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

Become Data-Driven In Five Steps

Although most of the online (SaaS) companies that I work with are heavily data-driven, the embedded systems companies have had more difficulty to achieve that. With connectivity becoming more and more the norm for embedded systems, though, these companies are now also starting to become data driven. This is great as replacing opinion-driven decision making … Read moreBecome Data-Driven In Five Steps

Stop Customizing Your System! Configure It Instead!

Although mass-market companies have figured this out long ago, companies offering their software-intensive systems to a smaller group of powerful customers are often under significant pressure to customize their systems for individual customers. There are at least three reasons why customizing your system for each individual customer used to be a good idea. First, when … Read moreStop Customizing Your System! Configure It Instead!

Enough Efficiency Already! Focus on Effectiveness!

Since the summer, I have worked with several companies that are starting to see continuous deployment on their horizon. This is great progress and brings many advantages such as fast feedback on quality issues in the field as well as the ability to quickly fix any issues that customers experience. Internally, more frequent deployment often … Read moreEnough Efficiency Already! Focus on Effectiveness!

Engineering Deep Learning Systems is Hard!

(Image credit: pixabay) This week I spent in the lovely city of Prague, attending the SEAA 2018 conference. The main reason for attending was that I had the opportunity to present a paper that I co-authored with colleagues from Peltarion about the software engineering challenges of deep learning. Peltarion offers an amazing platform for building … Read moreEngineering Deep Learning Systems is Hard!