Discover how today’s data teams are embedding automation and accountability into their architecture to prevent, surface, and resolve data integrity issues — before they compromise customer trust or compliance efforts.

Dashboards, reports, and real-time alerts are only as reliable as the data behind them. Yet, according to the 2023 KPMG AI Risk Survey Report, data integrity issues were the top challenges businesses face when training AI models — ranking even higher than statistical validity or model accuracy. 

The problem: businesses not knowing whether their data is relevant, complete, or trustworthy enough to support decision-making.

When teams rely on outdated or inconsistent data — or skip crucial steps like data validation — decisions suffer. Data teams lose hours cleaning up spreadsheets and rerunning failed jobs. Analysts question their results. And executives lose faith in company dashboards and reports

This dynamic shifts when data integrity becomes a priority. Teams move faster with fewer mistakes, and leaders make better decisions (without second guessing themselves).

This article breaks down the concept of data integrity and why it's essential. We'll also explain how pipeline automation and strong data governance can address some of the most common challenges (and how Fivetran can help).

What is data integrity? 

Data integrity means data is accurate, complete, consistent across systems, and secured against unauthorized changes. It allows you to trust your data throughout its entire lifecycle. 

Let’s take a closer look at each of those attributes.

There are 2 types of data integrity issues: Physical and logical. 

Physical data integrity

Physical integrity issues occur when saving or retrieving data, usually due to problems with storage systems or related hardware devices. 

Common causes include:

• Natural disasters (e.g., floods, fires, and other extreme weather events)
• Hardware failures (e.g., hard drive crashes)
• Environmental stress (e.g., power outages, extreme temperatures)

Any of these scenarios can undermine the physical integrity of data or result in a total loss.

A common method of protecting against physical loss is a Redundant Array of Independent Disks (RAID), which distributes data across multiple disks to prevent individual disk failure.   

Logical data integrity 

Logical integrity issues affect the sensibility and relevance of the information in a database. Structured data validation enforces rules like:

• Entity integrity: Each record must have a unique primary key. For example, all customer IDs in a database should be unique, with no duplicates or null values. 
• Referential integrity: Foreign keys must refer to existing records in related tables to keep relationships valid. For example, every employee ID in the Salary Table matches an employee in the Employee Table.
• Domain integrity: All data must adhere to a pre-defined format, such as restricting a column to numerical values only. 
• User-defined integrity: In this type, users set custom rules, such as no negative values or discounts exceeding 50%. 

Importance of data integrity 

Data integrity's benefits have a ripple effect throughout an organization:

• Informed decision-making: When an organization commits to data integrity, it has trustworthy data to make accurate decisions, not just best guesses. 
• Security: Data integrity safeguards organizational assets and sensitive information from unauthorized access. And with the $4.9M average in IBM’s Cost of a Data Breach Report, prioritizing data integrity also has a clear financial incentive. 
• Regulatory compliance: Effective data management keeps organizations audit-ready,  supports compliance frameworks like GDPR, and mitigates the risk of fines and penalties.
• Trust and reputation: Clean data leads to more accurate, reliable reporting and analytics. Over time, this builds trust and credibility with customers, stakeholders, and other strategic partners.

Overcoming 7 critical data integrity issues + how Fivetran can help

This section discusses 7 common data integrity challenges teams face, from duplicates to outdated records. We’ll also explain how data pipeline automation can catch and fix these issues before they impact your work or your customers. 

1. Fragmented data 

Organizations lose sight of the big picture when data is scattered across disconnected apps and databases. 

When the sales team puts deal updates in a CRM, marketing tracks leads in another tool, and finance keeps its revenue figures in spreadsheets, data gets siloed. As a result, reporting becomes inconsistent and unreliable. 

Solution: Centralize data in a cloud data warehouse to unify and update fragmented sources. This gives teams a reliable, centralized view, so they don’t have to rely on spreadsheets, exports, or manual data entry to extract and combine data from hundreds of sources — notoriously slow and error-prone processes.

You can also use built-in checks to minimize errors. For example, Intercom used Fivetran to automate its financial data integration. As a result, they reduced manual error-checking from 10 hours to just 1 hour per week.

Fivetran also helped engineers at WeWork save hundreds of hours a month by centralizing data from all of its on-premise and cloud sources into Snowflake. This also provided more visibility into occupancy, renewals, member growth, and profit margins across all locations.

2. Duplicated data 

Duplication occurs when the same data appears in a system multiple times. It typically results from manual errors, merging data from various sources, or weak data governance.

When customers update their account information, the system might create a new account instead of updating the original. These duplicate records undermine data integrity, lead to skewed customer reporting, distort growth metrics, and inflate storage costs.

Solution: Many of today’s data integration platforms use incremental syncs and primary keys or unique identifiers (e.g., customer email addresses, account IDs, transaction numbers, etc.) to only bring in new or changed records after the initial load. This way, systems can recognize and update existing records. 

With incremental syncing and schema-aware updates, pipeline tools can help preserve data integrity by avoiding redundancies and ensuring record uniqueness.

3. Outdated data

When source systems update data records, downstream analytics tools and databases may not always reflect those changes. For example, customer records deleted in the CRM might remain active in analytics tools, overstating customer counts or activity reports.

These discrepancies can happen when data pipelines don’t auto-capture deletions or changes, or due to irregular or untimely syncs.  

Solution: To keep data up-to-date, apply data validation checks to flag old entries for further review. Continuously monitor pipeline status to catch sync failures or delays that could result in stale data. 

Fivetran’s change data capture (CDC) tracks every addition, revision, and deletion in source systems and automatically applies them to the destination. This reduces the pipeline maintenance time and keeps data fresh.

That’s also how Sharp HealhCare reduced its data pipeline maintenance workload from 90% of its time to just 10%.

4. Lost data  

Broken connections, interrupted syncs, and corrupted files can cause data loss mid-transfer. This is common during manual uploads or large system migrations. If no one catches the problem quickly, the result is incomplete or missing records and delayed analysis.

Solution: Establish automated ingestion protocols with built-in retry logic. Platforms with idempotent syncs can detect failures, retry failed syncs on their own, verify past transfers, and resend only missing rows — mitigating the risk of loss with minimal effort.

Data movement tools also let teams re-sync only a specific time range. Unlike self-healing syncs, which catch and correct errors independently, re-syncs are intentional, so teams can fix or backfill past data as needed.

GroupM’s small data team used to spend much of its time troubleshooting errors and struggling to maintain connections. After switching to an automated data platform and setting up self-healing syncs and on-demand re-syncs, they've sped up data ingestion from over 15 sources, which saves around 75 hours a month.

5. Data security and compliance

Upholding data integrity is often a legal and ethical obligation — particularly in finance,  healthcare, and other heavily regulated industries. Regulatory frameworks like HIPAA, PCI DSS, and GDPR impose strict standards around how organizations collect, use, and secure personally identifiable information (PII) and customer data.

Solution: Encrypt data when it’s in transit and at rest, restrict access by role, and mask personally identifiable information; so even if someone tries to intercept this data, it’s unreadable to them. 

Teams can also use field-level data masking and blocking to protect sensitive information like credit card details and medical information. 

For internal staff, use fine-grained permission systems, where access controls (for viewing or editing) are given only to team members who need them. 

With these systems in place, meeting SOC 2, GDPR, HIPAA, ISO 27001, PCI DSS Level 1, and other compliance requirements becomes standard operating procedure.

Coupa used Fivetran’s automated data ingestion to consolidate its customer data from Salesforce and NetSuite into Snowflake. Their new setup keeps data secure and accessible for audit review, aligning with SOC 2 and ISO 27001 compliance standards.

6. Auditability

When data moves between tools and teams, knowing who changed what, when, and why is essential. But many pipelines lack clear audit trails. 

Without detailed pipeline logs, it’s practically impossible to trace errors, and teams don’t know the cause of report changes or if the information they have is reliable. 

Meeting compliance requirements requires strong internal controls, clear documentation, and comprehensive audit logs. Teams must maintain a complete, easily accessible log of all data changes and access history for audit purposes.

Solution: Track access and system changes with role-based permissions and detailed logs to capture pipeline events automatically. Fivetran enforces auditability with every sync, logging information such as:

• Which tables were pulled
• When the syncs started
• How many rows were updated

These systems and processes help teams stay audit-ready by default.

7. Legacy systems

Many modern businesses still depend on older, legacy software that doesn’t connect easily with cloud solutions. Extracting data, when possible, often requires custom scripts and considerable engineering hours. As a result, teams may have to wait days or weeks to get vital data.

Solution: Use automated data pipelines that have pre-built connectors for common legacy systems. This makes it easier to transfer on-premise data into a cloud-based warehouse. 

For systems like SAP ECC and S/4HANA, Fivetran’s pre-built connectors use change logs and application-layer APIs to capture updates without accessing the underlying database. Or, you can build your own custom connectors for legacy databases and applications for more tailored solutions.  

Build trust in your data pipeline

To protect against fragmentation, duplication, and outdated data, you need clean, complete data — by design. 

Fivetran makes that possible. As a fully automated, secure data movement platform, it simplifies and speeds up your pipelines, maintains accuracy from source to destination, and keeps data compliant with global standards like GDPR.

Thousands of companies use Fivetran to protect against reporting errors, reduce pipeline maintenance, and stay audit-ready.

Ready to build trust in your data pipeline?

Download our free ebook, The Ultimate Guide to Data Integration, to learn how modern data integration supports real-time, dependable, and governed data. 

Or start your 14-day free trial today to see how Fivetran fits in your stack.

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