Jan Bosch
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
One of the sayings that almost everyone in business has taken to heart is “data is the new oil” by Clive Humby. There are constant discussions about data ownership between end customers and product providers, as well as between OEMs and their suppliers. The first start-ups are now trying to advise companies on how to … Read more
With data- and AI-driven development taking over the world, it may easily seem that the cloud is the place where everything happens. This is where the data is stored and analyzed, where the machine- and deep-learning models run and where all the value resides. The perspective of people living in this world is that all … Read more
One of the things that keep surprising me over and over again is how much effort companies spend on processing, cleaning, converting and preparing data. For the companies that I work with, the data science teams easily spend 90-95 percent of their time just preparing data for use in machine learning/deep learning deployments. This is … Read more
Virtually any company that I work with is exploring its data sets and business processes to identify opportunities for productivity improvements, higher accuracy or lower cost. The constant question that these companies struggle with is how can artificial intelligence, and specifically machine learning (ML) and deep learning (DL), support existing processes and ways of working. … Read more
In earlier posts (such as here), I have discussed different approaches to software development and one of the key models for this is the HoliDev model where we combine requirements driven development, outcome-driven development (e.g. A/B testing) and AI-driven development. In the figure below, the HoliDev model is shown graphically. It may easily seem that … Read more
As I’m spending an increasing amount of time in the AI field with a variety of companies, I’ve noticed an interesting misconception in the ML/DL space. Many have a tendency to focus on data sets, experimenting with different models using a specific set of data and, finally, deploying a model in a specific context. This … Read more
