Leveraging data and analytics and performing data science is the “last-mile delivery” of a much bigger and longer supply chain.
A supply chain transports goods from the factories to end consumers, which can involve many different modes of transportation and steps before the goods even reach the final warehouse. From there, they need to be organized, stored and tracked so inventory can be located quickly to ship out to end consumers when orders are received. When one step in the supply chain fails, it can be catastrophic to the entire system—creating delays and dissatisfied end consumers.
To fulfill orders in a timely fashion, supply chains rely on proper systems and automated processes to make the goods available for those last-mile delivery providers. Data science is much the same way.
Data scientists can’t analyze data and provide insights to business decision makers in a timely manner without data that’s already been collected, transformed, organized and stored in a proper manner.
Business leaders and decision makers may think the work of data science is taking too long or that data science teams are ineffective, but what they might not realize is the issue is much earlier in the supply chain. They’ve hired excellent last-mile delivery providers without building the supply chain needed for them to deliver insights in a timely manner. That leads to data science teams taking on the additional effort of building the supply chain—something they may not be trained to do—instead of focusing on delivering the results they were hired to provide.