Don’t build new platforms

During the last months, I’ve met with several companies that had an interesting common denominator: they were all building a new platform to replace a legacy platform and things weren’t going so well. The legacy platform often is decades old and has functionality in it that’s the result of hundreds of person-years of effort. And … Read moreDon’t build new platforms

Finding your AI business case

Having worked with companies on the use of AI, I’ve noticed an interesting pattern: although most of the attention is spent on algorithms, data storage infrastructure, training and evaluation of applications, the hardest part very often seems to be coming up with a promising concept in the first place. When exploring promising concepts, many start … Read moreFinding your AI business case

Why your strategy fails

During the last weeks, I’ve experienced multiple situations where an organization (industrial or academic) simply doesn’t have a business strategy or a strategy concerning a key area for their business. When probed and questioned on the strategy, I’ve observed at least three types of responses. First, leaders in the company say that there is a … Read moreWhy your strategy fails

How to generate data for machine learning

In recent columns, I’ve been sharing my view on the quality of the data that many companies have in their data warehouses, lakes or swamps. In my experience, most of the data that companies have stored so carefully is useless and will never generate any value for the company. The data that actually is potentially … Read moreHow to generate data for machine learning

AI is NOT big data analytics

During the big data era, one of the key tenets of successfully realizing your big data strategy was to create a central data warehouse or data lake where all data was stored. The data analysts could then run their analyses to their hearts’ content and find relevant correlations, outliers, predictive patterns and the like. In … Read moreAI is NOT big data analytics

The game plan for 2020

In reinforcement learning (a field within AI), algorithms need to learn about an unexplored space. These algorithms need to balance exploration (learning about new options and possibilities) with exploitation (using the acquired knowledge to generate a good outcome). The general rule of thumb is that the less is known about the problem domain, the more … Read moreThe game plan for 2020

So, you’re an expert?

This week, I gave a talk at a company that’s starting with data-driven practices and A/B experimentation specifically. My talk was concerned with the enablers required for this, such as continuous deployment (or DevOps), the specific ways in which organizations can apply data-driven practices and A/B testing and the importance of value modeling so that … Read moreSo, you’re an expert?