Interdisciplinarity is a success factor in all areas of IT service and product development, both in terms of productivity and efficiency. The early involvement of experts and users from the application areas involved ensures that services and products find numerous users and can be successfully placed in the company or in external markets.
Data science, machine learning and artificial intelligence already play a central role in many companies today. And yet, the results of Data Innovation projects and the success of Data Monetization are mostly behind expectations. Why? Because traditional big data approaches that focus on technologies no longer meet the demands of a holistic, data-driven corporate strategy. Huge amounts of data are usually collected without a goal and only used as a basis for the identification of use cases in a second step. Above all, interdisciplinary thinking and agile working methods often come too short in this change.
Think user-centered from the start of the project
Design thinking as a systematic approach to complex problems ensures in the first steps that the user’s wishes and needs are incorporated into the problem definition, brainstorming and conception. Design Thinking takes the human perspective as its starting point to design innovative services/products that are attractive, feasible and marketable.
By focussing on users perspective Design Thinking designs and develops the implementation of new solutions or the optimization of existing services on the basis of strategic objectives with the participation of an interdisciplinary team of data analysts, information architects, product managers and IT.
Data Thinking places the users AND the underlying data central
In addition to the user-centered view of the persona, the data-thinking approach can be extended by the description and detailed information on the data used to create a data-driven added value. Companies first want to understand their potential on the path to digital transformation before making further investment decisions based on fast user and market feedback. Exemplary practical questions initially need to be answered on a strategic level:
– How mature is the company for digital and data-driven business models?
– How can data be effectively used for the company in services / products?
From this point of view, there are concrete operational challenges and opportunities for optimization that, for example, need to answer the following questions:
– Which data sources are already available, and which could be added as well?
– Which areas can be used to adapt existing service / product solutions?
A product solution can be, for example, a digital application for end customers, which displays various data sources represented in a mobile application as a navigation service in form of a map. Or, for corporate purposes, an in-house service solution might be an analysis dashboard that allows the product managers in the organization to view and steer the performance of the services they are responsible for at any time.
To answer these questions cost-saving formats such as Data Studio workshops with a two to three-hour time span up to one-week Data Thinking Sprints are possible. The procedures of these formats are subdivided into the following two phases:
Phase 1: Understand and interpret data and users
Understand: The first phase is about creating a common understanding of the challenge as a basis for the team and formulating the goal accordingly.
Observe: Following this, intensive research and field observations analyze user needs and data requirements in order to gain insights.
Define: After gathering the insights and framework conditions, these result into prototypical user profiles (Persona profile) and data profiles (data profile).
Phase 2: Design and implement solutions
Ideate: Within a brainstorming session, different data use case concepts are developed and visualized based on the Persona and data profiles (sketches, wireframes).
Prototype: The most promising concepts for the team are iteratively improved and evaluated in lean prototypes (data cockpit / dashboards).
Test: Through continuous collection of user feedback, test results are collected and applied within the prototyping phase.
In the systematic identification of data use cases, the data thinking method follows design thinking and places the needs of the customer or user at the center of all considerations. Data Thinking is a highly iterative and cost-effective data science approach that gathers fast feedback directly from the user and continuously tests, analyzes and optimizes potential solutions and hypotheses. Pervasive, data-driven use cases trigger a rethink within the organization – and accelerate the company-wide digital transformation.
Data Thinking enables organizations to evolve towards a data-driven business
The use of agile and creative approaches helps to culturally adjust the interdisciplinary interaction of employees, technology and processes. Acceptance for this cooperation, in which premises, interactions and time frames shape important framework conditions, is gained through visible results and progress. Data Thinking puts the really business-relevant use cases at the beginning of every data strategy – and only then defines which data in which quality is required for the implementation of data-driven services / products.
Through our daily consulting experience in different branches and topics, we have developed a comprehensive knowledge in digital product development and agile project management.
In the near future we will publish a detailed article about Data Studio.
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