Data reliability ensures that business teams can confidently use the data to operate effectively and to operate very quickly.
With analytics playing an ever-more important role in businesses today, there is increased attention to data reliability. RTInsights recently sat down with John Morrell, senior director of product marketing at Acceldata, to discuss what data reliability encompasses, why it is so important, and how data observability can help.
Here is a summary of our conversation.
RTInsights: What is the importance of data reliability for modern data environments?
Morrell: In today’s business world, analytics plays such a big part in a business operation now that analytics is business critical. They’re essential to the business. Many new modern business strategies require very accurate, high-quality, and timely data to support decision-making and action-taking. This makes the operation and management of data, as well as the data pipelines that feed these analytics, mission critical.
Data reliability provides the monitoring and management of the data assets and pipelines to ensure that data is delivered in a timely manner and has the highest degree of quality. It ensures that the business teams can confidently use the data to operate effectively and to operate very quickly.
RTInsights: What challenges do organizations face when trying to ensure data reliability throughout their data pipelines?
Morrell: The biggest challenge for organizations is recognizing data blind spots and putting in place the processes to not just monitor the problems within data blind spots but also eliminate them. You need to give them the tools to be able to make sure that problems just don’t occur within any blind spots. These blind spots can include things like the throughput and latency of the data pipelines or data reconciliation to make sure that the data is consistent in different places. Data quality is another key blind spot that can impact the health and overall execution of your data pipelines. And there are additional blind spots, such as schema and data drift.
Without visibility into these blind spots, it’s like trying to drive a race car in a blinding snowstorm. You have this great modern data stack that’s fully powered to do a lot of great things for you, but you don’t have the proper visibility.
RTInsights: How does data observability help with data reliability?
Morrell: Data observability is the solution to help an organization achieve better data reliability and more. At Acceldata, we see data reliability as one aspect of data observability. Specifically, data reliability is one good outcome that good data observability tools will deliver.
For data reliability, a good data observability platform gives you complete visibility into both your data assets and your data pipelines. It also allows teams to apply augmented, as well as custom data quality and reliability policy, at scale to make sure you get great quality of your data.
See also: Using Data Observability to Control Costs and Increase Data Reliability in Cloud Data Platforms
It can identify a variety of different problems, not just individual simple data quality things. That’s things like data drift or schema drift that a lot of the data quality tools don’t see. It can provide alerts about data problems as they occur. When a problem occurs, it offers deep insights that allow the data teams to quickly drill into a problem, troubleshoot it, and resolve the problem. And in other cases, it can actually automate responses to certain problems. So, for things like data reconciliation, you can actually automate a response to make sure that data is consistent throughout the process.
RTInsights: What are the benefits of automating data reliability using data observability?
Morrell: First and foremost, it ensures that the data is delivered on time and has the highest degree of data quality. In that way, when people make decisions based on this data, it is a timely decision, and it’s a very accurate decision. The second benefit is that when your data assets are reliable and of high quality, it increases the trust that the business teams have in the data. And when the business teams trust the data, they will then make many more data-driven decisions instead of gut-driven ones.
The other benefit is giving complete visibility into the state of your data assets and your data pipeline execution to the data teams. With that visibility, the data teams, data owners, heads of data, CDOs, and more can actually make sure that the SLAs or service level agreements to the business teams are met.
Those SLAs can include timeliness, quality, and a variety of other things. It ensures that the contract between the CDO and the business owners that are in place are constantly met. Another key benefit is it eliminates errors in downstream analytics so that the data teams can rapidly resolve issues when they occur. And finally, there’s visibility and coverage across the data assets and data pipelines. There’s more information to facilitate stronger data governance and compliance and, therefore, to help reduce risk.
RTInsights: How does Acceldata help?
Morrell: Acceldata is a data observability platform that covers a wide variety of functionality. It lets you observe your data assets, data pipelines, and the performance of what’s going on. It also helps you optimize the costs of your cloud data platforms. We have exceptionally strong data policy tools and data quality tools that let teams automate a lot of the policies that they need to maintain high degrees of data quality. And we’re extremely flexible for all forms of data. We’re also extremely scalable.
See also: Multi-dimensional data observability
When you get into some of these larger cloud data platforms, the enterprises that are using these platforms have a lot of data moving through the system. You’re constantly analyzing the data for data quality problems. At these high volumes, you must analyze the data for quality problems very quickly without disrupting business use of the data. And so, we offer great scalability across high-volume data in large, diverse architectures.
We also offer deep, multilayered data for observability. We can correlate data from different places up and down the data stack. When a problem occurs, you can stop and say, “I found the problem here, but the root cause of it was down here in the compute layer.” You can analyze these problems and resolve them very, very quickly.
The other key thing that Acceldata does is help shift left data reliability. We isolate data problems at the source before it creates a problem in the data warehouse and the downstream applications so that you’re not proliferating bad data. We cover data at rest, data in motion, and data to be consumed. So, we’re covering data through its entire life cycle of a data pipeline and in your modern data stack.
And finally, the product was specifically designed for the modern data stack. We run on any cloud and work with all the modern data sources.
