I recently had the pleasure of hosting a webinar with the team over at Rudderstack. It focused on Data Engineering and managing dynamic schemas at scale. More specifically, Rudderstack-generated, dynamic schemas at scale. This blog will discuss the tools, software, and methods to do just that.
Imagine a typical day at work that starts with checking Google Calendar to prepare for where you need to be and when. With engaging discussions, your teams create Google Docs to take notes on the important decisions coming out of the meetings and begin writing briefs to kick off your company’s next big project. Then you turn to Sheets to track the KPIs and leverage Slides to prepare the final presentation for your executive leadership by 4pm that day.
As we’ve previously written, old-fashioned employee time tracking methods such as paper timecards or Excel come with several downsides. First, the payroll process is time consuming and error prone, as you have to manually tally hours and overtime. Second, you don’t know if your employees are in fact on the job when they’re punching in, rather than a coworker (and those extra minutes or hours that employees report working can lead to a significant waste of money).
In the daily life of a Site Reliability Engineer, the main goal is to reduce all the work we call toil. But what is toil? Toil is the kind of work tied to running a production service that tends to be manual, repetitive, automatable, tactical, devoid of enduring value, and scales linearly as a service grows. This blog post describes our journey to automate our nodes rotation process when we have a new AMI release and the open source tools we built on this.
These are painful customer experiences we can all relate to and top indicators of poor customer service according to Zendesk’s 2021 Customer Experience Trends Report. With 75% of people willing to spend more money on a brand that provides a great customer experience, companies now know that customer loyalty depends on delivering exceptional customer service. However, brands often go about trying to improve customer experience in the wrong way, despite their best intentions.
AI is transforming customer service, making it more convenient and personal by automating high-quality resolutions on consumers’ terms and empowering human agents to work smarter. The use cases for automated customer service seem endless, so we’ve pulled together the best opportunities for leveraging the power of artificial intelligence in customer service that we see today. How are our customers using automated support to rethink retail customer service?
Data analysis is critical for all employees, no matter what department or role you work in. Whether you’re a marketer analyzing the return on investment of your latest campaign or a product manager reviewing usage data, the ability to identify and explore trends and fluctuations in your data is an essential skill for decision-making. Unfortunately, many companies today struggle with data organization and analysis.
