Jan Bosch
Imagine the following scenario. A (sizable) team at a large company writes customer documents in response to customer requests. They request help from the automation team to reduce their repetitive tasks. The automation team brings in an AI company, which develops an ML model that generates the customer documents automatically and virtually eliminates the need … Read more
Digitalization is fundamentally enabled by three core technologies: software, data and artificial intelligence. The common denominator, which is inherent in a digitalized business, is that automation is at the heart of it. Digital technologies allow for automation to a much more significant extent than traditional technologies. We see this reflected in companies: whereas in traditional … Read more
DevOps, DataOps, MLOps – the number of different “Ops” combinations seems to have exploded over the last year or so. There are manifestos, meetups, lots of blog posts and research articles about these various approaches. In order to get clear on terminology, I think it’s good to define what we’re talking about. So, first, DevOps … Read more
Few technologies create a level of hype, excitement and fear these days as artificial intelligence (AI). The uninitiated believe that general AI is around the corner and worry that Skynet will take over soon. Even among those that understand the technology, there’s amazement and excitement about the things we’re able to do now and lots … Read more
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 more
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 more
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 more
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 more
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 more
If you’ve been reading my posts, you know that I feel data is one of the key ingredients of a successful digital transformation. It’s not just about adding software to your products or putting DevOps in place. It is as much about collecting, analyzing and storing data and using this data to improve a variety … Read more
