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For organizations looking to automate processes for better performance, a combination of RPA and process mining can help. Let’s take a step back to understand why. Automation that’s implemented without data-backed decisions often yields poor results. And data without action leads nowhere. To get the most out of any technology investment, you have to be sure you're using the right combination of tools and techniques.

This year at Appian World, we announced the launch of Process HQ. This next-generation process mining capability combines both process and business data in a single place, automating process analysis and empowering users with intelligent recommendations for where and how to take action. And because it’s part of the Appian Platform, you can immediately apply these improvements to your processes using our AI automation and workflow orchestration capabilities.

Airports are now heavily inclined towards integrating self-baggage drop facilities to ensure a hassle-free experience and eliminate long-standing queues. Plus, some airports are also introducing smart baggage trolleys to help passengers locate wherever they want to go at just their fingertips. Well, these are just a few of the innovations in the Aviation industry because the list is never-ending. Nevertheless, did you know that the airports using this new technology have seen some amazing results?

Organizations are challenged today to become both more data driven and more nimble to adapt quickly to changing conditions. These challenges are the driving forces behind much of their digital transformation or “modernization” efforts.

Background jobs are essential to many Ruby on Rails apps. Since the introduction of ActiveJob, Rails developers have been able to manage their background jobs as natively as they do their database records. Still, ActiveJob requires you to select (and support) a backend adapter that will implement ActiveJob's backend. Many use Redis, a memory cache, to queue and process background jobs. Redis comes with incredible speed but is yet another dependency to maintain.

Managed disaster recovery in BigQuery offers automated failover of compute and storage to a different region and a new cross-regional SLA.

We've just rolled out new trigger controls and enhanced YAML syntax so you can initiate your builds with unmatched precision.

The success of data warehouse solutions depends on how well organizations implement test cases to guarantee data integrity. As organizations evolve, data warehouse testing becomes crucial to adhere to industry best practices.

When you’re ready to make the transition from manual testing to test automation, it’s natural to consider hiring. A QA engineer — who has the technical skills to write and maintain automated tests in an open-source framework — can take the burden of end-to-end test management off of your development team, allowing them to ship more code, faster. But hiring a good QA engineer is — often prohibitively — expensive.

"The whole is more than the sum of its parts." Aristotle is credited with this quote, and it's true in the world of data. Legacy systems typically approached their role in a limited manner. Each system was intended to be used by a certain user set and handle well-defined processes and associated data. The result was a disintegrated environment with data being difficult to obtain, and frequently out of date. The parts couldn't easily cooperate to make a whole.