Data-Driven Service Design
When data and design combine, service innovation can thrive. All too often overlooked by Service Designers, quantitative data can be the trigger, complement, challenge or foundation for traditional qualitative research.
In an article by McKinsey, published in April 2019, entitled “Fusing data and design to supercharge innovation — in products and processes” the authors found a “10 to 30 percent performance improvement among companies that tightly interweave data and design capabilities to solve business problems”. This was achieved through three key shifts that organisationally tie data and design together more effectively:
- moving from silos to squads of data and design literate teams
- moving from disconnected workflows to deep synchronicity of data and design skills
- moving from product innovation only to operations-wide use of data and design
The article concluded: “The more data and design experts are integrated and work together, the earlier they begin working together on a project, and the wider their purview across the enterprise, the greater the reward.”
So how can data be used in conjunction with Service Design to deliver value?
By incorporating both qualitative and quantitative data into service design initiatives we can understand human behaviours, revealing not just what is happening but also why it is happening. The UK Government’s Policy Lab, wrote in 2020, how they have been using big data to see the big picture before using ethnographic research to zoom in to the detail of people’s lived experience. They call the ethnographic films and analysis “thick data” which sits alongside quantitative data, helping policy-makers to build a rich picture of current circumstances. Whereas big data gives cumulative evidence at a macro level, thick data provides insights at an individual or group level.
In a 2019 paper, “Data-Driven Service Innovation: A systematic literature review” Christian Engel and Philipp Ebel investigated the integration of data into the field of Service Innovation — referred to as “Data-Driven Service Innovation” (DDSI). The review revealed three main perspectives on how data can be used in relation to Service Innovation: explorative DDSI, validative DDSI, and generative DDSI.
The team from the University of St. Gallen defined these terms as follows:
Explorative Data-Driven Service Innovation
“The use of data and analytics for discovering opportunities, such as needs, trends, ideas, or design options, for new or advanced services or (product-)service systems of any kind. From a process perspective, this can be viewed as data and analytics being the trigger of the Service Innovation process.”
More on Explorative Data-Driven Service Innovation.
Validative Data-Driven Service Innovation
“Addresses the guidance of the service development processes with data and analytics-driven software tools with the goal to monitor the success and
stepwise process achievements towards final Service Innovation.”
Generative Data-Driven Service Innovation
“Focuses on data as a key resource for value creation directed towards the customer. For example, user-generated big data to predict future customer needs, crowdsensing to enable traffic optimisation and predictive maintenance solutions based on machine operations data offered as a value-added service on top of the machines themselves.”
So, whilst explorative and validative DDSI do not necessarily incorporate a final service outcome which relies on data for service delivery, generative DDSI focuses on data as a key resource for value creation directed towards the customer.
How can companies make the most of data when designing services?
To help companies exploit the benefits of data analysis in the context of service design, a pilot development program was developed in Finland called “Data Driven Business” (or DOB in Finnish, Datasta oivalluksia ja bisnestä). The program developed an innovation platform consisting of processes, methods, and tools for data analysis and service design.
The data analysis process consists of the following tasks, which can be iterated as required:
- Understanding the situation and needs.
- Gathering and preparing a data matrix.
- Understanding the data.
- Making a model and applying it.
- Evaluating the results.
- Reporting and deploying the results.
The analysis methods used in DOB can be either “descriptive”, “diagnostic” or “predictive”. Descriptive analysis is used to understand the data — what has happened and what can be seen directly from the data using statistical analysis, for example, distributions with medians or percentiles. Diagnostic analysis is used to understand why something happened, for instance, by understanding correlations between phenomena. Predictive analysis is applied in order to foresee what will happen in the future. All of these insights are then brought into the service design process.
The Service Design studio take the guesswork out of innovation and improvements. Our Data Bakery kickstarts and empowers data-driven organisations.
Further reading
How to Build Competencies for a Data-Driven Business: Keys for Success and Seeds for Failure
“Data-Driven Service Innovation: A systematic literature review”
Fusing data and design to supercharge innovation — in products and processes
