September, 2022 | NextPhase

The world of data observability is growing rapidly because of the maturing nature of data reliability. The more data becomes complex, the harder it is to maintain its integrity, and it seems like new innovations and techniques are being developed every day. That is one more reason why having a strong data observability system or policy is critical to your success. Everyone from client-facing teams to functional leaders relies on quality data for day-to-day operations. Without some method of ensuring that information is up to date, accurate, and consistent, you run the risk of losing out on valuable insights. What is Data Observability? At its core, data observability helps companies monitor, manage, and track data throughout their various data transformation stages. It can help mitigate the risk of data downtime, and reduce data drift enabling the Data Management team to triage for potential issues that may pop up in the short or long term. The goal is to diagnose your data’s health throughout the lifecycle using a comprehensive process and approach that allows you and your team to resolve any real-time data issues. This way, the information being presented to data consumers is reliable and accurate. It can be highly embarrassing, not to mention harmful to a company that uses poor data quality for decision-making. To put it simply, data observability allows you to monitor and manage the health of your data lifecycle through notifications and alerts. Importance of Data Observability Organizations all over the world are competing in a fast-paced marketplace, often without the aid of a CTO or IT team that can introduce tools using connected infrastructure. That ends with these companies having to rely on disconnected systems where data can get changed or manipulated as it travels from one platform to another. When this is changed by instituting end-to-end visibility of your data throughout the entire lifecycle, you are better able to uncover root cause issues. This also reduces any bottlenecks and empowers you to triage the problems by actively observing a broad range of outputs. With data observability, you can quickly identify potential issues as well as automate some of your triage processes to avoid potential downtime. This is a comprehensive view of your data system that ensures you have better control and maintenance capabilities of your high-quality data. Defining Pillars of Data Observability For data observability to be efficient and produce the outcomes you would want for finding any issues or repairing any data problems, you need to have the core group of pillars in place. These include: Freshness: tracking how often your data is allowed to update so you can eliminate any stale or outdated information. Distribution: this allows you to record expected data values so you can better determine when information is unreliable. Volume: determines any incomplete data by actively tracking expected values for confirmation. Schema: identifies broken data by observing changes to tables and data organization. Lineage: collects metadata as well as maps sources and movement to uncover breaks or bottlenecks. With these pillars in place, you will be better able to determine metrics, traces, and logs (what is sometimes referred to as the traditional 3 pillars of observability). These are thought of as what detects problems, how to diagnose issues, and the “why” behind the underlying problem. The best option is to integrate your data observability dashboard into your entire infrastructure, so you gain a comprehensive view of the whole lifecycle. Data Observability vs. Data Quality Think of these two attributes as complementary rather than separate entities. You need data quality to ensure your data observability is effective. You also need observability to determine whether or not your data is up to the proper quality. While one can exist without the other, thinking of them as beneficial to each other helps with understanding their fundamental mechanisms. For example, it can be challenging to effectively test for data quality when you are limited to observability tools that do not consider the entire organization’s data infrastructure. You want a full-stack solution that ensures the data quality is maintained from end to end. One of the most significant ways to create a solid data quality process is to have a reliable data observability dashboard in place so teams can work with large datasets without the fear of broken, damaged, or mislabeled data. Trends of Data Observability As was mentioned in the beginning, there are numerous developments happening right now in data observability. The maturing technology and infrastructure are leading to integrating data observability trends like: Distributed Tracing: We are likely to see more companies integrating broader tracing tools that work in cloud-native and microservices architectures. That is likely to reflect new compliance regulations forcing higher data privacy applications of disturbed tracing. Expanding the 3 Pillars: In this article, the pillars of data observability are more open than the traditional 3 pillars of metric, logs, and traces. This is likely to continue to happen among businesses as unique needs will be addressed and applied to new processes that go beyond these pillars. Moving to Holistic Observability Tools: Organizations are likely to embrace tools that unify previously siloed infrastructure. This empowers teams to better maintain and monitor data instead of having to adjust to multiple sources or systems. Introduction of More Open-Source Elements: As data observability becomes more and more essential to standard operations, we are likely to see innovation in open-sourced tools and systems. This will hopefully motivate more competition for greater advancements in the niche, which only helps the end users. Need for Prioritizing Data Observability The whole point of data observability is ensuring your data is helpful for your organization without error. As more businesses move to a data-driven working model to generate insights and influence decision-making, we are likely to see wider adoption of these tools. It does not take a great deal of convincing to see how the proper collection, review, sampling, and process of data moving throughout an organization have to be maintained. This is especially significant for organizations moving their systems into the cloud for higher efficiency. The need to ensure any issues are quickly uncovered, managed, and reported is essential to maintain competitive operations. What is the Future of Data Observability? The more data collected to inform business operations, the greater the need for data observability. Even a business of only a few team members serving a hyper niche market has mountains of potential data from numerous sources. No one can use data effectively without ensuring high data quality. Having an effective data observability system and dashboard in place is the best way to boost your data quality. This is true for companies managing vast volumes of data while reducing silos and seeking to improve interdepartmental communication and collaboration in the future. You will likely see the same efforts occurring in data security as well. Tracking and monitoring data is an excellent way to reduce potential risk while complying with regional, local, and international privacy laws. As more companies see observability tools improving, the mass adoption of such technologies is sure to grow. That is where the expert team at NextPhase can help. We actively monitor the latest innovations and data observability trends while offering comprehensive tools that make it easy to monitor the health of your organization’s data systems and lifecycle. So, reach out to our team today, and let’s discuss how bespoke data observability tools can improve your data quality throughout your infrastructure.

Month: September 2022

Data Observability and its Pillars: A deeper look at its value to the enterprise

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