The Data Product Design Thinking Process

Especially in professional environments, very different requirements are placed on a new data product by very different users.

You can quickly find yourself in a situation where you have to solve a tricky problem (wicked problem).

Tricky problems are characterized by the following characteristics:• there are many interdependent influencing variables• there are no right or wrong solutions, only good and bad ones• you can’t solve them with a formula and logic alone• you can only solve them intuitively, not logically• there are social, psychological and cultural aspects to consider.

What are wicked problems?HOW TO DEAL WITH WICKED PROBLEMSThe challenge with data products is…on the content side:· the complexity of the underlying system is highon the data side:· data from different data sources must be connected to each other· the data quality is often insufficient· the preparation of data (ETL) is complex and time-consuming· data science techniques must be properly integratedon the visualization side:· the classic visualization methods (bar chart, pie chart, dot plot) are no longer sufficient· the possibilities of visualization with common tools like Qlik, Tableau and PowerBI are limitedon the user side:· the users have very different knowledge· they have to do very different jobs (jobs to be done)· decoding the information contained in data products and assigning it to your own knowledge is a challenge· they have to do very different jobs (jobs to be done)· the working environments of the users (medical technology, system control, vehicle control) are highly complexMore details on all these aspects can also be found in our article on the Data Design Guide.

hereHow to deal with wicked problems ?What is Design Thinking?Design Thinking offers a very effective approach to solving tricky problems.

Here, an interdisciplinary team approaches a potential solution in individual, systematic steps and from the user’s perspective.

This is then tested for suitability by users.

Design Thinking has become a well-known and well-proven method of developing innovation.

Many large technology and consulting companies use it.

The internet is full of practical and descriptive explanations of Design Thinking, so here is just a brief overview of the four underlying phases in the Design Thinking process.

· Discover — Discover the environment and context for the question or problem under consideration· Define — Formulate the requirements and needs of users· Develop — Develop initial ideas to the solution· Deliver — Create a solution and get user feedback.

The Data Design Thinking ProcessDesign Thinking for Data ProductsWe wondered whether this method could also be used for data products.

Design thinking is all about the users and their needs.

Now another very complex dimension is added: data or data science.

We then adapted the classic design thinking method specifically for data products and have been working with it very successfully since the beginning of 2018.

The challenge of using Design Thinking in the development of data products for business applications lies in the following points:• Data product development for Big Data and Data Science applications often breaks new ground.

At this point, the target use (job to be done) is not clearly defined at the beginning.

The context of use and user requirements must be developed equally with the data product.

Sometimes they only become apparent after the first visualization of the previously invisible system.

• The data competence of the user is unknown or too low to understand the message• Designers need a deep insight into the data structure, the raw data and the data pipeline.

What is developed creatively must also be technically possible.

A pure UX view is not enough.

• Many data products are based on data science algorithms.

Data and the potential of statistics and data science are the raw material for the creative process.

Data products only become efficient when they fully exploit this data potential.

Here, too, designers cannot go any further without an understanding of data literacy and data science.

• Data products in a professional environment are aimed at users and experts in specialist fields.

The data product thus intervenes deeply in so-called business domains — a further level of complexity.

Authors: Evelyn Münster / Christoph NieberdingAbout usEvelyn Münster and Christoph Nieberding are managing partners at Designation, a Munich based design company for data products and business design.



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