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Generali Investments
Data are a User Experience
Through this article, Francesco Giordano, Head of Data & AI Engineering & Platform Services, explores the evolving role of data, AI, and emerging technologies in asset management. He discusses the challenges of applying machine learning in financial markets, the need for scalable data infrastructures, and the transformative potential of quantum-ready technologies. He also examines how asset managers must adapt to technological advancements such as AI, blockchain, and cloud computing to remain competitive. He concludes by emphasizing the critical role of data strategy and innovation in shaping the future of the industry
The Importance of Data
It is pretty simple to stress the relevance of data today and how much analytics is of crucial importance in determining success in a fast-changing market like the one we experienced in the last few years. But I don’t want to fall into the classic cliché of statements like: “data is the new oil” or something similar. Instead, I want to say that investing in a comprehensive enterprise-wide data strategy has become crucial, today more than ever. The importance of making informed decisions is of paramount significance pretty much in every sector. But, maybe with some bias, not considering a profound renewal of the data stack in the financial industry (for an Asset Manager in particular) means being lost in the dust by competitors.
What Does it Mean to Invest in Data?
So far, the obvious. But what does it mean concretely to invest in data? It means many things, of course. Namely, it requires to re-consider not only the creation of a modern technology stack but also committing with patience towards a cultural shift of the overall behaviors in the Organization. It means to re-think the use of analytics, which are no more than an overlying layer used to show excellent charts to management, but also something more integrated that must be deeply embedded into the core transactional processes of the Organization. Decisions must be taken “in place”. Finally, it means to “democratize” the access to information (just falling again into another quite abused expression) to everyone in the Organization. Unicorns are giving several examples in this direction.
Mesh Up with Data
It is not my intention to introduce the concept of Data Mesh. Still, for the few of you that have ignored the buzz so far, Data Mesh is a cultural and technical concept that distributes data ownership across product domain teams with a centralized data infrastructure and decentralized data products. For example, the engaging readers may refer to the recent book of Zhamak Dehghani, who first introduced the new term some years ago. This paradigm is becoming increasingly
common when discussing data strategies, decentralized analytics, the importance of data democratization, and the expected path toward insights-driven organizations.
There are, surely, several pre-conditions that must be matched in the Organization for this strategy to be applied with any hope of success. But it is in the facts that Data Mesh is gaining momentum among big companies dealing with data revolutions. And this is true mainly because it addresses many of the concerns related to those critical topics on data.
And so, like many others, we have been working hard in the last years to begin our Data Mesh journey, and we successfully launched our first Data Products. With some success, we have immediately seen the Business warmly welcoming a self-serve approach to data in a domain-driven brand-new product. But here ends the favor and begins the problem.
Product Orientation is Not for Everyone.
Yes, because being an expert in a domain, knowing exactly (which is not often the case) what other colleagues might need from data, and accurately defining the requirements (which again is not always the case) does not mean being a Product Owner. Orienting the evolution of data products with the concrete concept of Products at its foundation requires a different approach to which old-fashioned companies might not be used.
Talking about data strategies, decentralized analytics, the importance of data democratization and the common path towards insights-driven organizations, this paradigm is becoming more and more common.
Users are used to changing requests, requirements, projects, deadlines, and costs. Not vision. Not internal clients. And especially not user experience. Needless to say, it is the same for the IT sector. Great product teams are user-centric and proactive; everyone seems to accept that. But there is no consensus that data teams must do the same to be great.
User-Centricity
Data teams must attach the same importance to users as product teams do. Both teams are trying to solve user problems. However, data Product teams are business teams on their own. They, therefore, must prove their value to the Company by concretely listening to user feedback and needs and solving their problems. Not passively receiving requests but continuously adapting features and functionalities to solve the issues and always responding better to unexpressed questions.
Data is a User Experience.
But it is even more than that. It is ultimately a user experience like every other tool we use in our daily life.
Why are we so unwilling to accept even the slightest delay or bug in the tools we use every day, and have such a poor experience when it comes to working? Why are we having awful interfaces regarding internal applications? If we do not pay attention to those aspects, how are we supposed to profoundly change the behavior of people in our Organizations to make them pay that much attention to data and analytics to make decisions?
Conclusions
Data Mesh is just one of the cultural shifts in the data ecosystem we are experiencing today. Data Fabrics and Data Contracts are other emerging patterns. Those are the mantra today, and with good reason.
Those approaches entail moving away from the classic old-fashioned idea of internal transformation projects to consider a product-driven and holistic approach instead. But being product oriented is not for everyone. It requires radically changing how we build data systems and trying to persuade people to use them. It is not just a matter of quality and reliability (of course, of paramount importance). For those systems to realistically drive the change, we need to treat users as clients (even when they are colleagues) and re-imagine their experience in a way they would love it. We should do product marketing, as well (even internally). We should sell our (data) products precisely as we would for any product.
