Across the EU and here in Sweden authorities and companies are working hard to come to terms with a changed geopolitical environment and concerns about data and digital regulation. This is often unfairly framed as a trade off between fast, US-dependent, innovation and slow-moving European regulations. Trice has done several advisory and exploratory projects in this space together with authorities, healthcare companies, and players in the defense sector. In this article, we want to share some conclusions in line with our continued focus on hands-on, value-oriented tech strategy. 

“We delivered the dashboard on time”
“The pipeline ran successfully”
“Usage is up 37%”

The dashboard was only opened once. The pipeline was deprecated. Increased usage meant higher computing costs but no new customers.

These are examples of signals and metrics that feel productive, but often drift into the category of vanity metrics — numbers that look good but might not mean anything unless tied to meaningful outcomes.

So how does a product manager distinguish value from vanity?

Internal developer platforms (IDPs) are becoming the backbone of modern software delivery. According to the latest Accelerate State of DevOps report, 89% of respondents are using IDPs to streamline engineering processes and improve developer productivity. But while the benefits are clear, the path to a successful platform is riddled with pitfalls. 

In this post, we’ll break down the most common traps platform teams fall into, and how to avoid them.

After the initial hype around generative AI, we're now seeing a wave of reports highlighting high failure rates of AI projects. This isn’t surprising — with any new technology, early enthusiasm inevitably gives way to the reality of steep learning curves and missteps.

Recent headlines makes us think that it is time to repeat some fundamentals around innovation, experimentation and failure.

In all the AI hype one thing stands out in terms of what you need to succeed with AI, machine learning and data driven projects. That is having a thought through, strategic approach to storing, structuring and protecting your data. 

In this second post on becoming data-driven, we’ll dive into three key elements of a modern data strategy; the data operating model, how your strategy can ensure you stay secure and compliant and, lastly, how to pick a technology stack and architecture to implement a data platform that will empower your organization and make you reach your business goals.

To build on top of the AI and machine learning paradigm, you need to be in control of your own data. Succeeding with your data strategy transformation, becoming truly data-driven, will unlock new business and technical opportunities and allow your organization to end up on top of the next big shift.

In this blog post we’ll share what the most successful data transformations we’ve been involved in has in common and where the challenges reside.

Cloud computing is a key technology for us at Trice. About two decades old it continues to grow in importance for product and service delivery. At the same time a lot of organizations struggle with their cloud journey and, to be honest, there are still a lot of things we miss in terms of simplicity, developer experience, and guidance when working with cloud today.