What use is your data?

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

How clean is your data?

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

DataOps: the key to operational AI

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

It’s not what AI can do for you

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

How to develop software

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

AI is not about data sets

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

You Think You Know, But You Don’t

Data-driven decision making frequently requires experimentation as a mechanism to acquire the necessary data. When working with companies in transitioning from opinion-based to data-driven decision making, however, I frequently run into push back. The typical comment is that we already know the answers to the specific questions at hand and since we already know this … Read more

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 more