Ask a Friend, Iiris Lahti AI Roots

In the past few months, what have you been experimenting with and learning from it, Founding partner of AI Roots Iiris Lahti?

We have been experimenting with a new Data Design method with our customers in various cases. The idea has been to test how basic design thinking, lean service creation, and user-centric service design methods can be used in designing data products and assets.

We have started the experimentation with a very simple process and helping our customers in understanding the core business problem, innovating alternative data solutions to solve it, and evaluating and prioritizing them based on the data availability, quality, and operational readiness. Once the solution has been selected and piloted, the process goes forward to continuous development, productization, and optimization.

As a part of the process, we have also tested a great variety of different canvases and design methods to spark innovation and bring structure to the design work.

Learnings

The experiences of testing the method in various organizations and teams have been very rewarding. Through experimentation, data analysts, engineers, architects, and scientists have realized how important it is to truly understand the business problem and spend more time in the design phase before jumping into the development of the solution.

Similarly, the business owners and end-users realized that they need to explain their needs properly to the data experts and not to take their understanding of the business problem for granted. The most impactful learning in this experimentation has been the power of collaboration and joint innovation sessions. The best and most innovative ideas are created in conversations between business, technology, and data experts. The design process and methods can help to facilitate the ideation session and act as a reminder of the things that need to be covered during the design phase.

The second learning has been to reserve enough time, resources, and allocation for the design work. In the best case, there would be a dedicated design sprint before the actual development sprint starts. Design methods and tools are valuable when the team needs an extra boost to creativity and collaboration effectiveness, but they are not enough alone. In data development projects, a feasibility study is as important as the ideation phase, including analysis on the data availability, usability, quality, and appropriateness to the use case.

More information: https://www.rootsof.ai/blog/data-design

Thank you Iiris!