for certain financial activities. Similarly, the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPPA) carry requirements around what kinds of personal data organizations can store, how they can store it and for how long.

Due to these kinds of regulations, data governance teams responsible for overseeing SORs must remain vigilant about how, when, where and which users access a system relative to its sensitivity.

Maintaining accurate data in a SOR across its entire lifespan requires significant investments in data governance, tools and practices to ensure the quality, security and availability of data.

This investment requires buy-in across the organization, as effective data governance involves support from executives, chief data officers (CDOs) and data stewards. 

The centralized nature of the SOR can be a double-edged sword: It organizes and brings together data for easier oversight, but keeping the repository updated and accurate requires ongoing investment. 

Many integrations are required to make sure that an SOR has and easily promulgates the most up-to-date and accurate information. A single system of record might need to interface with the source systems that feed it data, the systems that pull data from it and an overarching MDM layer—and many organizations have multiple SORs spanning multiple domains. 

For example, an MES might not only be configured to receive data from production lines, but also to connect with related supply chain management (SCM) systems to share information about required materials—and all of this might need to feed into a centralized single source of truth.