12/15/2023 0 Comments Data product manager![]() And the moment this happens, it finally leads to the situation where organizations struggle with truly adopting data products into their business processes, products or services. What is wrong with this and why? Coming from the world of data product management, projects can decoy you into a trap: Many data initiatives and developments - managed in a project mode - bear risks to forget the user. Equally, machine learning projects are often considered successful if a model can predict something with a certain accuracy. The initiative is considered successful the moment the desired output is generated. They largely follow a structure similar to other initiatives in large enterprises: A predefined scope should be delivered against specific budget constraints and time limitations. We call this role the data product manager.Ī user cares whether his problem is solved - not if a machine learning model has 90% or 95% accuracyĪI initiatives in “traditional” enterprises are often organized as projects - even if the goal is to develop a “data product”. We argue that there is a need for a dedicated discipline and role to make data-driven products successful. Secondly, product managers need to understand the unique requirements of data products and how machine learning features are developed as they will to some extent be part of any digital product in the future. Many AI initiatives forget to consider what value an initiative creates for the users as AI has inherent characteristics that make accurate planning quite difficult in the early stage of the initiative.Ĭhallenges as such address two types of people in today’s teams:įirstly, AI people are currently mostly equipped with technical skills and typically lack knowledge from the domain of digital product management with its processes and methods. But above all, one major reason that we observe with increasing frequency is the lack of an AI product mindset. the competition for skills and talents, the adoption of new technologies, and, of course, it’s easier to apply machine learning when you have a fully digital product and no legacy IT systems. What is the reason for this lack of impact in “traditional” enterprises and the high share of failed projects? Certainly, there is a variety of reasons, e.g. A large number of initiated ML projects remain stuck at a PoC level and fail to reach the hurdle of going into production - studies report failure rates between 80% and 90%. On the other side there are large companies that, amidst the Covid-19 pandemic, continue to heavily invest into data and AI initiatives ( NewVantage Partners), but only a minority report significant business impact of their initiatives. In fact, the business models of companies such as Uber, Airbnb, Zalando, or even TikTok would no longer function today without the use of machine learning. Looking at the application of artificial intelligence and machine learning these days, a big chasm seems to be opening up: On the one side there are most of the tech companies where applying machine learning is the norm.
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