Based on our research, we’ve developed a four-dimensional model for the digital transformation in the software-intensive embedded systems industry. In the last two posts, we explored the business model and product upgrade dimensions. This post is concerned with the data exploitation dimension. As shown in the figure, the first step in most companies is focused … Read moreExploiting your data well
In last week’s post, I mentioned our framework describing the transformation that companies go through when going digital. I also discussed one of its four dimensions – the business model dimension. In this post, the focus is on the product upgrade dimension. As shown in the figure, we’ve identified five steps or phases in the … Read moreBetter all the time
Over the last months (actually, more like years), we’ve studied the digital transformation of several companies in the Software Center. Professor Helena Holmström Olsson and I developed a model to illustrate how they actually transition from their legacy business rooted in atoms to a digital business based on bits (see the figure). It has four … Read moreDigital for real: business model
This week, I had an interesting discussion about data with the CEO of one of the startups I work with. The challenge is that many companies are collecting vast amounts of data, storing it and then leaving it as an unused asset. It surprises me that so many companies are maintaining such amazingly large data … Read moreIt’s not about data; it’s about actionable insights
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 moreWhat’s with all the Ops?
In my last column, I presented our research agenda for AI engineering. This time, we’re going to focus on one of the topics on that agenda, ie data versioning and dependency management. Even though the big data era has been with us for over a decade now, many of the companies that we work with … Read moreAI engineering part 2: data versioning and dependency management
This week, for the umpteenth time, I met a team in the process of putting a new product in the market, telling me that they were so customer centric. What they meant was that during development, they’d talked to a number of potential customers and some of the employees had used prototypes. For those that … Read moreSo, you’re customer centric?
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 moreHow to generate data for machine learning
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 moreAI is NOT big data analytics
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 moreWhy your data is useless