Machine & Deep Learning: Non-Critical Deployment

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 moreMachine & Deep Learning: Non-Critical Deployment

Machine & Deep Learning: Experimentation Stage

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 moreMachine & Deep Learning: Experimentation Stage

It’s All About Your Digital Twin

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 moreIt’s All About Your Digital Twin

How To Deliver Proven Business Value

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 moreHow To Deliver Proven Business Value

Why You Need To Slice Your Features

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 moreWhy You Need To Slice Your Features

Data-Driven Development: Step 2 – Build Data Collection and Analysis Infrastructure

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 moreData-Driven Development: Step 2 – Build Data Collection and Analysis Infrastructure

Data-Driven Development: Step 1 – Model Feature Value

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 moreData-Driven Development: Step 1 – Model Feature Value

From Dozens to Thousands (or Millions) of Products

This week I spent time in Denmark at a summit organized by the Manufacturing Academy of Denmark (MADE). During the summit I met several people that expressed pride at the richness of their product portfolio. Some companies have a dozen different products, others may have close a hundred products. All these products are complicated including … Read moreFrom Dozens to Thousands (or Millions) of Products