Here's a scene that plays out in businesses every single day: A customer calls asking about their order. The sales rep checks the CRM — it says "shipped." The warehouse manager pulls up the inventory system — it says "pending fulfillment." Someone else opens a spreadsheet that was "definitely updated this morning" — it says "backordered."
Three systems. Three answers. One very confused customer.
This isn't a technology problem. It's a truth problem. And according to IBM research, poor data quality costs U.S. businesses a staggering $3.1 trillion annually. Much of that comes from exactly this scenario — people making decisions based on data that simply isn't reliable.
The cost of multiple truths
When your systems disagree, everyone loses.
Your team wastes hours — sometimes days — playing detective. They're cross-referencing spreadsheets, making phone calls, and manually reconciling numbers that should just... match. McKinsey research suggests workers spend a significant portion of their time just searching for and validating information. That's not productive work. That's data archaeology.
But the time waste is just the beginning. The real damage happens when people make decisions based on the wrong version of reality.
A marketing team launches a campaign for a product that's actually out of stock. A finance team reports revenue numbers that don't match what sales recorded. A customer service rep promises a delivery date based on inventory that doesn't exist. Each of these moments erodes trust — with customers, with partners, and within your own organization.
What single source of truth actually means
Let's clear something up: Single source of truth (SSOT) doesn't mean cramming all your data into one giant system. That's not realistic, and honestly, it's not even desirable.
SSOT means something much more practical: for every type of data, there's one authoritative source that everyone agrees is "right."
Your CRM might be the truth for customer contact information. Your ERP might be the truth for inventory levels. Your HRIS might be the truth for employee data. The key is that everyone knows which system to trust for which information — and when systems need to share data, there are clear rules about which direction it flows.
Think of it like a newsroom. Multiple reporters might cover the same story, but there's one editor who makes the final call on what gets published. The editor is the source of truth. Other versions might exist, but everyone knows which one counts.
Identifying your authoritative sources
The first step toward SSOT is surprisingly simple: make a list of your most important data types, then decide where each one should live.
| Data Type | Common Authoritative Source | Why It Makes Sense |
|---|---|---|
| Customer contact info | CRM (Salesforce, HubSpot) | Sales and support update it most frequently |
| Inventory levels | ERP or WMS | Warehouse operations have real-time visibility |
| Financial transactions | Accounting system | Must match bank records and tax filings |
| Employee information | HRIS | HR manages the employee lifecycle |
| Product catalog | PIM or ERP | Product teams control specifications |
| Order status | Order management system | Tracks the complete order lifecycle |
The "right" answer depends on your specific business. A company with a complex supply chain might make their WMS the truth for inventory. A direct-to-consumer brand might let their e-commerce platform own that data instead. There's no universal correct answer — just the answer that makes sense for how your business actually operates.
Synchronization: keeping everyone on the same page
Once you've designated authoritative sources, you need to get that data where it needs to go. This is where synchronization comes in.
You have two main options:
Real-time synchronization pushes changes immediately. When a customer updates their address in your CRM, that change flows to your shipping system within seconds. This is ideal for data that changes frequently and where delays cause problems — think inventory levels or order status.
Batch synchronization collects changes and pushes them on a schedule — hourly, nightly, or weekly. This works well for data that doesn't change often or where real-time updates aren't critical. Employee directory information, for example, probably doesn't need to sync every millisecond.
Start with batch synchronization for most data types. It's simpler to implement and debug. Move to real-time only when you have a clear business need — like preventing overselling inventory.
The critical principle: data flows FROM the authoritative source TO the downstream systems, not the other way around. If your CRM is the truth for customer data, other systems should receive updates from the CRM. They shouldn't be sending changes back (unless you've built a deliberate feedback loop with conflict resolution).
When systems disagree: conflict resolution
Here's the uncomfortable reality: even with clear authoritative sources, conflicts happen. A customer updates their address on your website while a sales rep updates it in the CRM. Now you have two "new" addresses. Which one wins?
You need conflict resolution rules before this happens, not after.
Last-write-wins is the simplest approach: whoever updated most recently is correct. This works when all update sources are equally trustworthy.
Source priority assigns a hierarchy: changes from the website override changes from the mobile app, but changes from the CRM override everything. Use this when some sources are more reliable than others.
Manual review flags conflicts for human decision. This is appropriate for high-stakes data where automated resolution is too risky — like merging duplicate customer records that might actually be two different people.
Field-level rules let you get granular: the website is authoritative for shipping address (customers know where they live), but the CRM is authoritative for account classification (sales knows the relationship).
Document these rules. Write them down. Make sure everyone who touches the data knows how conflicts get resolved. Otherwise, you'll end up with well-meaning people "fixing" data in ways that create new problems.
Master data management: the formal discipline
If this all sounds like a lot of work, well — there's an entire discipline dedicated to it. It's called Master Data Management (MDM), and large enterprises have entire teams focused on nothing else.
The core idea behind MDM is treating certain data entities — customers, products, employees, locations — as "master data" that needs special governance. This data gets:
- A designated steward who's responsible for its quality
- Defined standards for format, completeness, and accuracy
- Formal processes for creation, updates, and retirement
- Regular audits to catch and correct issues
For smaller businesses, full-blown MDM might be overkill. But the principles scale down nicely. Even if you don't have a "Master Data Governance Committee," you can still assign ownership, define standards, and check your data periodically.
Practical steps to get started
Ready to stop arguing about which spreadsheet is correct? Here's a realistic path forward:
Step 1: Inventory your data
List every system that stores business data. For each one, document what data types it contains and who uses it. You'll probably be surprised by how many places the same information lives.
Step 2: Map the overlaps
Identify data that exists in multiple systems. Customer information is almost always duplicated. So is product data, pricing, and order information. These overlaps are where your truth problems live.
Step 3: Assign ownership
For each data type, designate one authoritative source. Get stakeholder buy-in — this doesn't work if the warehouse manager doesn't agree that the ERP is the truth for inventory.
Step 4: Define the flow
Document how data should move between systems. Which direction? How often? What triggers a sync? Draw it out if that helps.
Step 5: Implement synchronization
Start with your highest-pain data. If inventory discrepancies are causing daily fires, fix that first. Use batch sync initially, then upgrade to real-time if needed.
Step 6: Establish conflict rules
Decide in advance how disagreements get resolved. Write it down. Communicate it to everyone who touches the data.
Step 7: Monitor and maintain
SSOT isn't a one-time project. Data drifts. New systems get added. People find workarounds. Build regular check-ins into your operations to catch problems early.
The governance piece
Here's the hard truth: achieving SSOT is easier than maintaining it.
Someone will create a "temporary" spreadsheet that becomes permanent. A new tool will get adopted without thinking about how it fits the data architecture. A well-intentioned employee will "fix" data in a downstream system instead of the authoritative source.
Data governance — the rules and processes for managing data over time — is what prevents backsliding. This doesn't have to be bureaucratic. At minimum, you need:
- Clear ownership: Someone is responsible for each data domain
- Change control: New systems and data sources go through a review process
- Regular audits: Periodically verify that authoritative sources and downstream systems still match
- Training: New employees learn which systems are authoritative for what
The goal isn't perfection. It's having a system that catches problems before they compound into crises.
The payoff
When you achieve single source of truth — or even get meaningfully closer to it — something almost magical happens. Meetings get shorter because people aren't arguing about whose numbers are right. Decisions get faster because the data is trustworthy. Customer experiences improve because everyone in your organization is working from the same reality.
Gartner research suggests that poor data quality costs organizations an average of $12.9 million annually. Much of that is recoverable once you stop letting your systems contradict each other.
The path to SSOT isn't glamorous. It's inventories and ownership discussions and sync configurations and governance documents. But the alternative — continuing to let three systems give three different answers — costs more than you think.
Your data should tell one story. Make sure everyone's reading from the same book.
Entvas Editorial Team
Helping businesses make informed decisions
