Here are five things to know about data observability:

  1. Data observability is a process that helps you understand the health or state of the data in your Ecommerce organization. Carrying out observability identifies future data-related problems that might lead to data downtime or poor analysis. 
  2. There are various ways to improve data observability, such as creating a culture of observability, training team members, and investing in the right digital tools. 
  3. Data integration tools can improve observability by automating this process. The best tools automatically cleanse data, remove data quality issues, check for dependencies and inaccuracies, and notify you about data anomalies. 
  4. Be aware of data observability when using ETL, ELT, Reverse ETL, CDC, and other data integration methods.
  5. is a data warehousing integration tool for Ecommerce that helps you improve data observability. It automates many observability tasks and identifies data-related problems before analysis. 

Data observability solves many of the issues of modern data infrastructure. Still, few Ecommerce organizations understand this process or how to improve it. 

Here's what you need to know: Data observability, in a data science context, helps you understand the current state of all the data in your Ecommerce enterprise. It monitors and manages any problems that might occur during the data integration process. It helps you make better data-driven decisions from better business insights.

This blog post takes a deep dive into data observability and how you can improve it in your Ecommerce organization. Then, you will learn how a data warehousing integration solution like can enhance observability when moving data from one location to another. 

Table of Contents is a data warehousing integration solution for Ecommerce that implements data observability in various ways. The platform can cleanse data, check for inaccuracies, remove inconsistencies, ensure data complies with data governance guidelines, and transfer data into the correct format for analysis. Whether you choose ETL, ELT, Reverse ETL, or super-fast CDC, helps you operationalize data based on your circumstances and goals. Email for a 7-day demo and improve your data strategy.

What Is Data Observability?

In the simplest terms, data observability is the process of understanding the state of data in your Ecommerce systems. This term used to be synonymous with DevOps and referred to monitoring and tracking incidents to prevent downtime. However, in a data science context, data observability helps you understand the health of all the data existing in your organization. 

The primary goal of data observability is to ensure data quality and prevent data-related issues from occurring in the future. That can result in more successful big data pipelines, improving productivity and profitability in your Ecommerce enterprise. You can also generate better insights that grow your business. 

Here are some of the reasons you might need to improve data observability:

  • You have inaccurate, illegible, out-of-date, or erroneous data in systems and don't know whether this data will cause problems for analysis. 
  • You have numerous data sources and don't know whether the data in those systems will impact analysis. 
  • You need to comply with a service level agreement or data governance legislation.
  • You want to improve the quality of data analysis and generate more accurate Ecommerce insights. 
  • You want full visibility into data sets across the data ecosystem. 

Five Pillars of Data Observability

Towards Data Science has broken down data observability into five separate pillars so you can understand this process a little better: 

1. Freshness

Data observability helps you determine the "freshness" of your data tables, allowing you to eliminate stale and out-of-date data that could impact analysis. 

2. Distribution

Distribution tells you whether the data in your Ecommerce organization is within an accepted range and how "trustworthy" the data is in your tables. 

3. Volume 

Volume provides insights into the health of sources in your data systems and the completeness of data tables.

4. Schema

Data observability lets you monitor any changes in the organization of data—or schemas—and identify broken data sets that could negatively affect the data ecosystem.

5. Lineage

Data lineage helps you identify the root cause of data breakage by analyzing upstream sources and downstream ingestors. Following data lineage best practices can help you improve data management in your Ecommerce organization and adhere to data governance principles. 

Why Should You Improve Data Observability? 

When you improve data observability, you can understand the data in your organization better than ever before and prevent data downtime. That refers to the time lost to events involving partial, erroneous, or inaccurate data sets. Dealing with inconsistent or incorrect data can cost your Ecommerce organization time and eventually money. Improving data observability reduces or eliminates data downtime by ensuring data sets are complete, error-free, and accurate. 

Improving data observability can also help you establish better relationships with customers, clients, stakeholders, and partners. Say you want to share data with another Ecommerce enterprise or receive data from a company. You will likely have to create or adhere to a service level agreement (SLA) that guarantees that exchanged data will be accurate, up-to-date, and compliant. The company that breaks this agreement could receive penalties or jeopardize its reputation. Improving data observability helps companies adhere to SLAs when exchanging and receiving data by monitoring that data and ensuring its accuracy, freshness, and compliance. 

Improving data observability can prevent penalties for non-compliance with data governance legislation when moving data from one location to another. Ecommerce retailers might need to adhere to data governance legislation like GDPR, CCPA, and, when selling healthcare-related products, HIPAA. Each one of these frameworks imposes penalties for non-compliance with data governance principles such as data protection and sharing. is a data warehousing integration solution that implements observability by automatically managing and monitoring data for target systems. Whether you use ETL, ELT, ReverseETL, or fast CDC to transfer data, will cleanse data, improve compliance, and ensure data is in the correct format for your specific needs. The platform does all this without you worrying about jargon, programming, or code. Email for a 7-day demo. 

Read more: How Business Owners Can Use Data Integration to Their Advantage

How To Improve Data Observability

Here are some of the ways to improve data observability in your Ecommerce organization:

Ensure Data is the Correct Format for Analysis

You might have several data sources for data analysis, such as:

  • Relational databases
  • Transactional databases
  • SaaS tools
  • Apps
  • Metadata
  • Social media platforms
  • Customer relationship management (CRM) systems like Salesforce
  • Enterprise resource planning (ERP) systems 
  • Other Ecommerce data platforms

Data in these sources might exist in unique formats, making data analysis difficult. For example, a data warehouse—a data repository for data analysis—might not accept data in a particular form or structure. By transforming data into the correct format for analysis, you can observe data pipelines with greater clarity and identify any data-related problems that might occur in the future. You can also ensure data is ready to enter a repository like a warehouse, resulting in more accurate Ecommerce insights. 

Train Your Team

Data observability is more successful when team members observe data, too. Train data teams to identify duplicate customer accounts and data inaccuracies in software and systems and encourage employees to log data-related problems that warrant further investigation. 

You can also set up workflows that make it easier for team members to report data errors and other factors that might result in data downtime. Assign points of contact for different departments to report problems in real-time, and include these contacts in employee handbooks or on your intranet pages. 

Remove Data Errors and Duplicated Data

Data analytics tools are only as good as the data they collect. If data sets contain errors or duplicated data, for example, you won't be able to generate accurate insights into your Ecommerce business. 

Say you own a small Ecommerce store and want to understand your customers better. You move data from various databases to a data warehouse and push that data through a business intelligence (BI) tool like Looker. Database errors could result in poor-quality insights that impede decision-making. 

Removing data errors and duplicated data improves data analysis and helps you make more profitable decisions about your organization. 

Identify Data Issues Before Analysis

You might identify problems with data during the analysis stage—after you move data sets to a warehouse, for example, and then onto a BI tool. However, it might be too late to remedy these errors if you need metrics for an upcoming sales presentation or marketing campaign. 

One of the benefits of data observability is that it discovers data errors (and other factors that influence analysis) before you feed data into BI tools. Therefore, you can prevent problems before they affect the decision-making process. Observing data as soon as you extract it from a data source, for example, lets you identify the context in data, which results in more successful analysis. You get a 360-degree view of all the data in your pipelines before it even reaches a BI tool. 

Collect Data in Real-Time

Collecting data in real-time lets you view and detect data-related issues as they happen so you take swift action. While you won't be able to improve data observability for historical data sets, you can ensure all new data is free of errors and inconsistencies. 

Change Data Capture (CDC)—one of's many offerings—can help you manage data in real-time. This data integration method lets you compare changes made to two or more databases and revert unauthorized changes to data sets.

Create a Culture of Data Monitoring

For full observability in your organization, every team member should understand the importance of managing and monitoring data. Creating a workplace culture of data monitoring—where teams log, audit, and maintain data—improves observability and productivity. 

Don't Monitor Everything in Your Organization

Monitoring every possible data anomaly will complicate matters and add to already-existing data management workloads. Keep things simple by observing data-related issues that result in data downtime and will significantly skew the results of data analysis. 

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

How To Improve Data Observability With Data Integration Tools

Improving data observability often involves data engineers painstakingly monitoring and managing current and previous data sets in your existing systems. These engineers might review schemas, check data is in the correct format for analysis, remove bad data, and review data sets against data governance frameworks. Manual processes require lots of coding and programming and can take weeks or months to implement. Moreover, some smaller Ecommerce firms might not have the funds or resources to hire a data engineer for observability. 

Various data integration tools can help you achieve data observability without a data engineer. The best ETL platforms, for example, automate the processes associated with the data integration method Extract, Transform, and Load and offer no-code/low-code connectors that eliminate manual observability and data monitoring. Here's an example of how an ETL platform helps with observability:

  • The ETL tool extracts data from a source like a CRM system or relational database and places it in a staging area.
  • The tool eliminates manual data observability processes by automatically identifying inaccuracies, reviewing schemas, checking if data is correctly formatted, and keeping a historical record of how systems generate data before it reaches its destination (data lineage). 
  • Once data has been cleansed and transformed into the correct format, the tool loads that data into a target system, such as a warehouse.

Ecommerce companies can now run data through BI tools and generate insights about their organization. 

Read more: Top 7 ETL Tools for 2022

Platforms capable of executing other data integration methods like Extract, Load, Transform (ELT), Reverse ETL, and CDC can also prove valuable for data observability. These platforms automate data integration tasks like data cleansing, data wrangling, data remediation, data verification, and data governance, meaning you don't need to worry about manually monitoring data sets for inaccuracies and inconsistencies. That can help you avoid data downtime and carry out more productive data analysis. 

Not all data integration platforms will improve observability. Here are some features to look out for when choosing a product:

  • Select a tool that generates alerts when it detects a data-related problem that might impact analysis. These notifications will help you solve data reliability issues and ensure data remains of the highest quality at all times. 
  • Select a tool with a simple learning curve that improves the user experience for Ecommerce data employees. The right platform will automatically detect data anomalies without human intervention and help you achieve full data observability. Don't select a tool that requires you to build complex pipelines or write complicated code. 
  • Select a tool that protects your data from external threats. The best products offer enhanced security and let you comply with data governance principles. 

How Improves Data Observability

Data observability doesn't have to be a manual process. A data warehousing integration solution like offers observability features, helping you identify data-related problems that might occur in the future. It removes the need for complicated pipelines and code when observing data in your ecosystem, keeps track of all the information in your data flow, and gives you more confidence to make data-driven decisions that benefit your Ecommerce business. enhances observability during the following data integration methods: ETL, ELT, Reverse ETL, and CDC. No-code/low-code connectors and a drag-and-drop, point-and-click user interface make it even easier to manage and monitor data as you move it from a source to a target location. By simplifying the data observability lifecycle, Ecommerce companies like yours don't have to worry about data downtime, inaccurate data, or inconsistencies. Moreover, you don't need to hire a data engineer or build complicated big data pipelines. wants to streamline the entire data integration process. While manual data integration requires complex workflows, this platform enables Ecommerce retailers to operationalize their data environments and analyze data without the headache. 

Here are some other benefits:

  • You can communicate with an team member via phone, email, or live chat during the data integration process.
  •'s simple pricing model provides value for money.
  • Get world-class data security and compliance features.
  • No-code/low-code connectors with all major data repositories such as Snowflake, Amazon Redshift, and Google BigQuery. 
  • is one of the few data integration solutions that transfer data from Salesforce to a repository and then back to Salesforce again. is the data observability solution for Ecommerce. You can remove manual processes, identify data-related problems with out-of-the-box no-code/low-code data connectors, and ensure data sets are ready for analysis. Schedule an intro call now or email for more information.