What is Single Source of Truth (SSOT?
A Single Source of Truth (SSOT) is the principle where all relevant data is stored and managed in one system, so everyone in the organization works with the same, current information. It eliminates conflicting data between systems and makes decision-making more reliable. On this page you'll read what SSOT means, how to implement it and which approach fits your system landscape.
What is a Single Source of Truth (SSOT)?
Do you know that moment when two colleagues enter a meeting with different numbers? One has the sales data from the CRM, the other from the ERP. Both current, but they don't match. Who is right? That's exactly the problem a Single Source of Truth solves.
Definition and origin.
An SSOT is an architectural principle where one system or data source is the only official source for a certain type of data. All other systems that need that data type retrieve it from that one source, instead of storing it locally or synchronizing independently. The concept comes from computer science and has now been broadly adopted in data management, software architecture and organizational design.
SSOT versus Single Version of Truth: what's the difference?
SSOT (Single Source of Truth) and SVOT (Single Version of Truth) are often used interchangeably, but they are different. SSOT is about architecture: there is one system that manages and distributes the data. SVOT is about consistency: all systems show the same version, even though they store data separately. SSOT is the cleaner approach. SVOT requires careful synchronization and is more sensitive to deviations as soon as synchronization fails.
Why is SSOT important for your organization?
Inconsistent data costs more than you think. Gartner estimates that poor data quality costs organizations an average of 12.9 million dollars per year, through wrong decisions, rework and loss of trust in systems.
The cost of conflicting dataImagine: your inventory management says 200 pieces available. Your webshop shows the same number, but the ERP has processed a correction to 120. The webshop oversells. The customer gets a disappointment. The internal service solves it manually. This scenario is not exceptional, it's daily reality in organizations with multiple systems that are not well aligned.
When does it become a problem?
It starts small-scale: two systems with similar data, a manual export once a week. That works, until you add a third system. Then a fourth. Each addition increases the risk of inconsistency. The pain becomes visible when decisions are postponed because nobody trusts the data.
How do you achieve a Single Source of Truth?
There is no universal answer. The approach depends on the systems you have, the data you manage and the degree of real-time synchronization your processes require.
Option 1: Central data lake or data warehouse.
A data warehouse centralizes data from multiple sources. All systems write to, or read from, that central point. Popular with organizations that have lots of analytical data and extensive reporting. Less suitable if you need real-time synchronization for operational processes.
Option 2: Custom integration layer via middleware
Middleware is an intermediate layer that connects systems and manages data traffic. One system is the master, the middleware distributes changes to all other systems. This way all systems always have the same current data without communicating directly with each other. This is the approach Cube applies most in complex system landscapes.
Option 3: One system as master, the rest synchronizes.
The most pragmatic approach: you designate one system as the only source of truth for a certain data type and ensure all other systems follow that system. Simple in concept, challenging in execution: the synchronization must be reliable, fault-tolerant and continuously monitored.
SSOT in practice: examples per use case.
Customer data with CRM as SSOT.
The CRM is the only place where customer data is maintained. The webshop, customer portal and billing module retrieve their data from the CRM, never write directly to it. Result: one consistent customer profile in every system.
Product data with PIM as SSOT.
A PIM system manages all product information. Webshop, configurator, print catalog and dealer portal all receive their product data from the PIM. A change in the PIM is immediately visible in all channels, without manual updates.
Financial data with ERP as SSOT.
The ERP is the only source for financial truths: revenue, costs, margins, inventory valuation. All dashboards and management information are fed from the ERP. No more discussion about which version is correct.
How does Cube help achieve an SSOT?
Achieving a Single Source of Truth doesn't start with a technical choice. It starts with mapping your current system landscape: which systems do you have, what data do they manage and where do they overlap. Cube guides this process from analysis to implementation. We map the data landscape, advise on the right architecture and build the integrations needed to achieve one reliable data source. Read more about our Data Management Platform and how we guide digital transformation projects.
Want to know how your organization can work towards one reliable data source?
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Questions? No problem.
A SSOT is the principle where one system serves as the only official source for a specific type of data. All other systems consult or receive that data from this single source, ensuring that no conflicting versions exist within the organization.
A data warehouse is a technical implementation for centralizing data for analysis. A SSOT is an architectural principle: the agreement that one system manages the “truth.” A data warehouse can be part of a SSOT approach, but it is not the same thing.
Start by mapping your system landscape: which systems do you have, what data do they manage, and where are the overlaps? Assign a master system for each data type and build integrations so that other systems receive that data instead of managing it themselves.
It depends on the type of data. An ERP makes sense for financial data, a CRM for customer data, and a PIM for product data. The key is that one system takes responsibility and the architecture consistently respects that.