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.

Accelerate Healthcare and Life Sciences is a one-day virtual event, featuring technology and business leaders from Elevance Health, Ginkgo Bioworks, Datavant and more, to discover executive priorities, best practices and potential data and AI challenges that are top of mind for 2024.

IT security professionals need to recognize the critical importance of securing data pipelines in a data-driven corporate environment.

Despite the transformative potential of AI, a large number of finance teams are hesitating, waiting for this emerging technology to mature before investing. According to a recent Gartner report, a staggering 61% of finance organizations haven’t yet adopted AI. Finance has always been considered risk averse, so it is perhaps unsurprising to see that AI adoption in finance significantly lags other departments.

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.

Enterprise Resource Planning (ERP) systems are designed to streamline business operations by centralizing data across an organization. However, extracting and utilizing this data can be a complex and time-consuming process. One solution to this problem is the use of APIs (Application Programming Interfaces), which allow you to get data out of ERP systems and other software applications.

Geopolitical Instability Raises Threat of ‘Catastrophic Cyberattack in Next Two Years’- Global Cybersecurity Outlook 2023 – World Economic Forum As the world’s attention remains fixated on the unfolding events in Taiwan, Ukraine & Iran, it’s crucial to recognize that the theater of battle extends far beyond physical borders into the shadowy area of cybersecurity.

Data filtering plays an instrumental role in reducing computational time and enhancing the accuracy of AI models. Given the increasing need for organizations to manage large volumes of data, leveraging data filtering has become indispensable.

"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.

There are several ways to handle load spikes on a service. However, these methods are not cost-effective: you either pay for resources you don't use, or you risk not having enough resources to handle the load. Fortunately, there is a third way: horizontal autoscaling. Horizontal autoscaling is the process of dynamically adjusting the number of instances of a service based on the current load. This way, you only pay for the resources you use, and you can handle load spikes without any manual intervention.