Thinking of Analytics as a Supply Chain
Despite making big investments in advanced analytics for managing supply chains, many organizations are not getting the results they’d like. Sourcing data, building models and delivering solutions can take too long, and key steps are often overlooked. This is why minimizing inefficiencies in the analytics supply chain is vital.
It’s easy to view analytics in terms of the supply chain. When it comes to the analytics supply chain, customers are the decision-makers, while the products that they consume are the analytical models. Analytics engines providing solutions and recommendations can be likened to manufacturing plants in the sense that they transform data into consumable decisions. The raw materials, meanwhile, are all of the data that is used to come up with the analytical models. The supply chain output in this case is better decisions.
With this in mind, a closer look at data can provide some interesting revelations. Just like a lack of proper raw materials can wreak havoc on a physical supply chain, low-quality data can have a huge negative impact on your analysis and the decisions that are reached. Similarly, analytics supply chains that involve too much time or expense to deliver the needed solutions are sometimes brushed aside in favor of less productive options that happen to be more readily available.
One of them main benefits of thinking of analytics as a supply chain is that it enables firms to take a holistic and broad look at the use of analytics. Having complex analytical models won’t do much for a company if they can’t be deployed.
Measuring the performance of the broad analytical processes in an analytics supply chain is also useful. Some factors to measure include inputs, such as the number of analytics or models created, and outputs, such as the business value the decisions bring about. This can help improve the time to insight by a significant amount.
This blog post was based off of an article from LinkedIn. View the original here.