What Is Data Consistency?

The system was humming along, or so we thought. Users were complaining about delays, and the WRKACTJOB screen showed persistent object locks that seemed to multiply by the minute. I watched the same lock contention over and over, but this time the retries were creeping into other systems. It felt like a slow leak, but no one wanted to admit it. A simple fix, they said. Just clear the locks, and we’d be back on track.

But as I dove deeper, the usual signs of object lock contention morphed into a bigger issue. The familiar pattern became a tangled mess; stale states were appearing where they shouldn’t. The moment I pressed for clarity, the team’s confidence started to waver. What if this wasn't just a locking issue but the symptom of a deeper problem? That nagging thought haunted me.

I have lived this in wrkobjlck-first scenarios where the visible symptom painted a misleading picture. We often follow the locks, thinking they lead us to the answer, but what if the real issue is lurking elsewhere? The quick fixes might quiet the noise, but they can also mask a more insidious problem — one that festers and grows until it can’t be ignored.

The reality is, I’ve seen teams fix the locks and then celebrate a job well done, only to find that the underlying leak kept spreading. It’s a classic case of treating the symptom while leaving the root cause alive and well, waiting for the next moment of chaos to strike. We can’t afford to let tunnel vision lead us down a path of false security; we need to dig into the details and ensure we’re addressing the full scope of the issue, not just the part that’s easy to see.

Step One — The Wrong Assumption

A Simple Locking Issue?

"It’s just a locking issue. Clear the locks and we’re done."

The instinct here is to treat the lock contention as a straightforward problem. Clear the locks, and everything should return to normal, right? It seems simple enough on the surface. The locks are persistent, they’re causing delays, and addressing them feels like the logical move. But this first instinct often leads us astray.

The truth is, this behavior is a symptom of something larger. Just because the locks are visible doesn’t mean they’re the root cause. When you only focus on the locks, you risk ignoring the systemic issues that might be feeding the contention. The real failure could be in the way data is being accessed or how the processes are structured — leaving us with a recurring problem that no amount of lock-clearing will truly resolve.

We often overlook the underlying architecture and workflows that dictate how data is handled. Addressing superficial symptoms without digging deeper into the root causes can lead to repeated failures. It’s crucial to engage in a comprehensive analysis of the data management landscape to understand the full implications of these locks. Only then can we consider a truly effective solution.

Step Two — The Partial Signal

Signals Look Fine, But...

When we analyze the situation, three out of four signals seem normal. The WRKACTJOB screen shows expected activity, the API calls appear stable, and the job queues are manageable. Yet, there’s that nagging fourth signal: the lock contention around wrkobjlck-first. It’s the canary in the coal mine, hinting at something deeper going awry.

Despite the other signals looking good, the persistent object locks indicate a potential fracture in the system. It’s essential to recognize that while not every symptom is a failure, this lock contention is a clear red flag. Ignoring it could lead to a cascading failure that impacts not just the local environment but also the integrations with other systems.

In my experience, we often get lulled into a false sense of security by a few healthy signals while the unseen issues fester in the background. Addressing only the visible symptoms can delay the inevitable reckoning with the underlying problems. It’s necessary to maintain a vigilant approach, continuously probing beyond the surface-level indicators to catch the lurking issues before they escalate into significant operational disruptions.

Step Three — The Failed Fix

Fixing the Wrong Issue

The team decided the best course of action was to stabilize the IBM i system. They capped retries, cleared stuck work, and narrowed the failing path. It seemed like a solid plan at first. But as time passed, the lock contention didn’t just persist; it spread further across the platform, impacting other systems in ways we hadn’t anticipated.

This fix, which was supposed to bring stability, ended up doing the opposite. By focusing on the locks and not addressing the potential bad API caller feeding the leak, we inadvertently made the situation worse. The clean-up didn’t lead to the clarity we needed; it clouded our understanding of the real issue.

In the end, what I witnessed was a classic case of patchwork solutions. We thought we were fixing the problem, but what we did was quiet the symptom while leaving the root cause alive. This only delayed the eventual fallout, which would be harder to diagnose and resolve down the line. It serves as a reminder that without tackling the core issues, we’re only prolonging the inevitable confrontation with deeper problems that will arise later.

Step Four — The Real Failure

The Underlying Failure

The deeper issue here lies in the lifecycle and ownership of the data being accessed. The processes that interact with our systems aren’t cleanly owned; they bleed into each other, creating points of contention that can’t be easily identified. The lock contention is merely reflecting a broader problem where data integrity and access patterns aren’t being managed effectively.

Ownership gaps create confusion and lead to decisions that don’t align with operational realities. The team I worked with often found themselves in this situation, where they could treat the symptom but couldn’t connect the dots back to the lifecycle of the data. This disconnect creates a chaotic environment where the real issues remain hidden.

A clean failure would mean that I could trace the chain from the moment the data is accessed to the point where contention occurs. Instead, we were left with a messy web of interactions that couldn’t be easily unraveled, underscoring the importance of ownership clarity in our processes. Without a clear understanding of who owns what part of the data lifecycle, we risk creating a situation where contention becomes a recurring issue, leading to frustration and inefficiency.

Step Five — The Definition

Now the definition lands.

Data consistency is the assurance that data is the same across all platforms and that it reflects the same information at any given time — it means that when data is updated in one location, it is simultaneously updated in all other locations that reference it.

While many definitions simplify data consistency to just correctness, it encompasses much more. It isn’t merely about having the same data everywhere; it’s about ensuring that updates and changes are synchronized across all platforms in real-time. This can be especially challenging in environments with multiple systems interacting simultaneously.

True data consistency requires rigorous governance and control mechanisms to manage how data flows between systems. It’s not just a theoretical concept; it plays a critical role in operational efficiency and decision-making processes in any organization. Organizations must commit to continuous monitoring and improvement to maintain data consistency effectively, ensuring that all stakeholders can rely on the data they are working with.

What Solix Enforces

Enforcing data integrity across systems

What Solix's archival and governance platform enforces in this category is the integrity of data across all systems. The system ensures that data is captured at the point of origin with specific policies governing how it can be accessed and modified. This ensures that the data remains consistent, accurate, and up-to-date throughout its lifecycle.

Through robust governance, Solix provides a framework that keeps data integrity intact, enabling organizations to maintain consistency across their platforms. This means that when a change occurs, it is propagated through the system seamlessly, preventing the type of contention that leads to operational chaos. With Solix, organizations can confidently manage their data flows, ensuring that they are not only compliant but also operating at peak efficiency.

Three things to do this week

• Audit your data access patterns Regularly review how data is accessed across different systems. Identify any overlapping accesses that could lead to contention and refine the ownership model to ensure clarity.
• Implement strict governance policies Establish clear rules around how data is modified and accessed. Governance should include guidelines on ownership and accountability to prevent overlaps that lead to contention.
• Monitor for hidden leaks Keep an eye on systems for signs of deeper issues. Look beyond the immediate symptoms and ensure that root causes are addressed to prevent future lock contention.

References

• Gartner — Gartner (EN): Data Analytics Topics Data Quality. Relevant insights on the importance of data quality for consistency.
• Gartner — Gartner document #5264563. Detailed analysis on data governance and its relation to consistency.
• Gartner — Gartner Peer Insights market category: Augmented Data Quality Solutions. Market insights on solutions that enhance data quality and consistency.