The 5 Most Common Data Management Pitfalls

The path to data success is fraught with pitfalls. This guide isn't about best practices—it's about worst practices. There are five common mistakes that teams often make on their journey toward data observability. 

For each mistake, we'll explore the downstream consequences, as well as how to avoid these particular pitfalls. The more you know, the more you’ll be able to create a plan to sidestep these issues entirely.

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1. Hidden data lineage: The recipe for confusion

When teams implement a data observability program, data lineage should be a critical part of the equation. Simply put, data lineage is the lifecycle of data, including its origins, movements, transformations, and endpoints. It's a comprehensive map of your data's journey. 

Data observability without data lineage is like navigating through a city without a map or GPS. You can see what's immediately around you, but you have no idea how you got there or how different areas connect. A data observability program that hides data lineage may turn into:

• Guesswork: Teams will have little awareness around which jobs impact their datasets, and will take their best guess instead of relying on proof.
• Exclusion: Some of the critical systems in the data pipeline will be excluded from traceability.
• Inaccurate documentation: Teams will need to play detective about historical data details and procedures. 
• Mixing and mashing: Data from multiple sources will be combined, without any record of where it came from. 
• Metadata won’t be accurately updated when pipelines change.

Clear data lineage (ideally down to the job) isn't just a nice-to-have—it's the foundation of data trust and usability. Without it, you're building a house of cards that's bound to collapse under the weight of complexity and change.

How to avoid hidden data lineage

Pipeline traceability is the foundation for comprehensive end-to-end data observability. It means not only understanding where issues occurred within your data pipeline, but which individual jobs encountered the issue. Pipeline traceability allows you to troubleshoot with aggregated logs and cross-platform lineage.

The end result is less time spent firefighting, and more time conducting the rigorous analysis that drives your business forward. In the long-term, pipeline traceability helps you create a culture of trust in data across your entire organization.

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2. A focus on symptoms, not root causes

If your data ecosystem is a garden, ignoring the source of a data issue is akin to pulling out the weeds above the root. Here are some warning signs that your organization needs a better way to conduct root cause analysis with data problems:

• A "whack-a-mole" approach – When teams encounter a data issue, they routinely fix it and quickly move on to the next one without post-mortem analysis.
• Teams adhere to the "one-time fluke" myth, assuming every discrepancy is unique and will never happen again. 
• Symptoms are treated like they're the disease – teams are endlessly patching up surface-level issues, without being able to address the underlying root cause. 
• A "not my problem" culture: Each team focuses solely on their part of the data pipeline, without looking into emergent data patterns across other teams.

Treating symptoms (and not root causes) allows the same issues to manifest over and over again. Your teams waste resources on repeatedly fixing the same problems without ever solving them. In extreme cases, a culture of fear can seep in, where teammates are afraid to report data issues.

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3. Isolated data silos

No organization does it on purpose, but over time, teams build a series of impenetrable data silos. For instance, a data team might store their analyses in separate systems from the main data warehouse. A marketing department might use a CRM that doesn't integrate with the sales team's pipeline software. Or, a People Ops team might maintain employee records in a standalone database that isn't accessible to other departments.

If any of the following situations feel familiar, it signals that your team may be sitting inside an isolated data fortress: 

• Each department creates its own unique data storage and management systems.
• Access controls are strict and bureaucratic. If someone needs data from another department, they need to fill out several forms and wait for approval.
• Cross-departmental collaboration is extremely difficult. Each team operates in ignorance of what others are doing.
• There’s a culture of data hoarding. Teams may feel that sharing data is a zero-sum game. 
• You have incompatible systems. Each department uses different software, data formats, and naming conventions.

In short order, creating data fortresses will duplicate efforts and waste resources across departments. Overall, frustrated employees in silos feel they can’t be – or don’t know how to be – productive.

How to avoid isolated data silos

As a first step, implement a centralized data management system. Integrate data from various departments and sources into a unified platform, making it easier to access, share, and analyze information.

Once you’ve identified your existing data silos and implemented a centralized data platform or data warehouse, you can set yourself up for success in the future. Find a way to automatically identify and track data-related incidents for both data at rest and data in motion - highlighting downstream impact.

For example, Pantomath’s Pipeline Intelligence Engine shows a horizontal view of the pipeline, across teams and departments. No matter which team “owns” the data, Pantomath shows the explicit cause of an incident and suggests a guided path to resolution. With this type of easy collaboration, your teams can stop building walls, and start building a future where data-driven insights are second nature.

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4. Superficial monitoring

It’s tempting to set up a data pipeline monitoring system and call it a day. Busy teams want to “set and forget” their monitoring program. However, there is absolutely a right way and a wrong way to enact data pipeline monitoring. Superficial monitoring is decidedly the wrong way. 

Superficial monitoring means prioritizing quantity over quality. Your system generates as many reports as possible, and loads them with raw numbers, sans context or trends. Vanity metrics and dozens of pages of documents may feel productive, but they don’t actually tell you anything meaningful. You may have a pretty graph to share in a presentation, but you won’t have actionable insights.

Superficial monitoring can also take the shape of oversimplified data quality checks. If you’re ignoring operational metrics about system performance, data freshness, and pipeline efficiency, you may need to go a step further.

Superficial monitoring creates a false sense of security around your data health. You’ll miss the early warning signs of a critical issue, and have to deal with it once it’s a full-blown crisis. You’ll also waste time and resources on metrics that don’t drive action or improvement.

How to avoid superficial monitoring  

Dive into Pipeline Traceability Metrics (PTM) to understand the realities of your operational performance. From data latency to job operations to mean time to detection, these deeper metrics offer a comprehensive view of your data pipeline's health and efficiency.

If your monitoring system doesn’t help you measure the impact of changes and optimizations, or proactively address potential problems before they escalate, it may be too superficial. Instead, go one level deeper. Good monitoring is not about applying a single SQL procedure to one table. Rather, it’s about driving sustainable processes that give context to data engineers and catalyze problem-solving.

For example, data quality checks, created within your platform of record, are automatically recognized by Pantopath and embedded directly within data pipelines. You can manage data quality issues and maintain alignment to root cause and downstream impact.

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5. Bypassing documentation

Organized processes are the hallmarks of a functional data team. But busy teams with long to-do lists often forego documentation in favor of fighting immediate fires. 

Too many “I’ll get to that later”s turn into a long-term problem. Critical processes disappear when key employees leave or go on vacation. There’s a confusing and inconsistent environment that increases the likelihood of errors and data discrepancies. Each audit or compliance check can turn into a nightmare scenario.

How to avoid bypassing documentation

To prevent documentation from falling by the wayside, data teams need to integrate it into their daily workflows. Start by setting clear documentation standards and templates, making it easier for team members to contribute. 

Allocate dedicated time for documentation in project timelines and sprint planning, treating it as a crucial deliverable rather than an afterthought. Implement a "document-as-you-go" approach, encouraging team members to record processes and decisions in real-time.

Also, to make your life easier, leverage automation and organizational tools. For example, Pantomath’s data catalog centralizes and organizes the entire data ecosystem. Jobs, tables, pipelines, and tags sit alongside operational and business documentation. Automated alerts pop up so that documentation data never goes out-of-date.

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Final thoughts

In the world of data, ignorance isn't bliss—it's a disaster waiting to happen. The path to data success isn't just about adopting best practices; it's also about sidestepping the worst ones. As you move forward in your data journey, knowing these common mistakes will help you proactively address issues before they escalate into major incidents. Most data observability tools fall short in preventing these issues; they’re not a one-size-fits-all solution. But with the right approach and real insight into pipeline traceability, your data management goes from a series of reactive firefights to a strategic asset.

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