In 2023, AI is everywhere, and not a day goes by without someone developing a new way to use tools like ChatGPT. With the increasing hype about the promise of AI, it’s more important than ever to cut through the noise and focus on how AI can help you deliver real business value. Modern process automation platforms go above and beyond the capabilities of AI alone.
Your teams need constant connectivity to your organization's data and systems to operate effectively and thrive. And with digital transformation spurring a more rapid pace of innovation and technology adoption, this need for connected, democratized data is on the rise. That makes data silos an enemy you can no longer tolerate. Data fabric is the modern answer for eliminating data silos. Data silos occur when data is stored in separate systems or departments without proper integration.
In my last article, I outlined how we in Snowflake Support use contextual data about where our customers get stuck to improve the overall product experience. Now I’ll take you through how your organization can also implement these important feedback loops from support to product enhancements, to your company’s—and your user’s—benefit. Customers don’t wake up in the morning and decide they’d love to spend time with a Support team.
Last week’s announcement of a data fabric approach from Microsoft was interesting to us on a number of levels, most notably for what it confirmed: Data Fabrics are now mainstream. If you’ve not heard why Data Fabrics are the next big thing, then here is a bit of history. Data Fabric is a concept that started to take hold over a decade ago with published research from Forrester.
How to chart a roadmap to the pinnacle of data science.
Since leaving university, I have always been involved with data, learning so much during my time as an automation engineer and big data engineer working for an automotive tier 1 manufacturing company and as a data architect for an IT consultancy in the SAP and BI domain. And today, I count myself very fortunate to have a job at Kensu that I am truly passionate about. As a Technical Solution Architect, I get to help organizations with data health and data engineering problems on a daily basis!
Engineered with a focus on testing efficiency, our product team has introduced some really cool features this month. Let’s dive right in! With these new feature updates, we’re continuing to make test automation a breeze.
Manual tests slow down your team and limit test coverage, but implementing UI test automation can sometimes seem daunting. After all, you have a massive application with numerous moving parts, and UI tests are notoriously flaky. So, how can you improve quality and streamline your UI testing efforts? In this article, you’ll learn the difference between test automation and test management, and how integrating the two can unlock a more comprehensive approach to quality.
