What is Data Observability?

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Today, organizations have to rely more on data to make sound decisions, so high-quality data must flow promptly. This data moves around the organization through what is known as data pipelines. Data pipelines act as the central highways through which data moves around.

But how do organizations ensure that data in these pipelines are reliable and effective? Here comes data observability!

Beyond just monitoring, data observability focuses on managing the health of your data. It helps make sure that the flow of data is not only reliable but effective.

Drivers Behind Data Observability

In recent years, data volumes have continued to grow exponentially due to digital transformation and our modern digital economy.  An article on  Forbes reports that there are approximately 2.5 quintillion bytes created daily by internet users around the world.

A major contributor to data growth is the replication of data across an organization, oftentimes for analytics. 

With all this data flowing in an organization and business recipients becoming increasingly reliant on data delivery, DataOps and data observability are playing a highly critical role in everyday business operations.   

Disruptions in the flow of effective data can reduce the business team’s ability to make important decisions and take action in a timely manner.

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Data Observability Defined

The term ‘data observability’ is closely related to dataOps, but while dataOps is broad and generally about operational processes and sets of practices around automating data pipelines, data observability is narrowed to “the ability to track and manage the health of your data ensuring that the data is flowing and can be used properly.”

The truth is monitoring systems, networks, and applications is not a new subject.  But an important lesson learned from doing that applies to data observability – the need to get a holistic and impactful view of your entire data stack.

Data observability is managing, -not just monitoring the health of your data. Data observability helps organizations have complete visibility into their data pipelines to gain full context into their health.

So what are those things that necessitate the need for data observability, the drivers behind data observability?

Let’s consider this question next.


What Does Data Observability Give Me?

Data observability helps improve your DataOps processes by:

  • Ensuring data is properly delivered in a timely manner for faster decisions
  • Increasing the usefulness, completeness, and quality of data for more accurate decisions with full context
  • Delivering greater trust in data so the business can make more confident data-driven actions
  • Improving the responsiveness of the DataOps team to the business and meeting promised SLAs.

So the next logical question is, what do we then track, what kind of questions should we ask?

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What Do We Track with Data Observability?

When it comes to the health of your data, the problems go beyond questions such as “did a data pipeline run and deliver its payload.” 

Data observability incorporates these additional questions such as:

  • Did the data arrive on time?
  • Did all the data arrive?
  • Where was the data delivered to?
  • Was the data in the right format?
  • How did the data come into the final format?
  • Is the data at risk in any way?
  • What is the degree of data quality?
  • How useful and complete is the data?

The answers to these questions provide a complete view of the health of your data and data pipelines.  It also allows your organization to measure the effectiveness and operative use of your data.  

Let’s explore each of these in more detail.


Delivering data on a timely basis ensures that analysts and business teams are working from fresh data to make their decisions and see trends as near to real-time as possible.  To ensure timeliness, DataOps teams need to automate and run data pipelines as often as the infrastructure allows and monitor for their clean execution.


Erratic data volume production in data pipelines can be an indicator that the pipelines are broken and can create unforeseen holes in the resulting analytics.  Not only do DataOps teams need to monitor overall data volume, but they also need checkpoints at different points within the pipeline to drill down and identify where data pipelines are broken.


Data pipelines can have multiple delivery points for both the finished and intermediate datasets, and data pipelines can also be extended by analysts to produce derivative datasets.  

DataOps teams need to monitor if datasets are being properly delivered to their destinations and what those destinations are to ensure proper use of the data.


A data pipeline with multiple sources and destinations will work with and deliver data in different formats.  DataOps teams need to monitor for format and schema changes, keep them from breaking pipelines, and adjust the pipeline logic as needed.

Data Lineage

The end-to-end lineage of a data pipeline is important for many reasons, including data governance, regulatory compliance, and building trust in the data.  DataOps teams need to have and publish a complete, detailed data lineage that tracks every source, transformation, and destination.

Data Risk

Data risk takes into account the risk of exposing data from security, privacy, and regulatory control.  While data privacy teams may manage this overall process, DataOps teams should continuously monitor, assess, and govern the risk within their data pipelines.

Data Quality & Consistency

Incomplete and inconsistent data creates potential holes in the end analytics leading to less than optimal decisions and low trust in the data by the business.  DataOps teams need to constantly measure and monitor data quality and completeness, and be able to drill down, identify, and fix problems.

Data Completeness

In the same way, poor data quality can hinder data use and trust, data completeness can improve accuracy and context of decisions.  DataOps teams need to monitor the completeness of the data and collaborate with analytics and businesses to maximize usefulness and completeness.

What we’ve seen so far about data observability is similar to data governance, but are they the same? Let’s explore quickly.

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Is Observability Part of Data Governance?

A logical question one might ask is: shouldn’t data observability be a part of data governance?  Previously, we looked at the essentials of data governance, we obviously saw some convergence, but the two circles do not completely overlap.  

Data observability and data governance need to work harmoniously, but each has a slightly different focus and may often be operated by different teams.

Do I Need a Separate Data Observability Tool?

There are independent data observability tools emerging on the market, many being from early stage startups.  So this begs the question: do I need a separate data observability platform?

Some organizations build their data stacks with a complex set of multiple platforms and tools, perhaps with open source ones, and perform data movement in one tool and data transformation in other places.  Often the tools in this stack do not have integrated data observation capabilities, forcing an organization to explore independent data observability tools to navigate the complexity.

Deep and well-rounded ETL and data integration platforms such as Datameer have a complete suite of data observability tools that cover all the aspects we have outlined here in addition to the other data integration, DataOps, and governance features.  The integrated data observability capabilities are closely linked to the rest of the platform, ensuring seamless monitoring, measurement, and drill down.

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Data Observability with Datameer

Datameer provides all the key data observability capabilities discussed here, including:

  • Complete monitoring and auditing of all statistics and details of data pipeline execution, including timeliness and volume
  • Full visibility and drill-down into the formats of data in a pipeline from sources to destination
  • Data lineage with drill-down into each source, transformation, and destination
  • A complete view on the data security and privacy aspects of the data within each pipeline for data risk assessment and observation
  • A rich and detailed set of data profiling at every point in the pipeline for data quality monitoring
  • The largest suite of data transformation, enrichment, organization, and aggregation functions of any data integration tool for data completeness
  • Datameer enhances project documentation and discovery with its automated AI-powered solution.
  • Easy Cloud File Storage Integration easily integrates files from cloud storage into and out of Snowflake with auto table materializing and advanced scheduling.
  • Promote data quality across your organization with Datameer’s collaborative data monitoring feature.
  • Job Management; Create, manage, and monitor Cloud Data Warehouse jobs effortlessly, all within a Datameer’s secure and organized framework.

Read more about these capabilities in the following white papers:

Or, experience Datameer’s data observability first-hand by scheduling a personalized demo.

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