Jan Bosch
Over the last weeks, I have been on the road a lot for various engagements with customers as well as for our research activities. In virtually every meeting, the whole area of artificial intelligence and especially machine and deep learning come up as a discussion topic. This is great as I think the whole AI/ML/DL … Read more
In the latest two articles, I outlined the two phases of adopting machine & deep learning (ML/DL). In the figure below, we show the steps that companies typically evolve through while adopting AI, ML and DL solutions. As shown in the figure, the third step, the subject of this article, is where ML/DL components are … Read more
In last week’s article, I outlined the first phase of adopting machine & deep learning (ML/DL) which is concerned with experimentation and prototyping. In the figure below, we show the steps that companies typically evolve through while adopting AI,ML and DL solutions. As shown in the figure, the next step is the careful use of … Read more
This week I got the opportunity to speak at the initiative seminar organized by the Chalmers AI Research center (CHAIR). The key message in my presentation was that working with artificial intelligence (AI) and specifically machine & deep learning (ML/DL) constitutes a major software engineering challenge that is severely underestimated by companies that start to … Read more
As I have been writing about adopting data-driven decision making in the broad sense and data-driven development more narrowly over the last weeks, several people have reached out to me, discussed with me and more generally explored the subject with me. During these engagements, I realized that many products and services exist for the sole … Read more
As companies adopt data-driven development, there is an interesting pattern that is concerned with selecting the factors that we’re optimizing for. The goal, in the end, is to influence business level key performance indicators (KPIs) such as revenue, margins, net promoter score, etc. However, these KPIs tend to change very slowly and are lagging indicators. … Read more
We live in a world that is driven by a Need for Speed and when leaders mention agility, continuous integration and continuous deployment, often speed is mentioned as the key driver. Having worked with dozens of companies, my learning is that companies go through a number of evolution steps in order to work with speed. … Read more
In the posts from the last weeks, I discussed the first two steps in adopting data-driven development (see figure below), i.e. modeling feature value and building the necessary infrastructure. Once we have described the value that we expect from a feature and have constructed the infrastructure required to capture the data coming back from the … Read more
In last week’s blog post, I discussed the first step in adopting data-driven development (see figure below), i.e. modeling feature value in quantitative terms. Once we have described the value that we expect from a feature, the next step is to ensure that we can indeed measure and collect the necessary data in order to … Read more
In my research and consulting engagements with companies, one of the recurring themes is the ambition of companies to become more data-driven in their way of working. After working on this topic with a variety of companies, my fellow researchers and I defined an adoption process that companies go through when adopting data-driven development practices. … Read more
