Sometimes the best defense is a good offense. That’s never truer than when it comes to data quality and reliability.
The classic approach to data quality monitoring arose in the data warehouse and business intelligence (BI) era. It went something like this: An alert pops up or, worse yet, an end user calls with a problem. The data engineer springs into action and starts to investigate to see whether the issue can be traced back to poor data quality or some other cause.
This reactive, defensive approach worked fine when end users “didn’t mind”, there was time to re-run the process, and you were sure all issues would be caught. Unfortunately, that’s rarely the case today, and with each incident end-user trust in data and the data team is eroded. Companies need tools that help them go on the offense and take a proactive approach to ensure data reliability by predicting and preventing issues before they occur.
Today, data-driven organizations are leveraging data and analytics throughout operations in near real-time, with automated processes and embedded into applications.
You simply can’t interrupt the business while you troubleshoot issues. Moreover, slow data can be almost as bad as failed processes, since it forces employees, customers, partners, suppliers and others to either wait for information or give up and rely on guesswork.
Every organization typically has some basic checks that they implement to catch obvious and/or critical failures in data quality. These “usual suspects” often include missing data or wrong format. However, today's data can often be more unpredictable. Examples include:
These are just a few examples of why only monitoring for the usual suspects doesn’t cut it and why traditional data quality tools and approaches don't ensure data reliability.
Traditional approaches to data quality often require technical expertise, domain knowledge, manual work, and a lot of time. With so many things that can go wrong and so many data assets, it’s no wonder organizations have huge gaps in data quality coverage, particularly in large, complex data environments.
Organizations often resort to:
As more data and analytics use cases are implemented, data management gets more complicated and a lot more expensive. The only viable option is for organizations to leverage automation to improve productivity and scale data governance.
The Acceldata Data Observability platform delivers data reliability and operational intelligence solution brings a Data Observability approach that improves the reliability, productivity, and cost of data management.
The Acceldata platform provides: