As analytics has evolved from a supporting role to a business-critical one for all organizations, the processes for delivering the necessary data for analytics have become top priorities. Data must now be treated as mission-critical, and for it to have optimal impact, data must have the highest degree of data reliability.
Analytics have evolved from traditional data warehouse approaches to modern, cloud-based analytics. As a result, the types of data captured and used, and the data stack that delivers the data have all had to evolve as well. Modern analytics deal with different forms of data: data-at-rest, data-in-motion, and data-for-consumption. As the data stack moves and transforms data in near real-time, it requires data reliability that keeps pace with the continuously changing range and scope of data.
In this article, we'll explore what data reliability is and what it means for modern analytics. We'll look at why a fresh new approach to data reliability is required to keep enterprise data and analytics processes agile and operational.
Historically, data processes that were built to deliver data for analytics were batch-oriented and focused on highly structured data. Data teams had very limited visibility into the data processes and processing and focused their data quality eﬀorts on the data output from the processes: data-for-consumption.
Legacy data quality processes were limited in scope and functionality; they were equipped to only do a few things:
Legacy data quality tools and processes had limitations of data processing and warehousing platforms of the time. Performance limitations constrained how often data quality checks could be performed and limited the number of checks that could run on each dataset.
Modern analytics and modern data stacks are more complex, and as a result, the potential issues with data and data processes have grown:
Data reliability is a major step forward from traditional data quality. Data reliability includes data quality management but covers much more functionality that data teams need to support for modern, near-real-time data processes.
Data reliability takes into account the new characteristics of modern analytics. It provides:
Many data observability platforms with data reliability capabilities claim to offer much of the functionality of modern data reliability mentioned above. When looking for the best possible data reliability platform, there are some critical elements that need to be considered, as we outline below.
Traditional data quality processes were applied at the end of data pipelines on the data-for-consumption. One key aspect of data reliability is that it performs data checks at all stages of a data pipeline across any form of data: data-at-rest, data-in-motion, and data-for-consumption.
End-to-end monitoring of data through your pipelines allows you to adopt a "shift-left" approach to data reliability. Shift-left monitoring lets you detect and isolate issues early in the data pipeline before it hits the data warehouse or lakehouse. This prevents bad data from hitting the downstream data-for-consumption zone and does not corrupt the analytics results. Early detection also allows teams to be alerted to data incidents and remediate problems quickly and efficiently.
Here are five additional key characteristics that a data reliability platform should support to help your team deliver the highest degrees of data reliability:
Data reliability is a process by which data and data pipelines are monitored, problems are troubleshot, and incidents are resolved. A high degree of data reliability is the desired outcome of this process.
Data reliability is a data operations (DataOps) process for maintaining the reliability of your data. Just like network operations teams would use a Network Operations Center to gain visibility up and down their network, data teams can use a data reliability operations center in a data observability platform to get visibility up and down their data stack.
With data reliability, organizations derive important advantages, including benign able to:
The Acceldata Data Observability Cloud provides data teams with end-to-end visibility into your business-critical data assets and pipelines to help you obtain the highest degrees of data reliability. All your data assets and pipelines are continuously monitored as the data flows from source to final destination and checks are performed at every intermediate stop along the way for quality and reliability.
Acceldata helps data teams better align their data strategy and data pipelines to business needs. Data teams can investigate how a data issue impacts business objectives, isolate errors impacting business functions, prioritize work, and resolve inefficiencies based on business urgency and impact.
The Data Observability Cloud supports the end-to-end, shift-left approach to data reliability by monitoring data assets across the entire pipeline and isolating problems early in the pipeline before poor-quality data hits the consumption zone. It works with data-at-rest, data-in-motion, and data-for-consumption to work across your entire pipeline.
Data teams can dramatically increase their efficiency and productivity with the Data Observability Cloud. It does this via a deep set of ML- and AI-guided automation and recommendations, easy-to-use no- and low-code tools, templatized policies and bulk policy management, and advanced data policies such as data cadence, data-drift, schema-drift, and data reconciliation.
With the Data Observability Cloud, you can create a complete data operational control center that treats your data like the mission-critical asset that it is and helps your team deliver data to the business with the highest level of data reliability.
To learn more about data reliability within the Data Observability Cloud, please visit our data reliability solutions page and review some of your data reliability assets.