We have previously talked about design fiction at BBVA D&A and how it has aligned and eliminated boundaries between data science teams and their stakeholders. We have also seen the potential of advanced analytics when applied to product development in finance. The latter is a must-do in order to foster an effective exploration of data scientists and designers during product development (beyond visualization and prototyping). At BBVA D&A we are spearheading the change that comes with the current digital transformation of the financial sector.
At BBVA Data & Analytics we continue to explore how to apply design fictions in production, and how these techniques can be useful in the early phases of the project. This is when stakeholders and the field of work are being defined and there is a need to find common expectation, required skills and forming the teams in order to develop innovative solutions collaboratively.
Visions and revisions
With the main goal of establishing a common vision that encompasses business, data science and design we, at BBVA, have integrated multidisciplinary teams that identify areas of opportunity for data-driven products.
In this context, the previous production of fictions (diegetic prototypes) have been of great value, since it communicates with a narrative possible futures in which the solution proposed is a reality. Furthermore, it allows us to present a tangible vision of which we have ownership, we project ourselves and is food for debate about what future we are actually building.
The process of creating a design fiction is inherently flexible, it allows multiple and quick iterations, and results of a refined vision that considers the debate and opinions generated in the revisions. Ultimately, a balance is found among the participants. This dynamic helps to clarify and define a directions towards success.
These fictions are not trying to propose a definitive solution, but rather envision a time horizon that can guide the interdisciplinary dialogue, creating synergies, orienting efforts and in the long run deliver functionalities.
Besides, from a data science standpoint, the process of distilling the vision through design fiction helps identify the main hypothesis, the technical viability, capacity and skill sets for the desired solutions
Despite the ubiquity and deep integration of digital products and services in society, we believe that customers see a differential value in the humane treatment that technology cannot easily replace, how is a future in which advances in Artificial Intelligence and advanced data analysis capabilities are put at the service of our managers to provide a better service and a differential experience to our customers?
One of the options we explore from the perspectives of business, data science and design is the creation of design fictions as a useful tool to abandon found positions or clichés. For example, the current trend is towards customer service automation, but is that really what you we, as developers and users want?
are there design alternatives? does it make more sense to use technology to make the work easier for managers than for end users? what are the implications of deploying this type of functionality? These questions allow us to learn and iterate without writing a single line of code, and avoid possible friction with the end user.
In this context, the production of design fictions builds a productive dialogue beyond what seems possible or feasible, which is usually different for each of the parties involved in a project. By focusing not on a specific functionality but on the description of a future solution, they put the focus on the context and encourage the exploration of solutions.
Finally, from the perspective of technical discipline, the creation of design fictions can also serve to identify problems and risks in the use of data, availability, etc. As in the previous case, reviewing the problem with a broad perspective allows you to pivot on possible solutions and explore options when choosing the type of user interface, its design, how to collect feedback or data generated during use. If the acceptance of certain functionalities is subject to debate, which is something that the dynamic itself stimulates, it is possible to define experiments that allow us to test whether the design decisions are appropriate.