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Data Thinking as a power tool for data-driven service and product development
Data Thinking as a power tool for data-driven service and product development
2023-11-16
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Data Transformation
Interdisciplinarity is a success factor in all areas of (IT) service and product development, both in terms of productivity and efficiency. Early involvement of experts and users from the relevant application areas ensures that services and products gain numerous users and are successfully placed within the company or on external markets.
Data Science, Machine Learning, and Artificial Intelligence already play a central role in many companies. However, the outcomes of data-driven innovation projects, market success, and the associated monetization often do not meet expectations. Why? Because traditional Big Data approaches prioritize technologies and fail to meet the demands of a holistic, data-driven business strategy. Huge amounts of data are often collected without a clear goal and only used as a basis for identifying use cases later. Interdisciplinary thinking and agile working methods are often neglected in this transformation.
Thinking user-centered from the project start
Design Thinking, as a systematic approach to complex problems, ensures in the initial steps that the wishes and needs of users are incorporated into problem-solving, idea generation, and concept development. Design Thinking starts with the objective of creating innovative services that are attractive, feasible, and marketable.
With a central focus on users, Design Thinking initiates the implementation of new solutions or the optimization of existing services under the involvement of an interdisciplinary team of data analysts, concept developers, product managers, and IT professionals.
With a central focus on users, Design Thinking initiates the implementation of new solutions or the optimization of existing services under the involvement of an interdisciplinary team of data analysts, concept developers, product managers, and IT professionals.
Data Thinking puts users AND underlying data at the center
The Data Thinking approach extends the user-centered perspective of personas to include descriptions and detailed information about the used data, leading to data-driven value creation. Companies aim to understand their potential on their digital transformation journey before making further investment decisions based on rapid user and market feedback. Practical questions at the strategic level need initial answers:
– How ready is the company for digital and data-driven business models?
– How can data be effectively used for services or products within the company?
From this, specific challenges and optimization opportunities at the operational level arise, answering questions like:
– What data is already available and what could be added?
– To which areas can existing service or product solutions be adapted?
A product solution could be a digital application for end customers that displays various data sources in a mobile app as a navigation service in the form of a map. Alternatively, a service solution could be an internal company analytics cockpit, allowing product managers to view and manage the performance of services under their responsibility at any time.
To answer these questions, cost-effective formats such as Data Studio Workshops, lasting two to three hours, or week-long Data Thinking Sprints can be used.
These formats follow a two-phase approach:
– How ready is the company for digital and data-driven business models?
– How can data be effectively used for services or products within the company?
From this, specific challenges and optimization opportunities at the operational level arise, answering questions like:
– What data is already available and what could be added?
– To which areas can existing service or product solutions be adapted?
A product solution could be a digital application for end customers that displays various data sources in a mobile app as a navigation service in the form of a map. Alternatively, a service solution could be an internal company analytics cockpit, allowing product managers to view and manage the performance of services under their responsibility at any time.
To answer these questions, cost-effective formats such as Data Studio Workshops, lasting two to three hours, or week-long Data Thinking Sprints can be used.
These formats follow a two-phase approach:
Phase 1: Understand and interpret data and users
Understanding: In the first phase, the goal is to create a common understanding of the challenge within the team and formulate the objectives.
Observing: Intensive research and field observations follow, analyzing user needs and data requirements to gain insights.
Definition: The gathered insights and conditions flow into prototypical user profiles (persona profiles) and data profiles (data profiles).
Observing: Intensive research and field observations follow, analyzing user needs and data requirements to gain insights.
Definition: The gathered insights and conditions flow into prototypical user profiles (persona profiles) and data profiles (data profiles).
Phase 2: Design and implement solutions
Idea Generation: In a brainstorming session, different data use case concepts are developed and visualized (sketches, wireframes) based on the persona and data profiles.
Prototyping: The most promising concepts are iteratively refined and evaluated in initial, low-effort prototypes (data cockpit/dashboards).
Testing: Continuous user feedback collection feeds back into the prototyping phase to improve the solutions.
In systematically identifying data use cases, the Data Thinking method follows Design Thinking, placing customer or user needs at the forefront of all considerations. Data Thinking is a highly iterative and cost-efficient Data Science approach that requires quick feedback directly from the user, continuously testing, analyzing, and optimizing potential solutions and hypotheses. Successful data-driven applications trigger a shift in thinking within the organization and accelerate company-wide digital transformation.
Prototyping: The most promising concepts are iteratively refined and evaluated in initial, low-effort prototypes (data cockpit/dashboards).
Testing: Continuous user feedback collection feeds back into the prototyping phase to improve the solutions.
In systematically identifying data use cases, the Data Thinking method follows Design Thinking, placing customer or user needs at the forefront of all considerations. Data Thinking is a highly iterative and cost-efficient Data Science approach that requires quick feedback directly from the user, continuously testing, analyzing, and optimizing potential solutions and hypotheses. Successful data-driven applications trigger a shift in thinking within the organization and accelerate company-wide digital transformation.
Data Thinking supports organizations on their path to becoming data-driven
The use of agile and creative approaches helps culturally adjust the interdisciplinary collaboration of employees, technology, and processes. Acceptance for this collaboration, where spaces, interactions, and time frames shape essential conditions, is gained through visible results and progress. Data Thinking puts truly business-relevant use cases at the forefront of any data strategy and only then defines what data is needed in what quality for the implementation of data-driven services or products.
Through our daily consulting practice across various industries and sectors, we have comprehensive know-how in digital product development within agile project management.
If you have any questions or need support, please feel free to contact us.
Through our daily consulting practice across various industries and sectors, we have comprehensive know-how in digital product development within agile project management.
If you have any questions or need support, please feel free to contact us.
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