Every child in the Netherlands should receive an equal opportunity to begin their life with the best possible prospects. The first 1000 days of a child’s life are of utmost importance in establishing a strong foundation for future development. Children may experience a disadvantageous beginning due to various factors such as exposure to smoking or stress (Ministerie van Volksgezondheid, 2018; Roseboom, 2018). To offer the best start also for children, the objective of this master thesis is to develop a digital tool that leverages the potential of big data to aid in the early identification and referrals of families living in vulnerable circumstances. The research initiative known as “Making Big Data Meaningful for a Promising Start” is funded by the Dutch research council known as Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), seeks to detect vulnerable situations at an earlier stage using predictive models that use existing large amounts of data (big data) to identify potential adverse outcomes for children. The prediction model has the potential to estimate the level of risk a family faces for experiencing a negative outcome in the future. In order to cater this model to the parents’ needs, various user-centred design methods were used. The approach of this project was first to understand the context of the first 1000 days and understand who is involved, how the current risk identification and referral works, and the experiences of the parents and the healthcare professionals. Literature research and qualitative research methods such as interviews and observations provided insights into the different perspectives of parents and healthcare professionals in this specific context. This approach led to identifying several significant needs that would guide the design direction for the future conceptual tool. These needs include trust, safety and self-esteem. Parents often face fear regarding the potential outcomes linked to revealing specific information, and they may also experience emotions like shame, guilt, or self-doubt when they believe they are incapable of adequately providing for their child. This may result in parents choosing not to disclose information, which can lead to delayed identification and potential referral. Once the underlying needs of parents were understood, the following phase involved creating together and exploring the solution space surrounding a digital tool. To achieve this, co-creation sessions were held with parents and healthcare professionals. During the co-creation sessions, several additional requirements emerged that were deemed essential for the conceptualization of the digital tool. Among these newly identified requirements was the consideration that if the tool incorporated a prediction model, it must not only identify risk but also offer actionable solutions and be implemented with repetitive use since circumstances can change over time. Through understanding and aligning the needs of parents, a final digital concept Advies Op Maat was designed. The concept allows parents to fill in their information in a sandbox environment, enabling them to try the tool before committing to sending the information to the healthcare professional. Based on the information filled in, parents can receive preliminary advice and choose whether they want to continue. By conducting moderated user testing, the experience of the digital concept was evaluated and explored. At the moment, a significant challenge in achieving trust and safety relates to the questions posed within the tool, which can be sensitive and confrontational for parents. At the conclusion of this project, iterations were made to address and improve these specific aspects to potentially use parts of the concept in the next phases of the research project of the “Big Data and a Promising Start” initiative.