Featured in PARCEL: What Story Is Your Parcel Data Telling?
Travis Rhoades, our Director of Data Science, is authoring a series of guest columns on Big Data for PARCEL magazine. This column is the second in the series of three and asks the question, how many of us know the story our parcel data is telling? You can read the full column below or on the PARCEL web site. In the final column, Travis will share big data pitfalls to avoid.
Last month I shared some basics of big data and how it can help parcel shippers. My column explored the break-neck pace at which data continues to grow, as well as the benefits and competitive edge big data affords.
It made me wonder: How many of us have truly assessed the potential value of the data in our parcel spend? How many of us know the story our parcel data is telling?
From invoices to transit data and tracking information, these numbers tell a data story. At first glance, it may simply look like a pile of paper and a never-ending string of numbers. However, when you know what to look for – and how to look – you can find powerful knowledge and actionable business intelligence to help your business generate more revenue, minimize waste and potentially identify new revenue streams.
Data scientists play a crucial role in big data. They extract knowledge so you can better understand the current situation, know what to expect in the future, and recommend appropriate courses of action. This knowledge lets businesses develop robust and well-tuned logistics strategies. Let’s take a look at how data scientists do it.
Data scientists start with descriptive analytics, or by asking: What happened? Descriptive analytics provide a statistical narrative of historical data. The information tells us where we are and how we got there. Descriptive analytics helps businesses get to the root of issues. The data often reveals the operational health of systems, as well as any wasteful practices or duplication.
What’s About to Happen?
Data scientists use predictive analytics to describe what’s likely to occur based on the data story. Predictive analytics build on descriptive analytics to reveal what may happen and why, as well as the likelihood of it occurring.
Predictive analytics can answer questions like:
- If the logistics strategy stays the same, what will happen?
- What are the likely shipping costs of a sales promotion in the Northeast?
- If we switch to ground from two-day air, how much can we save? What’s the likely impact to customer experience?
Collecting and studying data has little benefit if you don’t use it. Unfortunately, though, that’s the route many businesses take. Today, less than five percent of all data collected is actually analyzed for business intelligence.
The impact that analysis has on outcomes determines its value. Business decisions made with a clear understanding of the current operational environment – descriptive analytics – as well as a clear expectation of the future state – predictive analytics – lead to better outcomes. This takes us to prescriptive analytics – the final element data scientists apply to help businesses fine-tune their logistics strategies.
What Steps Do We Take?
Prescriptive analytics combine information about the decision being made with predictiveanalytics. It recommends actions to optimize expected outcomes. All decisions have some degree of uncertainty; prescriptive analytics address the uncertainty by identifying paths that mitigate risk or seize opportunities.
Think of prescriptive analytics as the steps parcel shippers employ — as a result of descriptive and predictive analytics — to get desired pay offs such as cost-savings, less waste and new revenue streams.
A good example of this is eSigns, an online customer signage printer and retailer. With descriptive analytics, the business gained a thorough understanding of its shipping profile and identified elements of its parcel operations that increased costs and affected customer experience. These insights, combined with the predictive power of benchmarking and simulation models, enabled eSigns to make strategic business intelligence decisions that targeted parts of their carrier agreements. In doing so, they optimized their savings — to the tune of more than $375,000.
Big data gives parcel shippers access to information like never before, but its true benefit is in empowering shippers to make better decisions about their parcel spend. Maximize your parcel network efficiency and effectiveness by making big data – and data scientists – part of your logistics strategy.
Have you assessed the data in your parcel spend? What story is your data telling?