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.