PATRICK MCQUILLAN
THE BIOLOGICAL MODEL By PATRICK MCQUILLAN
PATRICK MCQUILLAN has a successful history leading data-driven business transformation and strategy on a global scale and has held data executive roles in both Fortune 500 companies as well as various strategy consulting firms. He is the Founder of Jericho Consulting and a Professor at Northeastern University and University of Chicago, where he teaches graduate programmes in Analytics and Business Intelligence.
THE CONCEPT OF A BIOLOGICAL MODEL How would you describe the concept of a ‘biological model’ that you developed in the context of data strategy and organisations? It’s essentially the idea that all data, decision-making power, and resources should be consolidated into a single epicentre that’s connected throughout an entire business or organisation. It becomes a self-feeding system that can quickly react to, or predict, any anticipated challenges or bottlenecks based on what it’s historically encountered. Crucially, the model continues to learn and provide an adjacency between access to data and the immediate access of that data by key decision-makers in
the organisation, rather than disseminating it to individual managers on different teams, and having decentralised analytics hubs or centres of excellence that might exist across different verticals that don’t communicate with each other. What’s the difference between your biological model that functions like a nervous system, and centralised data structures that currently exist? For example, small or medium-sized companies tend to be centralised already, as they don’t have the capacity to decentralise everything. Excellent question. The biological model is so named because it simulates the central
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nervous system. The nerves collect information on what’s felt in the fingertips and internal organs, and automated processes like blinking and breathing; they’re similar to AI. But the brain is the epicentre: the key decision-making component which processes the non-automated, conscious decisions like grabbing things, and influencing the world. So when using the biological model, it’s crucial to have a robust data foundation. With this in place, the model rectifies two common issues encountered when using traditional models: The first issue is that large companies with decentralised data centres can’t communicate with each other effectively, making it difficult to get the full picture
