Fix These 7 Data Integrity Issues and Embrace Best Practices

Data integrity issues can be aggravating, like when they cause a momentary loss of access to a data set. They can also be catastrophic, like when they lead to a PII data breach of your entire customer base. Here’s how to avoid that.

Failing to address data integrity problems can have costly, far-reaching consequences in terms of lost productivity and revenue, squandered opportunities, and reputation damage.

An IBM study estimated that bad data costs the U.S. $3.1 trillion annually.

As more organizations pursue data democratization, they must establish data integrity best practices to ensure that expanding access to data drives real business value.

Read on to uncover the top (read: costliest) data integrity issues organizations should be aware of and how to resolve them. Then dive into how data integrity issues impact your business through three lenses: marketing performance, operations, and compliance.

Let’s get started.

(Wait — are you asking yourself, What is data integrity? That’s OK, just start with our overview of enterprise data integrity.)

In This Article

Organizations that establish a robust data governance framework with data integrity best practices are well-positioned to make accurate, informed decisions that support continued growth and competitive advantage.

Unfortunately, many businesses fail to prioritize data integrity and are left to grapple with the consequences of bad data — not to mention the realization that remediation is far more costly and time-consuming than establishing good practices in the first place.

Here are seven examples of data integrity issues and how to address them, so they don’t impact operations and your bottom line.

1. Lack of data integration

As an organization’s data sprawl grows, it becomes increasingly difficult to meet the demand for accurate and consistent data. Often, data isn’t where it should be. Sometimes it’s late getting there. There are duplicates. And the data may be formatted incorrectly.

Each scenario represents a lack of data integration and leaves users to question the trustworthiness of data.

Data integration involves consolidating data residing in different sources to provide a single source of truth for users. The process involves data cleansing and ETL mapping. It provides the foundation for actionable and effective business intelligence.

By investing in data integration tools, organizations can ensure access to consistent data sets their users can trust. But that also leaves open the door to mismatched data from disparate sources.

2. Manual data entry and collection processes

Manual processes are inherently error-prone and are the root cause of many data integrity issues. Therefore, eliminating as many manual processes as possible — as soon as possible — should be mission-critical.

Implementing data validation processes restricts the data values users can enter into a cell, in an effort to eliminate input mistakes. Examples include text or data field types, drop-down lists, and multiple-choice menus.

Deploying data validation across an enterprise can be daunting and never-ending, though, without the right approach.

3. Multiple analytics tools

If you’ve been in business a while, chances are you’ve accumulated multiple analytics tools for different functions within the organization. Unfortunately, if they don’t “talk” to one another, you’re likely to duplicate efforts or generate conflicting results.

Data integration efforts and an effective technology stack will eliminate siloed analytics by standardizing data access and analysis across the organization.

4. Poor auditing

Knowing the “who, what, when, where, and why” of every change is essential for data integrity. Without complete and consistent audit trails, there’s no way to ensure accurate and trustworthy data.

A data steward or data controller can provide oversight, monitor audit trails, and take appropriate corrective action when necessary.

5. Reliance on legacy systems

Despite advances in technology, some organizations still rely on outdated data management techniques such as traditional enterprise data warehouses (EDW) or Excel spreadsheets.

Of course, these legacy systems don’t support data integrity or sophisticated analytics. Modern, data-driven organizations leverage the cloud to unify their data where it can be accessed for analysis and business intelligence.

6. Improper data entry training

When users aren’t properly trained on data integrity policies and processes, they’re likely to introduce errors into your data that could potentially impact the entire organization.

Regular training reinforces best practices for how users should interact with data, helping to minimize errors. Training also promotes the idea that everyone is accountable for data accuracy and data quality, ensuring users feel invested in the organization’s overall data integrity.

If this is where you think your organization needs the most help, consider kicking off a data democratization initiative simultaneously.

7. Inadequate data security and maintenance

Along with human error, inadequate security and maintenance practices also contribute to data integrity issues.

Staying up to date with antiviral software and current security threats while constantly monitoring and adjusting data access controls are essential to maintaining overall data integrity.

That’s all to say, elevating data integrity throughout your organization with a data standards tool unlocks solutions to these issues, including:

• Improved data integration that avoids duplication and mismatch
• Streamlined data entry/collection with built-in data validation
• Connectivity between distinct analytics tools
• Continuous auditing with change records
• Easier transitions from legacy systems
• Improved data literacy and democratization
• A single stop for access, security, maintenance, and other controls

Investing in data integrity isn’t just about avoiding catastrophic failures; it’s a proactive strategy for building a more efficient, competitive, and profitable business. Your organization can move beyond the costly consequences of bad data by addressing common issues like a lack of integration, manual processes, and reliance on legacy systems. Prioritizing a robust data governance framework and leveraging the right tools ensures every team can access the consistent, accurate data needed to make informed decisions and drive genuine business value. Commit now to safeguard your data—and your future growth.