EDITOR’ S QUESTION 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 existing manufacturing facilities. The purchasing team, separately, approaches the data science team to reduce the cost of each part. To do this, they want to identify where they have multiple suppliers and move to a model with a single global supplier that offers larger volumes and reduces costs. These competing philosophies will each optimise a piece of the business, but in reality, the outcome should be a single optimised approach for the business.
Instead of developing competing solutions, a centralised data science team can balance competing objectives and deliver an optimal solution that’ s aligned with overall strategy.
• Applying analytics across domains
The best breakthroughs in analytics come not from new algorithms, but from applying existing methods to innovate use cases across domains. A
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