KEY HIGHLIGHTS

  • Data observability is essential for ensuring trust in complex ecosystems, mitigating the $15 million annual losses organizations face due to poor data quality.
  • The four pillars of observability—freshness, quality, completeness, and drift—ensure data reliability for timely, accurate, and actionable insights.
  • Observability frameworks enhance cost optimization by identifying inefficiencies, reducing downtime, and enabling secure multi-cloud operations.
  • Real-world applications include e-commerce companies ensuring data accuracy during flash sales and CPG firms preventing operational delays through proactive error detection.
  • Building a successful observability strategy involves balancing build-versus-buy approaches, aligning processes, and fostering a culture of accountability.
  • As technology evolves, observability will address decentralized systems like IoT and edge computing, driving sustainable growth and enhanced decision-making.

Building data trust within an increasingly complex ecosystem is one of the biggest challenges organizations face today. From the ransomware attacks that significantly affected the delivery of services across the National Museum of the Royal Navy to the hacking of the Nigerian Bureau of Statistics’ website, the growing prevalence of cyber threats necessitates robust security measures. These incidents underscore the importance of not just securing data but also ensuring its reliability through proactive observability frameworks.

Establishing trust requires not only safeguarding data but also ensuring transparency and resilience to maintain operational integrity in the face of such challenges. A big challenge though for organizations today is meeting data standards. In fact, Gartner discovered that organizations hold poor data quality accountable for some $15 million losses per year.

Data observability offers a solution by proactively monitoring and maintaining the integrity of data pipelines. Beyond being a technical strategy, it’s a competitive differentiator that ensures businesses can rely on their data to drive insights and innovation.

The complexity of modern data ecosystems

Modern enterprises operate in dynamic data environments, with systems tailored for specific functions like marketing, supply chain, and finance. AI/ML solutions thrive on reliable, clean data, but achieving this starts with building a foundation of data trust.

Without consistent processes for monitoring and managing data, organizations risk undermining the very data trust needed for effective decision-making. Adding to the complexity, many businesses rely on multi-cloud environments, making seamless data operations even harder.

While many organizations focus on scaling their data infrastructure, they often overlook the hidden risks of poor data management—delayed insights, inaccurate analytics, and increased operational costs.

 

What’s missing? A robust observability framework.

Observability: The foundation of data trust

To build data trust, organizations must address four critical aspects of observability.

4 pillars of observability

    1. Data freshness
      Data loses value quickly if it’s delayed, making data reliability a critical factor. Observability ensures data reaches systems in near real-time, maintaining both accuracy and data transparency to support timely decisions. Imagine an e-commerce company analyzing customer behavior during a flash sale. Delayed data could mean missed revenue opportunities.
    2. Data quality
      Unreliable data erodes trust, which is why robust data governance frameworks are essential. Observability tools monitor for errors, inconsistencies, and anomalies, ensuring reports and dashboards provide trustworthy data pipelines for decision-making.
    3. Data completeness
      Missing data creates blind spots in decision-making, emphasizing the importance of data completeness in maintaining data integrity. Observability frameworks are pivotal in establishing data trust by ensuring that data is fresh, accurate, and complete. By proactively identifying errors and inconsistencies, these frameworks prevent issues that erode confidence in your data trust efforts.
    4. Data drift

Data drift occurs when patterns or thresholds, such as inventory limits, shift over time due to changing business cycles. Observability tools leverage AI/ML algorithms to detect and adapt to these shifts dynamically, applying updated thresholds across datasets to ensure ongoing accuracy and relevance.

Most discussions focus on ingestion and reporting, but observability in data transformation—the process of cleaning, merging, and preparing data—is equally critical. Neglecting this step can allow flawed data to flow downstream undetected.

Imagine this: A flat file needs to be loaded into a data lake every night so that daily planning at a CPG firm is streamlined and error-free. In case of absence or errors of the file, business users are at the risk of receiving inconsistent reports the next day.

Without observability, the issue went unnoticed, undermining enterprise data stewardship and causing operational delays as teams scrambled to address discrepancies flagged by users.

With observability, the company would have preserved data trust by detecting the error immediately, triggering workflows to correct it before it impacted planning and reporting.

The financial impact of poor data quality

Every delay, inconsistency, or missing data point impacts data trust, leading to decisions based on flawed information. Observability mitigates costs by supporting secure data management and optimizing cloud environments while ensuring data quality assurance across pipelines.

Cost optimization with observability:

  • Reducing waste: Observability frameworks enhance data reliability by identifying inefficiencies in processes or unused compute resources, optimizing costs while preserving the integrity of trustworthy data pipelines.
  • Avoiding downtime: Real-time monitoring minimizes disruptions that could lead to financial losses.

Observability isn’t an expensive luxury. Innovative gain-share models offered by observability partners—where payment is tied to cost savings achieved—make it accessible and ROI-focused.

Building a winning observability strategy

Implementing an observability framework isn’t just about adopting technology; it requires aligning people, processes, and tools.

The build vs. buy debate

  • Build: Offers full control but requires significant time and resources.
  • Buy: Faster implementation with expert support, often paired with cost optimization solutions.

When evaluating partners, prioritize those who integrate data compliance standards into their offerings and provide accountability through gain-sharing agreements that align their success with your outcomes.

Key questions for strategy design

  1. Are your monitoring tools proactive or reactive?
  2. Do your teams understand and implement observability best practices?
  3. Are your processes streamlined to incorporate data observability into every step of the pipeline?

Many organizations overlook the human factor in observability. Training teams on data concepts, embedding observability into deployment workflows, and fostering a culture of accountability are as crucial as the technology itself.

Read more: How data-driven decision making is shaping the next generation of industry leaders

Beyond technology: A cultural shift

While technology enables observability, it’s people and processes that sustain it. Organizations should invest in educating teams about data observability practices and embedding monitoring, alerting, and logging protocols to enhance enterprise data stewardship throughout the data lifecycle.

Observability practices don’t just reinforce data trust at a technical level; they also cultivate confidence among teams and stakeholders, embedding it into the organization’s culture. Beyond fixing errors, data trust also fosters a proactive culture of data integrity and strengthens data transparency across all levels of the organization. For example:

  • Regularly review deployment workflows to ensure data quality checks are integrated.
  • Encourage cross-functional collaboration to identify potential blind spots.

The future of data trust

As AI/ML models evolve, data observability practices will expand to address decentralized systems like IoT and edge computing, ensuring secure data management in these emerging ecosystems. Emerging trends like metadata observability and sustainability-focused data practices will further reshape how businesses ensure data trust.

Building and maintaining data trust ensures organizations can rely on their insights to drive sustainable growth. In a data-driven world, the question isn’t whether to prioritize data trust—it’s how quickly you can achieve it. With a robust observability framework, organizations can ensure data is fresh, accurate, and complete, driving confident decision-making and sustainable growth.

The question isn’t whether you can afford to implement observability—it’s whether you can afford not to.

Ready to start building data trust? Explore our data analytics solutions today.