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How to Re-engineer the Incubation Zone

Innovation Excellence

GUEST POST from Geoffrey A. Moore Having spent the last several years working with public companies in the tech sector who want to apply zone management principles to catching their next wave, I finally had an epiphany. Every one of my clients had an Incubation Zone of one sort or another, and all of them […]

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Engineering the Future from Astronomy to Augmented Reality

Tullio Siragusa

Engineering the Future from Astronomy to Augmented Reality. Tech Backstage interviewed Jason Lozo to learn more about his journey from being an astronomer to an optomechanical engineer at Microsoft HoloLens. in Mechanical Engineering, then accepting a Research position at the University. Optomechanical Engineering.

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Exploring High-Growth Opportunities in Software Engineering

Tullio Siragusa

Exploring High-Growth Opportunities in Software Engineering The software industry is a mature and vast market with a high demand for software engineers. Let’s explore high-growth opportunities in software engineering, from AI, Cloud Computing to Internet of Things (IoT), and Cybersecurity.

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Turning Reverse Engineering of Business Goals into Strategies

Tullio Siragusa

Turning Reverse Engineering of Business Goals into Strategies. A lot of learning can come from reverse-engineering many things. In business it can be a creative process on how to bring about new change, and innovation. Tech Backstage interviewed Jason Jepson to learn more about reverse engineering. Reverse-Engineering.

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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.

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A Day in the Life of Analytics Engineer, Kaley Coddens

Planview

Today in our Day in the Life series, we’re spotlighting one of our incredible Analytics Engineers, Kaley Coddens. Based out of Colorado, Kaley had a lot to share about her team, how she finds success, and what she loves about working at Planview. It’s a mix between a data analyst and a software engineer, but not fully one or the other.

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A Force for Good: Meet Value Engineering & RFXIS Director Paula Lina

Planview

Paula is Planview’s Director of Value Engineering & RFXIS and leads the Force for Good initiative. What is Director of Value Engineering & RFXIS? In Value Engineering, I help customers and prospects see the value Planview can bring to their businesses. Let’s get to know Paula Lina. My team has two components.

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The 5 Stages of Account-Based Marketing — and How to Win Them All

Data is the fuel that powers your ABM engine. In this eBook, we’ll walk you through how to leverage strong data and go-to-market tools to unlock the five stages of ABM: define, identify, engage, convert, and connect. Without it, you can’t find and reach your target accounts. ZoomInfo’s MarketingOS changes all that.

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Turning Metrics into Dollars: How to Turn Your Analytics Data into a Real Financial Model for your Startup

Speaker: Tristan Kromer, Lean Agile Coach, Kromatic

increase isn't worth the engineering time we're spending! Early stage startups in particular may not need a four year business plan, but they need to start building out a model which will show how they can someday be profitable. You'll learn: How to turn basic dashboard metrics into a financial model. Sometimes that 0.1%

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Achieve Manufacturing Digital Transformation

Smart manufacturing represents roughly 27% of the average annual digital transformation budget for engineering-oriented manufacturers. Read this report to learn how to merge digital information with your physical environment and reap the rewards of smart manufacturing.

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Best Practices for Creating Long-Lasting and Continuous Discovery Habits

Speaker: Teresa Torres, Internationally Acclaimed Author, Speaker, and Coach at ProductTalk.org

As a result, many of us are still stuck in a project-world rut: research, usability testing, engineering, and a/b testing, ad nauseam. These methods are better than nothing, but how can we improve on this model? How to define a clear benchmark for what a strong continuous discovery team does.