Companies like yours collect and store data for analytical activities such as business intelligence (BI). However, many organizations don't know how to harness the power of data or improve the efficiency of analytics. Data observability solves the problems of the modern data infrastructure by helping you understand the current state or health of data in your enterprise. You can detect and diagnose issues that might occur during data integration and improve data quality for analysis. 

Here are four things to know about data observability:

  1. Data observability lets you understand the state or health of the data in your organization. You can make data work for your team and identify problems that result in data downtime or poor analysis. 
  2. You can get a 360-degree overview of data, understand how data changes over time, and diagnose data-related problems when incorporating data observability into your organization. 
  3. Data integration tools automate the data observability process by checking for inaccuracies and dependencies, detecting data anomalies, and removing data quality issues. 
  4. Data observability gives you more value from data when using ETL, ELT, ReverseETL, CDC, and other data integration methods.

This blog post explores the benefits of data observability and how to incorporate it into your organization. You will also learn how a data warehousing solution like automates tasks associated with observability and makes it easier to move data sets from a source to a target system for analysis. 

Table of Contents

Data Observability, Explained

Data observability is the process of understanding the current state of all the data in your organization. That data might exist in business software and systems such as:

  • Relational databases
  • Transactional databases
  • Customer relationship management (CRM) systems
  • Enterprise resource planning (ERP) systems 
  • SaaS tools
  • Apps
  • Social media platforms

Data observability is a critical concept for moving data from one of these systems to a central repository like a data warehouse or data lake for analysis. For analysis to be successful, data needs to be accurate, consistent, and in the correct format for a repository and any BI tools that carry out analytics. The data observability process involves: 

  • Checking data assets for inaccuracies and inconsistencies
  • Ensuring data conforms to the requirements of the target location
  • Confirming data complies with any data governance legislation in retailer's jurisdiction or industry (GDPR, CCPA, HIPAA, etc.)
  • Detecting any data-related problems that could result in data downtime 
  • Improving the reliability of data analysis
  • Eliminating data complexity 
  • Scaling data usage

Carrying out the observability tasks above can lead to better quality data analysis and more accurate and insightful BI for decision-making and problem-solving in your organization.

Read more: How Do I Enrich My Data: Data Management and ETL

What Problems Does Data Observability Solve?

Many retailers want to generate actionable insights about sales, marketing, customer service, inventory management, and other day-to-day tasks. The easiest way to achieve this goal is to move data from its source to a repository and run that data through BI tools. However, problems can persist for companies with poor-quality or inaccessible data, especially when that data exists in silos or comes from legacy business systems:

  • Corrupted, inaccurate, and duplicated data sets can skew data analysis and lead to poor-quality BI that hinders, rather than helps, organizations.
  • Complex data sets exist in separate systems that don't "communicate" with one another, making it hard to compare that data and identify patterns and trends.
  • Data exists in formats not accepted by data warehouses, data lakes, or BI tools. 
  • E-commerce retailers can't process specific data sets because of data governance legislation. 

You might need data observability if you have illegible, erroneous, or out-of-date data and don't know whether that data will result in poor-quality analysis. Observability also proves valuable if you have numerous data sources, large volumes of data, or need to comply with service level agreements (SLAs) or data governance frameworks. Investing in observability improves the quality of analysis and provides full visibility during the integration process. 

What Happens During the Data Observability Process?

Data observability used to be the domain of DevOps, allowing software engineers to monitor the health of data in applications. In recent years, observability has become more prevalent in data engineering and science circles, helping data teams make sense of all the data in their business software and systems. 

No two data observability pipelines are the same. However, data teams will typically carry out observability to achieve the following goals, as described in the five pillars of data observability: 

  • Data teams will determine the 'freshness' of data tables and eliminate out-of-date data that might influence analysis.
  • Teams will determine the 'trustworthiness' of data in tables and whether data is within an accepted range. 
  • Teams will generate insights about the health of data sources in data systems and the completeness of tables. 
  • Teams will monitor changes in schemas (the organization or data) and detect broken data sets that could impact analysis.
  • Teams will identify the cause of data breakage and ensure data complies with any data governance principles.

Read more: Futuristic Ecommerce Data Trends--Are They Within Our Reach?

Benefits of Data Observability

Here are some advantages of investing in observability for your organization:

Get a Complete Picture of Data

It's difficult to know the state of data in your enterprise when there are multiple data sets in several business systems. If data exists in silos or legacy systems, it's even harder to determine whether that data will generate value or lead to successful analysis. 

Data observability provides a 360-degree overview of all the data that flows in and out of your organization. You can identify your most critical data sets, discover the best data sources for analysis, and uncover any possible bottlenecks that might affect analytical processes. That helps you build better big data pipelines for moving data from sources to a repository like a data warehouse (Snowflake, Amazon Redshift, Google BigQuery, etc.) and achieve more accurate BI. 

Without observability, there's no way to ascertain the quality of data sets or predict future data-related problems like data downtime. You might complete the data integration process without knowing whether your data will do what you need it to do. 

Understand How Data Changes Over Time

Moving data from a source to a repository generates a current snapshot of data in your organization without providing insights into how that data might change over time. What if someone makes changes to data sets in a relational database? How will that impact analysis? What if data tables change in the future? Will that influence BI? Data observability provides answers to these questions.

Data teams use metadata and query logs to understand data during observability. These processes provide context to data sets and help you manage changes to those data sets over time. You can execute more successful data analysis even if someone modifies data sources. 

Detect and Diagnose Data-Related Problems Early

Imagine the scenario. You move data from a relational database to a data warehouse and then run that data through a BI tool like Looker. Unfortunately, much of the data in the relational database is incomplete, erroneous, or inaccurate, which impacts the quality of data analysis. In this scenario, you might need to complete the entire data integration process and build expensive new data pipelines from scratch. 

Data observability can prevent the above scenario from happening. Data teams detect and diagnose data-related problems during observability, preventing bad data from impacting your business. Teams will ensure data sets are complete, error-free, and accurate, reducing the likelihood of data downtime and other issues. Therefore, observability can save your organization time and money. 

Improve SLAs and Data Governance

Data observability improves SLAs with stakeholders by monitoring data and maintaining its quality. You can guarantee partners that any exchanged data will be fresh, error-free, and compliant. Observability also helps you comply with data governance frameworks in your jurisdiction or industry. Ensuring data quality at all times will prevent expensive penalties for non-compliance with frameworks like GDPR, which can be as high as 10 million Euros or 2 percent of your entire global turnover for the preceding fiscal year, whichever is higher. 

Reduce Data Downtime

Data downtime refers to the amount of time data is unavailable because of errors, inaccuracies, or inconsistencies. This downtime prevents data teams from analyzing and operationalizing data for sales, marketing, customer service, inventory management, and other day-to-day tasks. Data downtime might occur when there are outdated tables, incorrect schemas, or other problems during data management lifecycles.

Data observability can reduce data downtime by monitoring data inputs and outputs and ensuring data remains of the highest quality at all times. Data teams will constantly check data for potential issues that might impact your teams and their ability to do their jobs properly. As a result, you can prevent lost time and resources and improve data management processes across your organization. 

How Data Warehousing Integration Solutions Enhance Data Observability

Like with DevOps, data observability in a data science context requires talented engineers to check for data inaccuracies, inconsistencies, anomalies, and other factors that might impact analysis. These manual processes can take weeks or even months and cost companies thousands of dollars in fees. Smaller enterprises without a data engineering team will need to hire engineers to improve data observability—an expense many of these companies can't afford.

Data warehousing integration solutions can provide a solution. These platforms move data from a source to a target location via low-code/no-code data connectors, requiring little effort from retailers. As a result, there's no need to hire an expensive data engineer or build complicated data pipelines manually. As data moves from its source to a repository, data warehousing integration platforms will automatically review data schemas, remove bad data sets, check data sets comply with data governance frameworks, and ensure data is in the correct format for data analysis. 

Here's an example of how these platforms can improve data observability during the Extract, Transform, Load (ETT) data integration process:

  • The platform extracts data from a source like a relational database and places it in a staging area. 
  • It then transforms the data into the correct format for analysis. It also automatically cleanses data, measures data outputs, remove inaccuracies, reviews schemas, ensures data complies with data governance frameworks, and identifies any data anomalies that might cause problems in the data lifecycle. 
  • It loads the data into a repository like a data warehouse. 
At this point, companies can run data through BI tools like Tableau, Looker, and Microsoft BI with the knowledge their data is high-quality, compliant, and free from errors. 

Here's an example of how a data warehousing integration platform can improve observability and measure the health of your data during ReverseETL:

  • The platform extracts data from a target system like a warehouse.
  • It transforms the data into the correct format for an operational system like Salesforce. It also automatically cleanses data, checks for inconsistencies, reviews schemas, and ensures data complies with data governance frameworks. These processes prevent data-related problems from happening in the future. 
  • It loads high-quality data into the operational system. 

At this point, teams can operationalize data in the business systems they are already familiar with. 

Unlike manual data observability, data warehousing integration platforms do all the hard work, removing the need for data engineers and data analysts. You can solve data reliability issues, detect data-related problems, and protect data from external dangers like data breaches. The best platforms send alerts when data anomalies pose a threat for analysis and help you improve data management across your organization. 

Read more: What Is a Data Pipeline and Why Your Business Needs One

How Helps With Data Observability is a data warehousing integration solution with various observability features, making it easier to:

  • Get a complete picture of your data
  • Understand how data changes over time
  • Detect and diagnose data-related problems early
  • Improve SLAs and comply with data governance frameworks like GDPR, CCPA, and HIPAA
  • Reduce data downtime

This platform has a simple philosophy: To remove the jargon and complex processes associated with data integration.'s no-code/low-code data connectors eliminate the need for manual pipeline building and observability, helping you take control over the data that exists in your enterprise. You'll find data connectors for various sources and target systems, allowing you to improve the flow of data as it moves from one location to the next.

Whether you want to integrate data via ETL, ELT, Reverse ETL, or CDC, you can improve observability and determine the current state of data in your business systems. Moreover,'s drag-and-drop point-and-click interface enables you to carry out complicated data integration tasks without a steep learning curve. 

Here are some other benefits:

  • The data observability solution conforms to all major data governance frameworks, preventing expense penalties for non-compliance. 
  • Contact an team member via phone, email, or live chat. 
  • Safeguard data with world-class security, including data encryption, constant verification, and SOC 2 compliance. 
  • Move data from Salesforce to a target location and then back to Salesforce again. 
  • Create simple workflows that define dependencies between tasks. 
  • Create a bespoke REST API with DreamFactory.