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
Virtually all organizations I work with have terabytes or even petabytes of data stored in different databases and file systems. However, there’s a very interesting pattern I’ve started to recognize during recent months. On the one hand, the data that gets generated is almost always intended for human interpretation. Consequently, there are lots of alphanumeric … Read moreWhy your data is useless
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?
The effects of digitalization and other technological shifts cause companies to realize they need to change. This often leads to significant discussions in the organization as there typically are several alternatives being considered by different people. These might include topics such as business models, product implications, partnerships with suppliers and technology providers. Agreeing on the … Read moreWhen you don’t know, run experiments
Before digitalization became a thing, the industry was divided into two types of businesses: those that sell products and those that provide services. Those that sell products have a transactional relationship with their customers, mostly consisting of selling one of their products. The service businesses tend to have a more continuous relationship with their customers … Read moreWhy you’re a product + service business
Last week, I wrote about the different types of use for data that we have available. That led to discussions with various people and I realized that there’s a problem around data that’s very typical in companies that have their roots in embedded systems or mechanical engineering: it’s actually unclear who owns the data from … Read moreGet your data out of the gray zone