Master Data Science, Master Supply Chain: A 4-Step Guide

Warren Miller

According to IBM, 2.5 quintillion bytes of data are created on a daily basis. Moreover, as much as 90% of the world’s total data has been created in only the past two years.

So it’s no wonder that many of today’s supply chain professionals are struggling to make sense of the abundance of data at their disposal.

But to succeed in this rapidly evolving world, where the generation of data will continue to rise at an exponential rate, your organization must be prepared. For many companies, taking the supply chain to the next level will depend on hiring or developing a knowledgeable data scientist.

The role of data scientists in supply chain

In a 2015 Gyro/FORTUNE Knowledge Group study, 62% of surveyed business leaders said they trust their gut over analytics in the decision-making process. With such a wealth a data at your fingertips, is this really the best way to guide your enterprise into the future?

Data scientists are experts at accessing, analyzing, and making sense of data, whether hidden or unhidden, structured or unstructured. Upon their examination of data, they deliver meaningful insight to the employees who need it most, enabling them to more easily make the decisions that positively impact their organizations.

While this may sound simple enough, data science is incredibly complex. A data scientist must possess industry-specific knowledge, analytical and mathematical skills, and programming expertise.

Plus, given the potential benefits, having a data scientist within your company is becoming increasingly critical. After all, an individual in this role can significantly impact:

  • Planning: Performing analyses to predict market trends and identify supply and demand
  • Design: Refining existing products
  • Development: Helping to create new products
  • Distribution: Forecasting sales and delivering items to suppliers before transactions take place

This list doesn’t even include some of the more obvious advantages, such as mitigating risk or gaining a better understanding of consumers.

4 steps to developing data science expertise

Many of today’s data scientists have advanced degrees. According to a Burtch Works study, 46% of data scientists hold a PhD, while 42% hold a master’s degree. Many of these professionals studied mathematics/statistics, computer science, or engineering.

But neither an advanced degree nor a focus in a particular subject matter are prerequisites for the role of data scientist. In fact, a number of these professionals simply had a strong interest in the growing field and charted their own course for pursuing this career path.

So how could you begin your journey toward becoming a data scientist in the supply chain line of business? Here are four ways:

  1. Explore college programs: Although a degree isn’t required to become a data scientist, you may want to entertain the idea of studying the craft at a university. Many colleges now offer programs devoted to data science – or a related concentration, including business analytics or data mining. While the University of Massachusetts and Worcester Polytechnic Institute were two of the first schools to officially provide students with an education in data science, programs are currently available at such esteemed institutions as Stanford, Georgetown, Notre Dame, Harvard, Cornell, and Columbia.
  1. Take online courses: Rather than enrolling in graduate degree programs, a growing number of people today are electing to take massive open online courses (MOOCs). This alternative, which requires a much lower time commitment and financial investment compared with a full-time college program, consists of traditional course materials, such as lectures, as well as interactive online user communities with participation from both students and professors. While universities do offer MOOCs, these courses can be developed and executed by a diverse set of organizations. Numerous data- and supply chain-related MOOCs are currently open for enrollment, from Driving Business Results with Big Data to Digital Transformation Across the Extended Supply Chain.
  1. Attend relevant conferences: One of the finest ways to gain proficiency in an area is to attend professional or academic conferences. In the fields of data science and supply chain, there are no shortages of opportunities to do this. Within the next few months alone, there are a number of data science events scheduled, including ones in Austin, Texas (Big Data Boot Camp); Chicago (The Data Science Conference); Boston (Re-Work Deep Learning Summit); and London (PyData). Likewise, a variety of must-attend 2016 supply chain conferences will feature thought-provoking sessions on collecting, analyzing, and deriving business value from data.
  1. Seek internal training: More and more, companies are beginning to develop their own data scientists. The thought behind this is that it would be easier to train an existing employee, who already possesses institutional knowledge, than to introduce a new employee to a company’s operations and quickly get that individual up to speed. Global IT leader EMC, for example, recently launched a training and certification program in data science and Big Data analytics. The program is open to both EMC employees and customers. Some individuals have already graduated from the program and are assisting in data science projects.

Reimagine the supply chain with a data scientist

Properly staffing your organization is crucial to overcoming your greatest supply chain challenges. In today’s data-driven world, your success may hinge on employing a knowledgeable data scientist. By following the steps above, you could even become one yourself!

Data science isn’t the only new discipline that you need in your workforce. To ensure your employees have the skills your business needs to grow, learn How to Create a Culture of Continuous Learning.


About Warren Miller

Warren Miller is a senior writer with more than 10 years of experience. He has written extensively on the topics of marketing, customer engagement, commerce, sales, HR, and supply chain.