Understanding Data Integrity

At its core, data integrity is a data management concept that refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It encompasses not just the correctness of individual data points, but also the relationships between different data elements and their adherence to business rules and requirements.

Data integrity shows through several aspects, which need to be managed in balance:

Physical Integrity

The fundamental requirement that data remains intact and unchanged during storage, retrieval, and transfer operations. This includes protecting against data corruption from hardware failures, system crashes, or transmission errors.

Logical Integrity

Ensures that data makes sense within its context and follows established rules. For example, a date field should contain valid, properly formatted dates, and relationships between records should maintain referential integrity.

Entity Integrity

Guarantees that each record in a database is uniquely identifiable and that primary key fields contain valid, non-null values. This prevents duplicate records and ensures data can be reliably accessed and managed.

Referential Integrity

Maintains the consistency of relationships between different data elements. Data models of core systems are based on tables with columns used to establish connections between records belonging to different tables. For instance, if a customer record is referenced in an order table, that customer record must exist in the customer table.

The Business Impact of Poor Data Integrity

Organizations often discover the consequences of lackluster data integrity through spontaneous painful experience. A manufacturing company might find itself shipping products to outdated addresses because customer information wasn’t properly synchronized across systems. A healthcare provider might struggle with duplicate patient records, leading to confused care protocols and billing errors.

Ongoing efforts for data integrity and accuracy may not sound important…
until a business realizes it is a prerequisite for the latest strategic project.

Poor data integrity usually has impact across three critical areas:

Operational inefficiencies show in system administrators spending countless hours navigating between different modules and screens to manage fundamental business entities. What should be simple tasks, like updating vendor information or syncing employee credentials, become complex operations requiring careful cross-referencing and manual verification.

Financial implications manifests in affected bottom line through revenue leakage from billing errors, increased operational costs from error correction, compliance fines from reporting inaccuracies, and lost business opportunities due to unreliable analytics.

Strategic disadvantages happen when organizations find themselves making flawed decisions based on incorrect data, experiencing delayed market responses due to data uncertainty, and suffering from reduced customer trust due to service inconsistencies.

Decor - library catalogue representing data integrity in data management

Common Data Integrity Challenges

System integration represents one of the most common battlegrounds for data integrity. When multiple systems need to share data, maintaining consistency becomes exponentially more complex. Consider a large P&C insurance brokerage where carrier information exists across multiple Agency Management System installations (like Vertafore AMS360 or Applied Epic). Data updates must propagate correctly to ensure consistency.

Data migration presents another critical challenge. During system upgrades, consolidations, replatforming, or post-M&A migrations, organizations must ensure data maintains its integrity while moving between different platforms and data models. Aside from the huge challenge of data conversion between systems – i.e. mapping data, be it between installations of the same vendor or two competing systems) – the migration project often reveals inconsistencies that have existed for years, hidden in the complexity of legacy systems.

Data Admins Against Data Integrity

At the heart of data integrity challenges lies a fundamental disconnect between the tools available to data administrators and the complexity of their task. These professionals are tasked with maintaining data integrity and quality across increasingly complex systems. Meanwhile, their tools are not made for efficient large-scale updates of entities in critical data domains.

Do you think great Business Intelligence can happen in spreadsheets?
So, how could data admins be effective in clunky, lagging interfaces?

Typical core system interfaces are a product of the bygone era, featuring low usability and lack of support for data management tasks. They were simply not designed with data administrators in mind, but rather made to cater towards client-facing roles in the organization (sales people, CSR, account managers etc.). Moreover, many of these systems have been designed at least a decade ago and carry huge technical debt. At best, their data management functionalities are nothing more than an afterthought.

The end result is tedious click-by-click operations for even basic data administration tasks. What should be straightforward – like updating multiple employee records or standardizing vendor information – becomes a time-consuming exercise in switching between screens and manually verifying each change back and forth. This approach not only drains resources but also increases the risk of errors that can compromise data integrity.

Centralized MDM – Promise or Trap for Data Integrity?

Traditional Master Data Management solutions emerged as an apparent answer to data integrity challenges. They came with a tempting promise of creating a central repository that would serve as the Single Source of Truth for all core system data. This is a seemingly logical approach, which often adds layers of new issues.

Centralized MDM solutions typically attempt to override existing system authorities and enforce rigid standardization across all domains. They create new “master” repositories separate from operational systems and implement complex synchronization mechanisms. In doing so, they fundamentally alter existing workflows and processes that keep businesses running smoothly.

Going after a big MDM project is the fastest way to shorten a CIO’s tenure.
It is not even a joke at this point.

If you are not sure whether a total overarching MDM is the good direction for your organization, it probably is not. Similarly, if you want a classic MDM mostly to handle data for Business Intelligence purposes, you will most likely end up with increasingly more painful friction in operations.

Don’t let anyone talk you into MDM + Single Source of Truth architecture revolution unless you are 100%:

  • convinced you need it.
  • aware why you need it.
  • certain it won’t obliterate operational workflows.
  • informed about data models of every core system to be involved.

Hyperintegrated MDM implementations frequently fail to deliver on their promises. Core systems lose their autonomy, leading to conflicts between MDM rules and operational needs. What works in the MDM repository may not align with the practical requirements of day-to-day operations. Organizations find themselves investing heavily in new infrastructure and specialized talent, often without achieving clear value. Paradoxically, the more complex the data ecosystem, the larger the risk of an MDM implementation.

decor - two planes side by side, representing the concept of Wingman MDM

The Wingman Approach to Master Data Management

A more practical and lightweight approach to maintaining data integrity is possible – one that respects the authority of core systems while providing better tools for managing their data. This “Wingman” approach focuses on enhancing existing systems rather than trying to replace their functionality with a cornerstone data management project.

At its core, the Wingman philosophy acknowledges that operational systems must maintain their authority over the data they use. Instead of imposing a new overarching master system, the goal is to improve existing processes with practical, non-disruptive tools that bring quick time to value without complex and costly implementation. This means providing enhanced interfaces for data administration while supporting rather than replacing existing workflows.

Instead of giving up authority of your core systems, try giving your data administrators a platform for focused and efficient data management tasks.

RecordLinker can effectively become a data administration hub for your organization, enabling data admins to manage data for each system within specific data domains. The only pre-requisite here is access to your core system’s API for read-and-write synchronization.

Feel free to contact us to discuss your data administration needs. There is no cookie cutter out-of-the-box SaaS. While RecordLinker is highly configurable, we need to look into data models with domains and their attributes.

Maintaining Data Integrity During System Migrations

Data integrity becomes particularly critical during system migrations and data conversion projects. The greatest pain of core system’s vendor solutions often involves tedious, manual matching and mapping records between systems in spreadsheets, creating opportunities for errors and inconsistencies.

The process becomes especially challenging when dealing with large volumes of data, complex organizational hierarchies (e.g. tree structures of issuing companies and their top-level carrier in insurance brokerage and some Agency Management Systems).

Free Book: Practical Guide to Implementing Entity Resolution

Interested in implementing an in-house record matching solution with your own development team without using any outside vendors or tools?

guide to implementing entity resolution e-book cover

RecordLinker introduces guided automation, helping data conversion teams in the migration process. Our machine learning helps identify matching records and suggest appropriate mappings, while leaving edits and approvals solely to your data conversion specialists or business systems analysts. Collaborative workflows enable teams to review and approve mappings while maintaining clear audit trails of their decisions.

Do you grow through acquisitions that come with heavy system migrations?

Maybe you find yourself constantly in need to standardize records coming from partner/supplier systems into your destination core system?

Similarly, ask us for a demo.

Together we will assess your goals, data models of system pairs you commonly convert between, find opportunities for API-based integration, and decide if we are a good match. To understand our general capabilities, take a look at RecordLinker’s list of features.

Data Integrity in Data Management Wrapped Up

Organizations face mounting challenges in maintaining data integrity across their core systems. Data administrators find themselves caught between increasing demands for data quality and tools that weren’t designed with their needs in mind. Traditional system interfaces force them into tedious, error-prone operations that compromise data integrity.

The path for achieving accurate data and maintaining its integrity lies in providing better tools for managing data where it lives – in the operational systems that run your business. Success comes not from imposing new layers of complexity, but from empowering teams with the right tools to manage data effectively within their existing systems.

Suggested Reading about Data Normalization and Quality

Take a look at our recommended reading list for practical and easy-to-understand resources to help you establish good data practices in your organization. Proper data management is not simple – learn foundational concepts to discover helpful solutions to your data challenges.

Problems with Data Management and Migrations?

Are you acquiring businesses, migrating operations, or consolidating similar business systems?

RecordLinker is not just a data management platform! We are primarily known for helping some of the top 100 US P&C brokers with their data conversion (mapping reference data from one acquired system to the destination system post-M&A). Recordlinker uses Machine Learning to make data conversion painless. Let’s discuss your data needs – contact us!

RecordLinker uses Machine Learning to normalize records across your data systems

You can reliably cut conversion project times with ML from weeks to days!