When discussing data-driven development and the use of data to identify new features and products, it is always the needs of the organization that come first. The companies design the system, design their organization’s needs and then experiments which will provide the organization with the data needed to validate the hypothesis.
). What the theories prescribed, back then, was that the organizations should only look at their goals and needs. What we discovered was that it was a combination – what the company needs and what it can actually measure. The reality of the organizations that we studied was that not all needs could be fulfilled by the data they had or by the data they could possible have.
The article did not have anything to do with the measurement programs, but it had a lot to do with the data. It’s content was about the global apps, but what caught my attention was the concept of providing the user with the feedback what he/she can do with the data, rather than what data is needed for the task.
Sounds a bit crazy, but I think that it’s an important step towards a real data-driven development. Imagine that instead of thinking about discussing what we should do and how to do it, we can take a look at the data and immediately know what we can do.
If we know directly what we can do with the data, then we can just do it (or not) rather than spend time to discuss whether we can or cannot do it.
What it also means is that we can think more about the product than thinking about the data. We can think about what which features can be developed or dropped from the product. We do not even need to design experiments, we can just observe the products in field.
Software engineering is an applied scientific area. It includes working with industrial applications and solving challenges that modern organizations face today.
Thanks to many of my colleagues, I’ve had the opportunity to work with industry-embedded research since I arrived here in Gothenburg. I want to share these experiences with colleagues and students, which led me to writing a book about action research.
This book addresses action research (AR), one of the main research methodologies used for academia-industry research collaborations. It elaborates on how to find the right research activities and how to distinguish them from non-significant ones. Further, it details how to glean lessons from the research results, no matter whether they are positive or negative. Lastly, it shows how companies can evolve and build talents while expanding their product portfolio.
The book’s structure is based on that of AR projects; it sequentially covers and discusses each phase of the project. Each chapter shares new insights into AR and provides the reader with a better understanding of how to apply it. In addition, each chapter includes a number of practical use cases or examples. Taken together, the chapters cover the entire software lifecycle: from problem diagnosis to project (or action) planning and execution, to documenting and disseminating results, including validity assessments for AR studies.
The goal of this book is to help everyone interested in industry-academia collaborations to conduct joint research. It is for students of software engineering who need to learn about how to set up an evaluation, how to run a project, and how to document the results. It is for all academics who aren’t afraid to step out of their comfort zone and enter industry. It is for industrial researchers who know that they want to do more than just develop software blindly. And finally, it is for stakeholders who want to learn how to manage industrial research projects and how to set up guidelines for their own role and expectations.
In the eve of 2019, I got the time to read my copy of AI Superpowers. I must admit that I was sceptical towards it in the beginning. I’ve read a fair number of AI books and many of them were quite superficial – a lot of text, but not much novelty. However, this book seemed to be different.
First of all, the book is about the innovators and the transformations from low-tech to high-tech. The transformation is described as a process of learning. First copying the solution of others, then making your own. First learning the market, then creating your own. Finally, the examples of building the software start-up ecosystem are based on these small examples.
Second of all, the book discusses the issues that I’ve advocated for since a while back – the ability to utilise the data at hand. The European GDPR is a great tool for us, but it can stop the innovation. China’s lack of GDPR is a problem, but also a possibility. However, it needs to be tackled or it will never be fair. the description of the wars between companies show that the scene in China is not like it is in the Silicon Valley. It’s not great, but it was a mystery to me before. I’ve not really reflected upon that.
I guess that looking at the holistic picture of how Ai will affect the society is not very common. Well, maybe except the doomsday prophecies about how AI will take our jobs. This book is a bit difference in that respect. It looks at the need for basic income and how this could reshape the society. It discusses how this can be done both on the technical and on the social levels. To show a preview of it, please take a look at how the Kai-Lee predicts that the AI will affect our work.
Finally, I’ve got a number of ideas from the book. Ideas which I can use in the upcoming course about start-ups. I strongly recommend the book to my students and all entrepreneurs, who want to understand the possibilities of this new technology. I also recommend this book for people who believe in doomsday prophecies about AI – the revolution is near, but AI will not be like a Terminator. More like HAL 🙂