Exploding data volumes and increased operational demand for data has led to significant complexity in modern data environments. There are no excuses, however, for data teams who do not successfully manage their data and put it to use towards achieving business outcomes. In today’s data environments, that means continuous awareness and understanding of data and data systems within an environment, all with the intent of optimizing data supply chains so they deliver reliable, high-quality data when it is needed.
How do modern data teams achieve this? They use data observability.
Data observability can be defined as the ability of an organization to completely understand the health of its data. Put another way, it is a systematic solution to the problem of data complexity. It monitors and correlates data workload events across application, data, and infrastructure layers to resolve issues in production analytics and AI workloads.
Implementing a multidimensional data observability platform, such as Acceldata, is the most effective and efficient approach for data engineers to ensure the health of their data and the optimal operations of their data infrastructure investments. Unlike traditional APM tools that only monitor the organization’s application layer, multidimensional data observability platforms provide visibility across your data, infrastructure, and pipelines.
What features should you look for in a data observability platform?
Our data observability checklist (at the bottom of this page) provides an at-a-glance introduction to nineteen important data observability features—all of which are core to the Acceldata platform. (Request a demo to learn more about these capabilities.) We’ve categorized these features into three groups:
Let’s take a quick look at each category.
Bad data leads to bad decision-making. Fixing the bad data problem can be extremely challenging in a business environment that’s plagued by dark, redundant, and cold data. What started out as a pristine data lake can quickly turn into a data swamp—a swamp that few are brave enough to wade through and clean up.
Implementing a multidimensional data observability platform can make it easier to predict, prevent, and resolve data quality issues, especially when it offers:
Continuously adding more environments, more technology, and more data-driven use cases can lead to an unscalable situation that is both costly and prone to outages. Rising costs paired with frequent downtime creates additional complexity, causes friction with users, and erodes return on data.
A multidimensional data observability platform can help data teams overcome the traditional roadblocks to performance monitoring by providing:
One problematic data pipeline can wreak substantial havoc on an organization’s data quality and efficiency. Unfortunately, identifying the culprit is no small task without the proper transparency into your multi-cloud and hybrid cloud environments.
A reliable data observability solution provides end-to-end visibility, making it possible to trace the flow of data (and the cost of data) across your interconnected systems. The Acceldata platform makes this possible by delivering:
Multidimensional data observability offers a scalable approach to monitor, detect, predict, prevent, and resolve issues across your data, processing, and pipelines. Use this handy data observability checklist to ensure you’re making the right decisions about data observability.
Multidimensional data observability enables organizations to monitor, detect, predict, prevent, and resolve issues across their data, processing, and pipelines. Acceldata makes this possible by providing:
Ready to start your data observability journey? Get a demo of the Acceldata platform to see if it’s right for your organization.