Those who have used the Trifacta platform wouldn’t just tell you that it works—they’d also tell you that we’ve actually made data preparation fun. What was once considered a tedious, often dreaded activity has now become quite addictive with the Trifacta platform. Still, getting started with a new technology isn’t easy. Here are a few tips to get you feeling confident and comfortable with the Trifacta data preparation platform.
Read MoreIf you have an active Scrum practice, make a plan to implement one of these tips. Change doesn’t have to be daunting, nor does it have to be forced in all at once. It never hurts to be agile about Agile. In their book Fixing Your Scrum, Ripley and Miller offer an approach to effectively identifying and applying changes within an organization, by asking the simple question: “What’s within my control that I can change to get our organization 15% closer to where it needs to be?”
Read MoreFew ideologies or concepts have been as transformative in the realm of project delivery over the past couple of decades as Scrum. Now celebrating 25 years in existence and deeply embedded at tech startups and tech giants alike, Scrum has practically become a household name ( – though not quite, as evidenced by recurring questions from my wife: “What exactly are you a master of?” and “What’s a… scrum?”).
Read MoreThe benefits of cloud computing are widely accepted these days, but we’re still in the early stages of organizations actually moving systems to the cloud. While all sorts of workloads can benefit from cloud computing, your data and analytics systems are a great place to start—or to look at next if you’re already on the cloud journey.
Read MoreThe recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies at the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.
Read MoreThe recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies at the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.
Read MoreData requirements are the technical specifications of what data you will need for your project, who is responsible for that data, where and how it is stored and used, and what measures you’ve taken to protect and optimize it for usage.
Read MoreThe recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies in the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.
Read MoreThe recurring perception that artificial intelligence, AI, is somehow magical and can create something from nothing leads many projects astray. That’s part of the reason that the 2019 Price Waterhouse CEO Survey shows fewer than half of US companies are embarking on strategic AI initiatives – the risk of failure is substantial. In this series, we’re examining the most common ways AI projects will fail for companies in the beginning of your AI journey. Be on the lookout for these failures – and ways to remediate or prevent them – in your own AI initiatives.
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