What is Data Observability?

What is data observability?

Data observability is the process by which data is monitored for health. The primary purpose of the process is to enable data engineers to provide reliable, accurate data. One way to promote data observability is automation. Automation is a way of using technology and tools to prevent users from doing everything by hand. Thus allowing you to be more efficient with your time and resources.

How did data observability come about?

The term "data observability" is derived from a broader term, "observability," which has its root in control theory.

Observability tells us how to observe the internal system of your application based on its external outputs. Conversely, control theory studies how to manipulate a system's inputs to achieve a desired output.
It is important to note that we initially traditionally measured observability by tracking basic system performance metrics like CPU usage. However, since then, this academic concept has evolved into the "shift to observability" concept, which requires much deeper insights into system behaviours through logs, metrics, and traces. Data observability came about because of new challenges like data quality, schema changes, reliability, data pipelines, integrity, and the need to know how our system operates internally. In essence, data observability tells us the degree of visibility in our data systems.

Data observability and data governance: how are they similar?

While the two concepts differ, data observability plays a role in data governance and can complement it by ensuring high data quality and integrity. This will, in turn, influence the effectiveness of business decisions made.

Data governance ensures that your data is secure, and used responsibly, and policies and standards are followed. On the other hand, data observability ensures that you have accurate and reliable data through data monitoring.

Thus, by enforcing data observability, you can ensure that your continuous monitoring efforts resolve data quality issues, therefore enforcing data governance standards. This practice also ensures that you comply with regulatory requirements, identify anomalies, and mitigate these security risks. These efforts also help with operational efficiency by reducing the manual efforts required for data management.

Why should organizations worry about data observability?

Organizations work to ensure observability so that they’re always on top of their data systems, watching for errors and quickly resolving any issues that arise. Monitoring and observability for modern services and infrastructure is critical to ensuring data quality and transferring it between systems.

Businesses that fail to adequately monitor their data often run into problems that could have been resolved relatively quickly had they been addressed straight away. Because data observability empowers organizations to monitor their data pipelines in real-time, they can easily spot errors and work to make necessary changes to the system as soon as possible. They can also identify new areas of opportunity, better optimize their systems, and reduce downtime. Thus, finding the right data observability tools for your business that can empower you to do all this and more is important.

Data observability tools

Data observability tools help simplify data observability from multiple angles. For example, if you’re looking to automate error detection, you can implement a tool that automatically scans for errors and alerts users to problems with the system. This way, rather than constantly combing through their data by hand, which can be extremely time-consuming and lead to even more errors, users can focus their attention on other things while still receiving critical notifications. In this way, a data observability platform can help you better use your time.

The data observability market size is growing rapidly and is expected to continue expanding in the near future. More and more businesses are realizing the value of monitoring their data in real-time; they are more concerned than ever with data quality and reliability. For this reason, the demand for data observability tools is growing. With so many options to choose from, it can be difficult to narrow your search, but defining your needs and preferences is a great place to start. If you prefer open source software, for instance, you should try searching specifically for data observability tools and open source solutions.

Data observability tools by Gartner

Gartner is a leading platform in tech research and provides helpful tools and insights to help organizations make smarter business decisions. The Gartner report helps companies compare data tools to find the most appropriate solution for their data observability needs. Various tools and solutions can help organizations with their data observability process and accessing resources. This, in turn, can be used to make an informed decision for business operations.

One of the tools you can explore is Gartner’s Magic Quadrant. Gartner’s Magic Quadrant helps users visualize different data-related tools and solutions, seeing how they stack up against each other and getting a better idea of what each brings to the table. To compare solutions, each quadrant in the Gartner Magic Quadrant places. This quadrant puts each solution along a spectrum of challengers, leaders, niche players, and visionaries. Thus, if, for example, an organization is looking for an observability platform that can execute even the most advanced initiatives, the solutions within the challengers' quadrant would be the most ideal for your organization.

The Application Performance Monitoring Gartner Magic Quadrant is another tool users can use to analyze APM and observability tools and decide which is best for their unique business needs. You can access the Gartner Magic Quadrant APM 2023 resource for the most up-to-date Gartner application performance monitoring information.

How do you ensure successful data observability implementation in an organization?

To successfully implement data observability in your organization, you need to understand your organization's data needs and data infrastructure.

Knowing your data needs here entails knowing where your organization is and where you want to be. It would help if you looked at your current organization's data quality stage, data sources, data types, complexity, frequency, and volume of data. As much as you try to understand your infrastructure, you also need to identify your current challenges, potential bottlenecks, and issues and understand how each component complements each other. Doing this before anything else will ensure you pick the right data observability techniques, especially ones best suited to your organization's needs and industry.

Once you have this in check, you can select a tool, preferably one that can notify you of issues and comes with real-time monitoring capabilities across cloud, on-premises, and hybrid data systems. Acceldata is a perfect example of a complete data observability solution that covers you regardless of your organization's data stacks and types.

Next, you can use the data observability pillars to create a robust data observability framework. Your work isn't done yet, though. To be successful, you need to track your monitoring efforts by regularly auditing and re-evaluating your tools and processes as your organization grows.

Data observability pillars

The key data observability pillars, sometimes referred to as the six facets of data observability, are accuracy, completeness, consistency, freshness, validity, and uniqueness. Many people erroneously believe that accuracy is the only component of quality data, but even if data is error-free, it could still be inconsistent or out of date. This is why taking a holistic, multidimensional approach to data quality is important when constructing a data observability framework. You can’t assume that just because data lacks errors, it’s necessarily useful—in truth, data observability and quality are multifaceted.

Your data observability architecture should account for all pillars of data observability and empower you to take action when needed. If something is wrong with your data, your framework should make room for adjustments, outlining the steps you should take to resolve issues as quickly and effectively as possible. It should also allow for scalability. Your data-related needs will likely evolve over time, so selecting a tool or solution that can grow alongside your business is important.

Data observability vendors

Data observability vendors provide the tools and software for users to observe their data in real-time, ensuring that it moves smoothly through the pipeline. There are multiple components of data observability, and some platforms are better at monitoring certain things than others. If you’re particularly concerned with completeness. In that case, for instance, you should look for a data observability platform that automatically checks to make sure that all data is complete and up to date. Likewise, if you want to tackle observability with open source software, you should seek out a vendor that offers data observability open source tools.

Many businesses assume that their data is fine as it is; and that it will move through the pipeline without issue. While for some, this may be the case more often than not, data pipeline observability is crucial to ensuring that nothing important slips through the cracks. Data observability vendors equip businesses with the tools to manage and optimize their data better, routinely checking for errors and responding to issues to resolve them as quickly as possible. Full data visibility is key to maintaining data quality, which can then be used to drive more informed decision-making. As such, data pipeline observability can increase revenue and aid in business growth in general.

The future of data observability

Undoubtedly, data observability will continue to evolve to address the challenge of managing and telling you what's happening within your systems.

So what can we expect in the future? Since the volume of data continues to grow, there will be more innovation and a need for machine learning (ML) and artificial intelligence (AI) algorithms in the observability space. ML and AI-powered solutions will enable you to do more. You can proactively detect issues, resolve them faster, and have a holistic view of your system. A great example is Acceldata AI technology, which enables businesses to use AI capabilities to improve the outcomes of their data observability. Are you curious about how this works? Request a demo from the Acceldata team.

Tracking and monitoring cloud-based technologies is another trend to expect, especially since organizations are adopting cloud-based infrastructure more. Thus, observability tools must be versatile and able to monitor and integrate with multiple landscapes and third-party cloud dependencies. Lastly, with the growing need for cost-effectiveness, organizations require scalable tools and a comprehensive approach to evaluating IT environment costs.

The best part of all these trends is that Acceldata is moving in this direction. Acceldata is an all-in-one data observability solution that addresses all your observability needs—from monitoring regardless of your data stack to being AI-assisted with enterprise-class security.

So, request a demo and explore what Acceldata can do for you today!