As businesses accelerate their digital transformations, the concept of data observability is quickly achieving momentum. While many companies are integrating automation tools into their daily operations and deploying massive hybrid or cloud-native systems, it has become intrinsically challenging to run and monitor all the assets. With that said, let’s dive into why observability data tools have become the answer.
With microservice-based systems growing and scaling dynamically, the actual difficulty is to make sense of data in real-time within the context of a complex technological stack, all while analyzing the effect on users and avoiding business-impacting issues immediately. It has become evident that the difficulty of this challenge will soon exceed the capabilities of even the most talented individuals.
Here’s where data observability tackles the problems of cloud-native apps by offering a more efficient method of gathering data from all system components to get comprehensive and helpful visibility.
What data observability is and why you strongly need to consider it
Data has become extremely important when it comes to decision-making and product development. As a result, information reliability and quality are top priorities for data engineering and analytics teams. Since measuring up to current user needs and market trends is essential, organizations are increasingly embracing data observability. This concept helps to monitor the health of their most essential analytics while also fostering corporate confidence in data at scale. In addition, such an approach assists data teams to minimize and avoid data downtime by providing visibility, automation, and timely alerts.
If you need more reasons to consider it, evaluate the amount of data your company is constantly acquiring. In reality, an organization will only keep on adding more (often third-party) assets; therefore, investing in data observability is crucial. Moreover, newer and more information will inevitably lead to more complicated pipelines, raising the chances for incorrect or stale data to impact your business.
With that said, let’s discover the essential facts that most business executives miss when considering data observability.
1. Data observability was inspired by DevOps
Over the last few years, DevOps engineers have worked hard to prevent software downtime; that’s why data observability follows its principles and best practices. Observability, like DevOps, employs a security blanket for data, inducing proactive monitoring and alerting. By following these principles, business executives can better detect and assess data quality, establish trust with other teams, and set the basis for a data-driven company.
2. Missing essential factors that contribute to data outages
Unfortunately, data downtime is one of the biggest pain points for business leaders. However, knowing what may cause the issue is extremely helpful. So, let’s start by identifying each factor:
- The more providers you have, the better chances for your data to be inaccurate or go missing
- Data pipelines’ complexity will grow along with the number of sources you have
- The more data you move, the more chances something may go astray
- The increasing number of data consumers being piped into more dashboards raises the possibility of breakage
3. Bad data is worse than no data
A big data engineer will tell you that bad data is deceptive. Even if testing is employed, it’s hard to notice when malicious data enters your ecosystem via APIs or endpoints. Consequently, bad data might go unnoticed for an extended period, resulting in inaccurate reporting and incorrect decision-making. When that happens, people tend to work extra hours, and while analytics and dashboards are misleading, business leaders miss the opportunity to solve problems and pursue higher profits.
So, bad data has a tremendous impact on organizations in terms of credibility and productivity. Here, data observability provides insights into your information quality and health, allowing you to detect and reduce errors as early as possible. Furthermore, by decreasing the cycle time of error resolution, data observability enables engineers and end-users to feel confident about the reliability of your assets.
4. Protect your data with the right approach
Business security should always be a priority when selecting a data observability platform. Consider solutions that rely on read-only info access and ensure SOC 2 certification first. How does the SOC 2 procedure help? It is applied to any SaaS company or tech service provider and requires building a data platform with security in mind. The SOC 2 certification is based on five criteria, namely availability, safety, processing integrity, privacy, and confidentiality.
5. Data observability is more than testing or monitoring
Because unit testing is unable to detect or predict all potential issues, creative data teams combine testing with ongoing monitoring and observability throughout the pipeline. They apply machine learning to monitor, analyze, and anticipate downtime using automatically produced controls and route warnings for quicker resolution.
In addition, observability offers a broad vision of where your data has come from, the changes made to it, the interactions received, and how it has helped your end users. All this information makes it easy to understand the current state of your data across every domain. So, when downtime does happen, your team will be well-prepared to pinpoint the source and react quickly, freeing up time for creativity and problem-solving.
Understanding the health of your data
What’s the best way to monitor the health of your data? Yes, the answer is observability. Companies increasingly rely on rich, unstructured information to support decision-making; so, accuracy and reliability are essential. Furthermore, a data engineering team applying DevOps observability principles sees an increment in the level of visibility into the health and security of its assets. Therefore, data governance principles will ensure the efficient use of information, enabling an organization to achieve its goals by focusing on the following data observability pillars:
- Freshness. With thousands of reasons for a pipeline to break, freshness is one of the top ones. It relates to how updated your data is and the gaps in time that can lead to more issues.
- Distribution. This refers to your information falling into the expected range while also reviewing the quality of the data at a field level.
- Volume. This indicator becomes crucial to evaluate whether your data intake is reaching expectations. It also indicates the completeness of your info tables and notifies about the health of your sources.
- Schema. It’s usual to find that schema changes are the cause of data downtime incidents. As part of the data observability architecture, having a robust audit of your schema is a functional approach to evaluating your assets’ health.
- Lineage. This final pillar is possibly the most comprehensive component combining all four preceding data observability pillars into one. Lineage creates a map of your data ecosystem that speaks to big data governance, letting you know exactly where and how information has been managed to understand its health level.
The five pillars of data observability help alert the data engineering team of any issues arising immediately without harming the business. By literally observing and exposing information downtime problems as soon as they occur, data observability provides the complete foundation required for genuine end-to-end reliability.
Characteristics of great data observability
Observability is vital in an ever-changing big data environment. However, when embracing it, how can you know if your solutions are effective? Below we outline some characteristics of exceptional data observability practices that can help you assess your efforts:
- First and most important, you don’t have to make a significant investment since it should connect to your existing stack quickly and smoothly, without requiring any changes to your pipelines, codebase, or programming language.
- It will not require the data to be extracted from its present location. As a result, solutions are fast, scalable, and cost-effective. It also guarantees that you adhere to the strictest security and compliance standards.
- It will adopt a holistic picture of your data and any given issue’s possible impact rather than individual measurements. Therefore, you won’t have to spend time and money setting up and maintaining harsh rules.
- It won’t ask for prior mapping. You will get comprehensive observability with little effort.
- It will anticipate problems by revealing detailed information about information assets, allowing for responsible and proactive adjustments before any issues take place.
Organizations that wish to maximize the use of big data and remain competitive must go beyond simple monitoring and embrace observability. Contact us with all your questions and embark on the journey of upgrading your data’s health level to keep operations predictable and cost-effective.
