We all have blind spots—in our personal lives, in our jobs, in our cars (though backup cameras help with that last one!). These blind spots impact how we see, navigate, and interact with the world. Often, we don’t even realize we have blind spots or what we’re missing until someone (or something) brings them to our attention.

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The Hidden Dangers of Data Blind Spots

In a world inundated with data, blind spots can be perilous. Just like those blind spots in our cars that we rely on technology to eliminate, blind spots within data systems can wreak havoc, and fixing them isn’t as straightforward as adjusting your side mirrors. In this article, we’ll delve into the intricate web of data complexity and the blind spots it creates, explore the repercussions of these blind spots on enterprises, and explore how data lineage can shed light on data darkness.

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The Complex Data Landscape

Data environments have evolved into intricate ecosystems, filled with a multitude of data sources and systems, all operating with varying logic and structures. It’s akin to a bustling metropolis, with data flowing between applications, microservices, and infrastructure, traversing public clouds, private clouds, and on-premises servers, creating a web of dependencies that few can fully comprehend.

This tangled web gives birth to what we aptly term “messy data.” These are datasets that follow different rules, speak different languages, and defy easy integration. It’s the genesis of our data blind spots.

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Unearthing the Unknown Unknowns

Picture this: You’re driving your car, confident in your knowledge of the road ahead. But suddenly, an obstacle appears out of nowhere, a situation you never anticipated. In the data world, we call these “unknown unknowns.” They are the factors you couldn’t account for because you weren’t even aware of their existence.

With data streaming in from myriad sources and systems, it’s almost impossible for any single person or team to have complete visibility. You have a blind spot. In fact, you likely have multiple blind spots hiding more blind spots, a labyrinth of data complexity where problems can fester undetected.

Just as you remain oblivious to your blind spots while driving until an accident or near miss occurs, data blind spots can lurk, waiting to manifest as:

• A broken dependency causing an application to grind to a halt.
• An unseen security vulnerability leading to a damaging data breach.
• A seemingly harmless software update triggering a catastrophic service failure.

These issues remain concealed until they rear their ugly heads, impacting end-users and potentially tarnishing your reputation.

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The Paralysis of Data Complexity

Navigating through these data blind spots is akin to being stuck in a never-ending traffic jam. Your data team is forced to inch forward, often manually, as they attempt to uncover the root causes of issues. Meanwhile, other critical tasks grind to a halt:

• The rollout of innovative features and services is delayed.
• Updates to existing features and services become mired in complexity.
• Issues affecting user experiences remain unresolved.
• The ability to prevent recurring issues becomes a distant dream.

Data complexity not only creates blind spots but also paralyzes your IT teams, preventing them from swiftly addressing the challenges that arise.

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Impact on Enterprises

When data blind spots emerge due to complexity and remain unaddressed, they have far-reaching consequences. User experiences suffer, customers grow frustrated, brand reputations are tarnished, and business outcomes nose dive.

In a recent survey of over 250 IT professionals and business decision-makers, a staggering 93% cited data management complexity as a roadblock to their company’s digital transformation. In today’s data-driven landscape, no industry is immune. Let’s explore how a few enterprises grappled with blind spot issues:

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Data Consolidation within a Financial Firm

A prominent U.S. financial firm embarked on a mission to consolidate data from multiple subsidiaries and affiliates into one central finance and risk data lake. Unfortunately, in this labyrinth of data interdependencies, they overlooked a critical one. This oversight introduced a major bug affecting the primary data lake and reporting systems across all integrated affiliates.

The impact? Limited access to crucial financial insights, impairing their decision-making capabilities. It took more than seven painstaking months to recover, with damages exceeding $60 million.

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Multiple Acquisitions for a Healthcare Insurance Company

A healthcare insurance provider went on an acquisition spree, acquiring several entities. Each acquisition brought its unique data environment, creating a daunting challenge for the IT team. Integrating and consolidating these diverse data sources, reestablishing data pipelines, and decommissioning unused infrastructure parts became a Herculean task, filled with data blind spots and bottlenecks.

The impact? Sensitive data lingered in “dead tables,” connected upstream but not downstream, leaving critical data pipelines vulnerable.

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Migration Project within a Global Tech Company

A global technology firm initiated a cloud migration project, transitioning from their existing Teradata infrastructure to Snowflake. The planning phase failed to uncover several indirect dependencies. As the migration commenced, new problems and broken links surfaced daily.

The impact? The migration project had to be restarted multiple times, ultimately being abandoned, incurring costs exceeding $20 million.

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Shining a Light on Data Blind Spots

The tales of these enterprises serve as cautionary tales, illustrating the perils of data blind spots. So, how can you navigate this treacherous terrain and avoid falling into these traps? The answer lies in automated data lineage.

Automated data lineage simplifies your data environment, empowering your IT team to take control. It eliminates the risk of overlooking or accidentally disrupting critical data components. Eradicating blind spots translates to increased productivity, enhanced efficiency, and a future free of unpleasant surprises.

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Embracing Automated Data Lineage

Imagine having a clear map that reveals where your data originates, where it flows, and what it interacts with along the way. Automated data lineage, as offered by MANTA, accomplishes just that. It creates a comprehensive end-to-end data lineage, harvesting information from multiple sources in an automated manner. This lineage provides an unobstructed view of all data flows, sources, transformations, and dependencies, effectively eliminating blind spots.

The result? Streamlined migration projects, proactive issue resolution, accelerated incident responses, and full control over your data pipelines, even as data complexity continues to grow.

In conclusion, data blind spots are not to be underestimated. They can inflict significant harm on enterprises, impeding progress and damaging reputations. The solution lies in automated data lineage—a powerful tool that grants you the vision needed to navigate the complex data landscape without stumbling into the darkness of blind spots. Don’t let data complexity hold you back; gain a clear line of sight with data lineage and stay ahead of the curve.