This week, for the umpteenth time, I met a team in the process of putting a new product in the market, telling me that they were so customer centric. What they meant was that during development, they’d talked to a number of potential customers and some of the employees had used prototypes. For those that … Read moreSo, you’re customer centric?
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
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
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
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
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
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
Peter Drucker famously said that the purpose of a business is to create a customer and a customer is defined as someone who pays for the products and services the company offers. This perspective seems to be shared by many in business: as long as revenue and profits are generated, there’s no reason to bother … Read moreWhy care about purpose in business?
Over the last weeks, I’ve been to three different conferences where I heard presentations that were variations on a common theme: if we would just add more structure and more process to the topic at hand, if we would only introduce more steps, more checkpoints, involve more people, and so on, then all the problems … Read moreMore process doesn’t help
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?