In order to draw insightful information from data, businesses need to ensure that the data they interact with is highly-available while also offering a high-quality and demonstrating integrity. While data cleansing and management processes can help tick these boxes, businesses need to be absolutely sure that their data exhibits all of the right characteristics if they’re going to use it.
Data observability is the overall practice of monitoring data, ensuring that its quality, performance, and reliability are all up to standards. Instead of a singular process, data observability is actually a series of different processes that all work together. For example, tracking, maintaining visibility, and analyzing data all fall under data observability.
In this article, we’ll dive into everything you need to know about data observability, exploring the following areas:
- Understanding Data Observability: What is it?
- What Are the Main Pillars of Data Observability?
- How to Implement Data Observability in a Business
Let’s dive right in.
Understanding Data Observability: What is it?
Data observability is the practice of monitoring and managing data in order to ensure its quality, reliability, and performance remain as efficient as possible. Especially for businesses that use data-driven decision-making, data observability is a vital practice. Beyond just monitoring data, observability also pinpoints and addresses any issues in the data.
The main advantage of data observability is that it helps to improve the quality of data. Instead of having to work with incomplete, inaccurate, or inconsistent data, observability will ensure that all data is up-to-date and accurate. Whenever an anomaly, error, or deviation from a set structure or pattern is noticed, data observability allows for an instant reaction and solution to the problem.
Data observability is a core part of data infrastructure, allowing businesses to streamline data analytics, identify bottlenecks, and better understand their own processes when it comes to using data. From root cause analysis to simply monitoring the performance of the data pipeline, data observability covers it all.
What Are the Main Pillars of Data Observability?
There are a few core pillars within data observability. These components cover each area in which data observability works, working together to create a comprehensive framework for data management.
Here are the main pillars that data observability consists of:
- Data Quality: Data quality is, arguably, the most important part of data observability. Quality checks cover help to identify any issues within data, scanning for things such as duplicated fields, incorrect values, and outliers. This stage of data observability allows businesses to be sure that the data they use is up to the necessary quality standards.
- Data Monitoring: Data monitoring takes place across the data pipeline, especially around areas where a business is transforming data. At the T stage of ETL/ELT, data monitoring ensures that the data remains healthy, precise, and correctly structured.
- Data Lineage: Data lineage is a core part of observability, allowing businesses to trace the entire story of a piece of data. This part of observability would track where data came from and how it was transformed, noting which systems it has been through. Lineage is especially important for regulatory compliance and governance, which we’ll discuss shortly.
- Data Governance: Businesses need to establish clear data governance across their organization to correctly manage, handle, and work with data. While most data governance focuses on the integrity of data, this part of observability also covers cybersecurity and privacy.
- Regulatory Compliance: Finally, regulatory compliance makes sure that the data that companies use is up to the standards of their local governments. For example, regulations like GDPR or CCPA will impact how a business stores data and what data it can collect. Observability ensures that all regulatory obligations are being fulfilled by a business.
Data observability is an incredibly large process, encompassing the entire data pipeline and creating a uniquely expansive framework to manage the quality and consistency of data.
How to Implement Data Observability in a Business
Data observability isn’t something that happens overnight. Due to how expansive it is as an overall system, businesses need to work carefully to establish a comprehensive network of observability practices.
When looking to implement data observability into a business, there are four main areas to focus on:
- Access Current Standings: Before doing anything, businesses should look at their current data infrastructure, understanding what systems they have in place and how they manage in terms of regulatory compliance. Once a company knows what they already have, it’ll be in a much better position to then begin to work out what other tools they need to employ.
- Select the Correct Tools: After taking stock, it’s time to add tools to the tech stack to cover any areas that are currently lacking in terms of data observability. For example, additional data monitoring tools, tools for visualization, lineage tracking tools, or even specific technologies like Clickhouse and Druid to support data storage. When comparing Clickhouse vs Druid, it’s important to recognize that they have distinct capabilities, meaning your business should match its tools to what it is specifically looking for.
- Build Observability In: When building your data pipelines, it’s important to establish data collection mechanisms, monitoring, and alerting within the pipeline itself. By improving your technical implementation of these tools, you’ll be able to rapidly increase observability and begin to adhere to more stringent compliance and regulatory policies.
Beyond just having the right tools, its important to make sure that your employees understand the importance of data observability. While the largest development will come on the technical side, this will be useless if your employees don’t engage with observability and introduce it into their workflows.
Holding conversations around why data observability is important and how to engage with new tools that promote observability will go a long way toward establishing a data-driven culture.
Final Thoughts
Data observability is more than just a singular process. On the contrary, it’s an entire ecosystem of processes, regulations, and strategies to manage the quality of data. Without data observability, businesses would have to deal with low-quality data that frustrates their systems, slows down analysis, and reduces the impact of data-driven decisions.
What’s more, data observability is also a vital part of data regulation and governance. Without clear data observability practices in place, a business may not know if it’s adhering to the regulation that its place of operation requires. Data observability is one of the most important parts of data management, even if it doesn’t always get the praise it deserves.
