The Data Observability Pillars: The piece you need to know to manage your data

Traditionally, data engineers have often prioritized the creation of data pipelines over comprehensive monitoring and alerts. Delivering projects ahead of established deadlines and budgets have taken precedence over long-term data health.

The consequences have been a gradual degradation of data performance or quality, which can lead to problems that ripple throughout a company’s processes. This is where observability comes in, which reveals hidden bottlenecks, optimizes resource allocation, identifies gaps in the data pipeline, and transforms firefighting into prevention. Here are all the details!

Data Observability is the process by which enterprise data is monitored, managed, and maintained for health, accuracy,y, and usefulness.

It involves understanding an enterprise’s data’s health and quality across the entire data ecosystem. It includes various activities beyond traditional monitoring, which only describes a problem, and helps identify, troubleshoot, and resolve data issues in near real-time.

The main function of these tools is to anticipate potential problems generated by incorrect data, which is essential for data reliability. They enable automated monitoring, classification alerting, tracking, root cause analysis, logging, data lineage, etc. All of these work together to help better understand end-to-end data quality.

Gartner estimates that “by 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility into the state of the data landscape, up from less than 20% in 2024.”

This is why implementing a Data Observability solution is so important for modern data teams, where this data is used to gain insights, develop machine learning models, and drive innovation. This will be crucial to ensure that data remains a valuable asset rather than a liability.

To do this, it must be integrated uniformly throughout the data lifecycle, so all data management activities involved are standardized and centralized across all teams for a clear, uninterrupted view of issues and impacts across the organization. This is helping the evolution of data quality, which is making the practice of data operations or DataOps possible.

Data observability is based on five pillars that provide valuable information on data quality and reliability:

1. Freshness: describes the degree to which the data is up to date and how often it is updated, as data obsolescence occurs when there are significant gaps in time when it has not been updated.
2. Distribution is an indicator of data health. It refers to whether or not the data falls within an accepted range. Deviations from the expected distribution may indicate data quality issues, errors, or changes in the underlying data sources.
3. Volume is the amount of data generated, ingested, transformed, and moved through various processes and channels. It also refers to the completeness of data tables, as volume is a key indicator of whether or not data ingestion meets expected thresholds.
4. The schema describes the organization of the data, and observability helps ensure that the data is organized uniformly, compatible with different systems, and maintains its integrity throughout its life cycle.
5. Lineage: examines the data from its origin to its final location and notes changes.

Although it is a worrying fact, the reality is that most organizations believe that their data is unreliable. This can be very dangerous, as the impact of incorrect data comes at a high cost.

It used to be difficult to identify bad data until it was too late, as companies could operate with bad data unknowingly for quite some time. Therefore, data observability is the best defense against incorrect data leakage, as it ensures complete, accurate, and timely delivery of data, which avoids downtime, as well as ensures compliance and trust.

Modern data systems provide access to a wide variety of functions that allow users to store and query their data in a variety of ways. But there is a downside: the more functions you add, the more complicated it becomes to ensure that the system works properly.

In the past, data infrastructure was built to handle small amounts of data and was not expected to change much. Now, we find that many data products rely on internal and external sources, which, coupled with the sheer volume and velocity at which this data is collected, can lead to unexpected deviations, schema changes, transformations, and delays.

If new data from external sources is incorporated, all such data needs to be transformed, structured, and aggregated into the other formats to make it usable, otherwise, a domino effect of subsequent failures would occur.

In addition, complex ingest pipelines have created a marketplace of tools to simplify this end-to-end process by automating the ingest and extraction, ETL, and ELT processes. When combined, this results in a data platform that the analytics industry has dubbed the “modern data stack” or “modern data stack” (MDS). Its goal is to reduce the amount of time it takes for data to become usable for end users, so they can start leveraging it faster. But, the greater the automation, the less control you have over how data is delivered, so you need to create customized data pipelines to better ensure that data is delivered as expected.

To support the work of data engineers, companies are starting to invest in advanced data warehouses, big data analytics tools, and other intelligent data solutions. Despite this, these engineers face significant data-related pain points: locating appropriate data sets, ensuring reliability, managing constantly changing data structure and volumes, lack of visibility, cost overruns, poor forecasting, and maintaining high operational performance…

To address these challenges, data observability platforms offer powerful and automated data management capabilities. Not only that, they also offer reliability, discovery, and AI-driven data optimization capabilities that ensure data accuracy, reliability, and integrity across the entire data stream.

Key benefits include:

• Improved data accuracy: Companies can improve the reliability, accuracy, and trustworthiness of their data. This also enables confident reliance on data-driven information and ML algorithms to make informed decisions and develop data products.
• Faster troubleshooting: Data observability enables teams to quickly identify errors or deviations in data through anomaly detection, real-time monitoring, and alerts. This helps minimize the cost and severity of downtime.
• Downtime prevention: provides businesses with relevant information and context for root cause analysis, which in turn helps prevent data downtime.
• Improved collaboration: by using shared dashboards that provide data observation platforms, different stakeholders can gain visibility into the status of critical data sets, which can foster better collaboration across teams.
• Compliance: can help organizations in highly regulated industries ensure that their data meets the necessary standards of accuracy, consistency, and security.
• Improved customer experience: high-quality data is essential for understanding customer needs, preferences, and behaviors, which will enable companies to deliver more personalized and relevant experiences.
• Cost optimization: provides analysis of data flows and processing that can be used for better resource planning. This helps eliminate or consolidate redundant data, misconfiguration,s and over-provisioning, leading to better utilization of resources as well as optimization of data investments.
• New business opportunities: by improving data quality through observability, organizations can identify trends and uncover potential revenue-generating opportunities.

Data observability supports and enhances Data Quality, although they are different aspects of data management.

The latter refers to the accuracy, completeness, consistency, and timeliness of data. For its part, observability enables monitoring and investigation of data systems and channels to develop an understanding of data health and performance. But both work in synergy to ensure data trust.

The fields of data quality and observability converge to create a comprehensive framework to ensure the reliability, accuracy, and effectiveness of an organization’s data-driven initiatives. In fact, they share common factors for optimal results:

• Shared focus on accuracy.
• Real-time monitoring for quality assurance.
• Proactive problem detection that improves quality.
• Root cause analysis and data integrity.
• Holistic data excellence through collaboration.

However, they play different roles in ensuring that the data are accurate, reliable, and valuable:

Source: Atlan

Although observability practices can point out quality problems in data sets, they alone cannot guarantee good data quality. For this, efforts are required to fix data problems and prevent them from occurring in the first place.

In addition, a very important concept would also enter here, which is data governance, as a strong governance program helps to eliminate silos, integration problems, and poor quality that can limit the value of data observability practices.

Therefore, all three will be critical in having a robust, reliable, and compliant data strategy.

Data observability is fundamental to effective DataOps, a practice that enables agile, automated, and secure data management. In addition, ignoring data quality can have serious consequences that hinder a company’s growth. Without the benefits of this practice, it will not be possible to optimize and manage data, leading to risks such as:

• Reduced efficiency: poor data quality can hinder the timeliness of data consumption and decision-making, reducing efficiency. In fact, studies show that the cost of poor data quality to the U.S. economy could amount to $3 trillion in GDP.
• Missed opportunities: companies can face reliability issues that prevent them from delivering effective data products to both customers and external stakeholders. Unreliable data results in inefficient or inaccurate data, which is detrimental to users and results in lost opportunities to interact and develop incremental revenue channels.
• Reduced revenue: bad data can directly affect a company’s revenue. If data teams cannot see where data is being used and how they are being charged for consumption, significant cost overruns and misallocation of charges are likely to occur.

As data becomes increasingly critical to business success, the importance of data observability is gaining recognition. With the emergence of specialized tools and an increased awareness of the costs of poor data quality, companies are now prioritizing this practice as a core component of their structure.

Observability allows data engineers to focus on the technical aspects of moving data from various sources to a centralized repository, in addition to taking a broader, more strategic approach.

At Plain Concepts we have extensive experience and expertise in data strategies that will help you optimize pipeline performance, understand dependencies and lineage, and streamline impact management. This will ensure better governance, efficient use of resources, and reduced costs.

You will be able to proactively identify potential problems in your data sets and channels before they become real problems. This will result in a healthy and efficient data landscape, mitigating risks and achieving a higher ROI on your data and AI initiatives.

We offer you a Data Adoption Framework to become a data-driven company. We help you discover how to get value from your data, control and analyze all your data sources, and use data to make smart decisions and accelerate your business:

• Data analytics and strategy assessment: we evaluate data technology for architecture synthesis and implementation planning.
• Modern analytics and data warehouse assessment: we provide you with a clear view of the modern data warehousing model through understanding best practices on how to prepare data for analysis.
• Exploratory data analysis assessment: we look at the data before making assumptions so you get a better understanding of the available data sets.
• Digital Twin and Smart Factory Accelerator: we create a framework to deliver integrated digital twin manufacturing and supply chain solutions in the cloud.

We will formalize the strategy that best suits you and its subsequent technological implementation. Our advanced analysis services will help you unleash the full potential of your data and turn it into actionable information, identifying patterns and trends that can condition your decisions and boost your business.

Get the most out of your data now!