What Is Data Quality?
What data quality actually means, the six dimensions everyone ends up arguing about, where integrations break them, and how DBSync enforces each one across connected systems.
It starts with something this small
Salesforce has a customer called Acme Corp.
QuickBooks has the same company saved as ACME Corporation Inc.
Nobody notices for months. Then finance sends two invoices to one customer, the pipeline report counts them as two accounts, and someone spends a Thursday afternoon working out which balance is real.
That’s a data quality problem. Not a dramatic one, no system went down, no alert fired. Just two systems quietly disagreeing about who a customer is.
Data quality is the degree to which your data is accurate, complete, consistent, timely, and fit for what you’re actually using it for. In a single system, it’s a housekeeping concern. The moment data starts moving between a CRM, an ERP, an accounting system, and a warehouse, it becomes the thing that decides whether your integrations are useful or just fast at spreading mistakes.
Gartner puts the average cost of poor data quality at $12.9 million a year per organization. That number is easy to dismiss as analyst-speak until you’ve watched a finance team reconcile two customer records by hand.
The six dimensions
Every data quality conversation, no matter where it starts, ends up at the same six words. Here’s what each one means, what it looks like when it breaks, and what DBSync does about it.
| Dimension | The question it asks | Where DBSync enforces it |
|---|---|---|
| Accuracy | Is this value actually true? | Field-level mapping rules and schema validation |
| Completeness | Is anything missing? | Conditional logic routes incomplete records to a review queue |
| Consistency | Do the systems agree? | Bidirectional sync with configurable conflict resolution |
| Timeliness | Is this current enough to act on? | Scheduled replication and CDC |
| Validity | Does it match the expected format? | Schema-aware connectors validate during ETL/ELT |
| Uniqueness | Is this entity in here twice? | Deduplication during sync, matched on key fields |
Accuracy
Accuracy is whether the data matches reality. A misspelled email address in your CRM isn’t a formatting problem, it’s a lead you’ll never reach. A wrong price in the ERP isn’t a typo, it’s margin walking out the door.
The tricky part is that inaccurate data looks completely normal. Nothing flags it. It just sits there being wrong until someone tries to use it.
DBSync’s field-level mapping rules let you define transformation logic that normalizes values before they land in the target. A Cloud Workflow rule can standardize company name formats during sync, turning “ACME Corp” into “Acme Corporation” every time, everywhere, so the drift never accumulates in the first place.
Completeness
Completeness is simpler: is everything there that needs to be there?
A customer record with no billing address. A product with no SKU. An invoice with no line items. Each one is fine sitting in the source system and each one breaks something downstream the moment it moves.
When DBSync replicates from Salesforce into SQL Server or Snowflake, completeness checks catch empty required fields before they arrive. In Cloud Workflow, conditional logic sends incomplete records to a review queue instead of letting them through, which matters more than it sounds, because the alternative is usually blocking the whole batch. Clean records keep flowing. The problem ones wait for a human.
Consistency
Consistency is the one that bites integrated environments hardest, because it can’t exist inside a single system. It’s only ever a question about two.
Someone updates a customer’s address in Dynamics 365. QuickBooks never hears about it. Now billing goes to the old address, the reports disagree, and both systems are certain they’re right.
DBSync’s answer is bidirectional sync with configurable conflict resolution. A change in one connected system propagates to the others on the next cycle, and when two systems have both edited the same record, a rule decides the winner, source system wins, most recent timestamp wins, or a field-level merge. Without a rule, the winner is just whichever job happened to run last, which is how good data gets silently overwritten by stale data.
Timeliness
Data can be perfectly accurate and still be useless if it’s old.
A sales dashboard showing yesterday’s pipeline. An inventory count that’s three hours stale, which is exactly long enough to oversell something you don’t have. Timeliness isn’t only a reporting concern, it’s an operational one, and the cost shows up in decisions, not in error logs.
DBSync handles this two ways depending on the source. For relational databases, CDC tracks row-level changes at the database level and streams only what changed. For CRM and SaaS sources, API-based incremental sync pulls records modified since the last run. Either way you can also fall back to scheduled batch replication where near-real-time isn’t worth the overhead. The right answer depends on how fast your business actually makes decisions, not on which option sounds most impressive.
Validity
Validity asks whether data conforms to the rules it’s supposed to follow. An email field holding an actual email. A date field holding an actual date rather than “call back next Tuesday.”
Validity failures are quiet. They rarely throw an error at the moment they happen. They just corrupt everything computed from them later, which is why they tend to be discovered by an analyst rather than an engineer.
DBSync’s schema-aware connectors enforce format validation during the ETL/ELT process, catching violations before they reach the target. Custom validation rules in Cloud Workflow go past data-type checks into business-specific rules, the ones only your team knows, like which account codes are legal or what a valid contract length looks like.
Uniqueness
Uniqueness means each real thing appears exactly once.
Duplicates inflate customer counts, split one company’s history across two records, and generate an ongoing reconciliation tax that nobody budgets for. And they’re rarely a one-time mess, most systems are actively producing new ones.
DBSync’s deduplication logic runs during sync, matching records against configurable key fields before inserting anything into the target. For bidirectional setups, conflict resolution handles the related failure: two systems updating the same record, with the last writer quietly overwriting the good version.
Where integrations break data quality
Four failure modes account for most of it.
Schema drift
An admin adds a custom field in Salesforce on a Friday afternoon. Your target database has no matching column. Depending on how the pipeline is built, that data either disappears silently or the sync fails outright, and the silent version is worse, because you find out weeks later.
DBSync’s schema-aware replication detects the difference and generates the corresponding column in the target automatically, without dropping what’s already there.
Transformation errors
A type mismatch. A truncated string. A lookup that quietly returns nothing. Any of these can propagate corrupted records across every connected system before a human notices anything is off.
DBSync puts validation checkpoints at three stages, before extraction, after transformation, and after loading, so a failure surfaces with enough context in the execution logs to trace it back to the source rather than guessing.
Inconsistent data entry
Sales reps abbreviate company names differently. Finance uses one date format, operations uses another. Optional fields get skipped by whoever’s in a hurry.
None of this is anyone’s fault exactly, and all of it compounds. It’s also nearly impossible to fix retroactively, which is the argument for catching it at the integration layer: Cloud Workflow standardization rules normalize formats, enforce required fields, and apply lookup-based cleansing while data is in motion, before the inconsistency spreads.
Conflicts nobody defined
In bidirectional environments, two systems will eventually update the same record at the same time. If you haven’t decided what happens next, the outcome is unpredictable and usually invisible.
DBSync gives you four strategies, source-system-wins, timestamp-wins, field-level merge, or a manual review queue, configurable per integration and per object type. Operations teams can change them without a developer, which is mostly what determines whether the rules stay current.
A working approach to improving data quality
This isn’t a project you finish. It’s four habits that keep the problem from coming back.
1. Profile before you build. Look at what’s actually in the data, structure, distributions, anomalies, before configuring a single sync. Replicating bad data faster helps nobody. DBSync’s schema discovery shows you the source structure during connector setup, which covers the structural half of profiling before any job runs.
2. Write down who owns what. Which team owns each data domain, what standards apply, and what happens when they’re violated. Without this, quality work is ad-hoc and doesn’t survive turnover. DBSync’s audit trails and role-based access support governance at the integration layer, but the ownership decisions have to come from people.
3. Automate the checks. Quality controls that depend on someone remembering to run them aren’t controls. Cloud Workflow embeds conditional logic, field-level validation, and error handling inside every sync, so the checks run on every record, every time.
4. Watch it continuously. Alerts on sync failures, record count mismatches, and schema changes. DBSync’s execution logs and email notifications provide this out of the box, the difference between finding out from your monitoring and finding out from a customer.
Assessment checklist
Seven questions. If you can’t answer one of them cleanly, that’s where to start.
| Ask yourself | How DBSync addresses it |
|---|---|
| Are all required fields populated across CRM, ERP, and accounting? | Cloud Replication completeness checks flag empty required fields before data lands in the target |
| Do the same records in different systems hold the same values? | Bidirectional sync with conflict resolution keeps Salesforce, Dynamics 365, and QuickBooks in step |
| Are duplicates identified and merged regularly? | Deduplication runs during sync, preventing the last-write-wins overwrite |
| Is data refreshed as often as your decisions require? | Scheduled batch, CDC, or API-based incremental sync depending on velocity needs |
| Are validation rules enforced during integration, not just at entry? | Schema-aware connectors enforce format rules at the ETL layer |
| Can you show when data changed and who changed it? | Execution logs capture every record touched, timestamped and attributable |
| Are source schema changes detected automatically? | Schema drift detection generates target columns without dropping existing data |
How the two products divide the work
Cloud Replication handles getting data into databases and warehouses accurately: schema-aware connectors map source to target automatically, drift detection adds new columns without data loss, incremental sync reduces how much data is exposed to failure on each run, execution logs capture every record with timestamps, and email notifications fire on sync failure or record mismatch.
Cloud Workflow handles quality between applications: conditional logic routes bad records to review queues, field-level validation enforces format and type constraints, cleansing steps normalize values mid-transformation, lookup-based standardization fixes inconsistent entries at sync time, error handling surfaces failures with context, and post-load record count checks confirm nothing was silently dropped.
Worth being straight about the boundary: DBSync enforces quality on data in motion. It won’t clean up a decade of duplicates already sitting in your CRM, that’s a one-time remediation project. What it does is make sure the integration layer stops adding to the pile, which is the half most quality initiatives skip.
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