EDITOR’ S QUESTION causing product failure, identifying plant processes or considering customer demographic data. These are not isolated problems confined to a single department. The solution spans multiple domains.
A centralised data science function is ideally positioned to tackle such complex problems. It can draw insights from various domains as an integrated team to create holistic solutions without different parts of the organisation working at odds with each other. In contrast, where data scientists report to individual departments there’ s a big risk of duplicating efforts and developing siloed solutions that miss the bigger picture.
• Fostering talent with clear career paths
Organisations shouldn’ t neglect the fact that data scientists need clear career paths. The most important asset of any data science domain is the people. If data scientists work in small, isolated teams within specific departments career development opportunities can be limited as they’ re not exposed to a broader range of problems. For example, a data scientist in a three-person marketing analytics team has fewer opportunities and less interaction withthe overall business than a member of a 25-person corporate data science team reporting to the C-suite.
Centralising the data science team enables a more robust career path and fosters a culture of continuous learning and professional development. Data scientists can collaborate across domains, learn from each other and build a diverse skill set that enhances their ability to tackle complex problems. Moreover, it’ s easier to provide consistent training, mentorship and development opportunities where data science is centralised, ensuring that teams are fully equipped with the best tools and techniques. In this model the BI team has a foundation to upscale and with a strong data science leader will move well past BI.
• Analytics as a connector
A centralised data science function acts as a valuable connector across different parts of the business.
Let’ s take an example. Two departments approach the data science team with seemingly conflicting requests. The supply chain team wants to minimize shipment costs and asks for an analytic that will identify opportunities to find new suppliers near
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