Let’s be real – client reporting often feels like the marketing world’s equivalent of doing taxes—tedious, time-consuming, and to be honest, not the reason you got into this field. Adding to the frustration is the fact that, more often than not, many clients don’t even understand their reports. That’s why marketers often talk about how reporting feels like a necessary evil. But what if we looked at reporting as a tool for client retention?

‍ They started in awe, which soon turned into desperation to keep up, and it is only now that we have started realizing the utility and business value of our Artificial Intelligence (AI) goals. I like this stage of our industrial revolution, where we are no longer expecting magic from AI but are integrating it nevertheless for all the wonders it can still do for our businesses. This was the same space where our DevOps efforts started yielding enterprise-level transformations.

Intelligent automation is transforming the manufacturing industry, optimizing operations, reducing costs, and enabling data-driven decision-making. However, to fully harness the potential of intelligent automation, manufacturers must measure its impact using specific Key Performance Indicators (KPIs). These metrics provide a comprehensive view of performance, help identify areas for improvement and demonstrate the value of automation to stakeholders.

UI testing helps to ensure that the UI of your website or mobile app behaves as expected and that a high-quality user experience is maintained. This guide covers the basics of automated UI testing and how it can help your organization perform full UI testing more efficiently and consistently.

Business leaders find themselves involved in a range of high-priority tasks, most of which require making critical decisions. Let’s say you’re the sales head of a global organization. You’re ready to make an important decision about next quarter’s sales strategy, but you must first look at the right data set. You know it exists somewhere in your organization’s databases, yet it’s not within the arm’s reach.

Organizations must constantly seek ways to improve their operations and drive efficiency. Two concepts gaining traction in this quest are process intelligence and process mining. While these terms may sound similar, they serve distinct purposes and can provide different insights into your processes. Understanding the differences between them is crucial for organizations aiming to enhance productivity and streamline workflows.

In the insurance industry, the claims process can often feel like a complex maze of manual processes for both customers and agents. However, new and advanced technologies are paving the way for smoother, more efficient claims handling. Claims processing automation at insurance companies can streamline routine tasks, reduce risk of errors, and enhance customer satisfaction. In this blog, we’ll explore three key areas to focus on for effective automation.

As technology evolves, the skills gap within IT departments continues to widen. New technologies are being adopted more quickly to keep pace with business changes, necessitating a constant update of skills. Yet, fully outsourcing these skills isn’t always the solution, as they are a core part of any IT organization. To effectively address this issue, we need a strategy that focuses on continually closing the skills gap.

The stage is set for a new era in marketing, and marketers have never had so much data and technology at their fingertips. But to deliver the ROI that enterprises require today, marketers must have a strategic mindset and fine-tune the tools, tactics and approaches in their marketing data stack. Snowflake is here to help marketers evolve and accelerate their marketing impact with our third annual Modern Marketing Data Stack report and global virtual event.

Are you thinking about hand-coding an API? You have the perfect application in mind. It has an attractive UI and achieves everything the customer wants. However, it needs to connect with a Microsoft SQL Server database, so you have to start working on the data model and API. Hand coding APIs offers greater customization and control but is time-intensive, whereas API generation provides a faster, more streamlined approach with automated tools, albeit with less flexibility.