What Is Data Accuracy?

The terminal screen flickered, casting an eerie glow over the keyboard. I could see the familiar decimal data errors piling up like a stack of broken promises. Each line of spooled output was a reminder that something had gone terribly wrong, yet the source was obscured, hidden behind a veil of complex interactions. The clock was ticking, and the pressure mounted. The team was on edge, frantically trying to pinpoint the root of the issue while I could only watch as the signals began to blur.

As I dug deeper, I found myself chasing ghosts. The decimal data corruption seemed to echo louder than the other failures, but I knew I had to resist the urge to fix what was visible. Every time I thought I had a handle on it, another system would leak, complicating the picture. I was stuck in a loop, fighting against time and the relentless tide of issues that kept surfacing.

Suddenly, a thought struck me: the real problem wasn’t just where I was looking, but what I couldn’t see. The spooled output started to tell a story, but it was a story marred by contamination from a bad API caller. I needed to find a way to stabilize the IBM i—cap retries, clear the stuck work—while proving whether that caller was really the culprit.

I have seen this chaotic dance in data-decimal-first scenarios where the decimal data corruption is the loudest, yet the truth lies buried in the noise. The instinct is to fix the visible symptoms, to bandage the wounds as they appear, but that only serves to obscure the deeper issues at play. The team is left scrambling, each fix merely a temporary reprieve in a battle against a relentless tide of errors.

What we often overlook is that the clean failures, the ones we can explain from trigger to symptom, are the ones that allow us to truly understand and rectify the situation. It’s the messy ones, where the signals are mixed and the ownership is unclear, that become the real challenges. The clock keeps ticking, and the longer we wait, the more complex the situation grows.

Step One — The Wrong Assumption

Misleading Symptoms of Data Quality

"Decimal data errors must be a system bug. The values should never be wrong!"

The first instinct often leads to misdiagnosis, assuming that decimal data errors stem from a bug in the system itself. The rationale seems sound: if the data appears incorrect, there must be something fundamentally broken in the process or the platform. However, this line of reasoning overlooks the broader picture. Data accuracy issues can arise from a myriad of sources, including user input errors, integration problems, or even issues in upstream systems.

This assumption can lead teams down a rabbit hole of unnecessary investigations and fixes that fail to address the real problem. When the focus is solely on the system, we risk ignoring the influence of external factors or the data lifecycle, leading to a host of missed opportunities for more effective remediation. The reality is that data accuracy is a multifaceted issue that requires a broader lens to truly understand and resolve.

Step Two — The Partial Signal

Signals That Seemed Fine

The playbook check revealed that three of the four signals seemed stable. The data was flowing as expected from the source to the system, the transformations were executing without errors, and the outputs were landing where they should. But the fourth signal, the one measuring data integrity, was the one that told a different story altogether. It was the weakest link in the chain, and it was here that the real issue lay.

The team was quick to celebrate the apparent stability of the first three signals, but I knew better. Data accuracy is not merely about flow; it’s about trustworthiness. When even one signal falters, it casts doubt on the entire system's integrity. The fact that we had overlooked this fourth signal was a red flag that warranted immediate attention.

In such scenarios, what appears to be a minor detail can spiral into significant trouble. The clean data we thought we had may have been tainted at the source, leading to downstream consequences that would compound the problem. I learned that it’s essential to treat every signal with equal weight, lest we allow ourselves to be misled by a false sense of security.

Step Three — The Failed Fix

Fix That Should Have Worked

The fix we attempted seemed straightforward: stabilize the IBM i environment. We capped retries, cleared out the stuck work, and narrowed the failing path, all while trying to prove whether a bad API caller was the source of our troubles. It felt like a logical approach, a methodical way to deal with the chaos we were facing. But in reality, it only made matters worse.

Instead of resolving the underlying issues, our fix merely masked the symptoms. The pressure we placed on the system led to additional failures, which compounded our confusion. We were left with a patchwork of temporary solutions that failed to address the core problem. Each step forward felt like two steps back, and the mounting frustration was palpable.

The lesson here was clear: quick fixes may provide temporary relief, but they do not substitute for a comprehensive understanding of the problem. Every time we attempted to fix the visible symptoms without addressing the root cause, we only added to the complexity of the situation, turning a manageable issue into a full-blown crisis.

Step Four — The Real Failure

The True Source of Failure

The real failure emerged from a lack of clear ownership within the organization. The lifecycle of the data was muddied by unclear responsibilities, leaving everyone scrambling to fix what they could see while ignoring the larger context. This gap in ownership created a cascade of misunderstandings and miscommunications that ultimately led to the corruption of our decimal data.

In my experience, when no one takes responsibility for the data’s lifecycle, the issues multiply. Each team member tries to patch the visible problems, creating a cycle of temporary fixes that fail to address the systemic issues. As a result, the integrity of the data suffers, and the organization is left navigating a quagmire of errors and inaccuracies.

The chaos we faced was not simply a technical failure; it was a failure of processes and communication. In the end, it reminded me that data accuracy is not just about the numbers; it’s about the people and systems that interact with them. Without clear ownership and accountability, even the simplest tasks can spiral out of control.

Step Five — The Definition

Now the definition lands.

Data accuracy is the degree to which data is correct, precise, and reliable, reflecting the true values it is intended to represent. It is fundamental in ensuring that decisions based on that data are valid and effective.

This definition illustrates that data accuracy encompasses more than just correct numbers. It involves the broader context in which the data is generated, processed, and utilized. Accuracy is not a one-time check; it requires ongoing validation and monitoring to ensure that data remains trustworthy throughout its lifecycle.

Moreover, data accuracy differs from data quality, which can include factors such as completeness, consistency, and timeliness. While data accuracy focuses solely on correctness, data quality encompasses a wider array of attributes that collectively contribute to the reliability of the information being used.

What Solix Enforces

Data accuracy requires robust governance practices.

What Solix's archival and governance platform enforces in this category is a rigorous framework for ensuring data accuracy across the board. By establishing clear data ownership, defined lifecycles, and comprehensive validation processes, the platform ensures that every piece of data is not only accurate at the moment of entry but also remains accurate throughout its entire lifecycle.

This means that organizations can trust their data to support critical decision-making processes, knowing that it has been rigorously governed and maintained. In environments with complex data flows, such as those involving multiple systems and integrations, Solix helps create a clear line of sight into the data’s journey, ensuring that accuracy is upheld at every stage.

Three things to do this week

• Audit your data handling processes. Review your current data management practices to identify gaps in ownership and accountability. Ensure that every team member understands their role in maintaining data accuracy and that there are clear protocols for data validation.
• Trace data origins and transformations. Map out the entire data lifecycle, from source to end-user. Understanding where data comes from and how it transforms throughout the process is crucial for detecting and addressing accuracy issues.
• Register a data accuracy protocol. Establish a comprehensive data accuracy protocol that includes regular checks and validation processes. This ensures that data remains accurate and reliable as it moves through different stages and systems.

References

• Gartner — Gartner (EN): Data Analytics Topics Data Quality. A key resource on data quality principles.
• Gartner — Gartner document #5264563. In-depth analysis on data accuracy implications.
• Gartner — Gartner Peer Insights market category: Augmented Data Quality Solutions. Explores solutions to enhance data quality.