Executive IT Corner
with Biju Kewalram
For the last four or five years, the term “big data,” (closely related to “data analytics”) has become a key trend in using systems to gain competitive advantage. At its core, this subject revolves around the use of massive databases to derive patterns that can add commercial value.
The concept itself is not new. Big data has its roots in database technology that has been around for a long time. However, a number of trends have come together in recent years to popularize the concept:
- As the cost of data storage has gotten cheaper, companies’ IT departments have stopped worrying about data archiving, choosing instead to make more data available online for querying. Companies find themselves sitting on terabytes of information collected over many years.
- The rate of data growth has grown exponentially with the advent of digital data gathering. For example, technologies like RFID allow the capture of many more data points for each SKU moving in a logistics network. The use of mobile devices that are capable of data capture means that more locations, even far-flung ones, are able to feed data points for products moving through the logistics network.
- C-level executives in logistics companies have become more data savvy as they become aware of the potential that these massive databases can provide for analysis of everything from logistics network design to altering supply chains so they can respond to rapidly changing consumer behavior.
While the term itself correctly refers to the underlying resource of large quantities of data, the use of data analytics and big data results in extremely targeted solutions. For example, President Obama’s 2012 election campaign effectively utilized big data approaches to pin-point voter intentions down to each house in an electorate. The campaign’s ability to bring all activity from door-knocking to email targeting to such specific targets resulted in tremendous leverage — and a well-publicized use of big data.
But what does a logistics company need to do in order to take advantage of big data?
- Define a strategy: As the data continues to pour in at the rate of gigabytes a day, companies need a defined strategy and a cross-functional team of operations, technology and commercial staff to define the objectives and tactics required.
- Define technology needs: The key to big data technology is processing capacity for massive databases. These need to be “near-line” databases, continuously updated but storing information offline, accessible in real-time. In recent years open-source databases and technology from vendors of supply chain systems has become widely promoted.
- Find the right skill-set: The past few years have seen the growth in importance of professionals known as “data scientists.” The skills required for this job are a unique combination of knowing how to frame a question, understanding what kind of information is valuable, the ability to look for patterns in large data sets and finally, the technological ability to mine the large data sets. This is a melding of operational, commercial and technical skills that are essential to the success of big data usage.
Big data has significant pay-off in the logistics sector. Each element of the supply chain is able to play not just a contributory part by making available its data set to a larger pool, but each player also has the ability to leverage the application of the technology to maximize its own competitive advantage.
Let’s break the potential down by sector:
- Traditional retail: Let’s say a large supermarket chain wants to figure out how to specifically target a particular demographic niche — say, families with male children between the ages of 4 and 14. By gathering information from multiple sources (credit card information, days of the week that the customer visited the store, inventory purchased and other related items) it is easy to see how extremely tailored profiling of spending habits is possible. Building automated routines to offer massive customization of offerings is a very simple progression and a matter of executing a big data strategy.
- Online retail: The practices of traditional retail in utilizing big data only become more practical in the online retail world. From tailoring customized offerings “on the fly” to logged-in shoppers — to small parcel inventory orders as a consequence of each order, mining big data is even more relevant for this segment. The opportunities are only limited by the imagination — it is not difficult to imagine a scenario where a consumer’s YouTube visit to a company’s video channel results in many small tweaks to a company’s constantly changing inventory forecast.
- 3PLs and logistics companies: As intermediaries and service providers in the supply chain, 3PLs and other logistics providers are uniquely placed to add value to retailers and wholesalers. Since the handling of freight is accompanied by so much data (I have seen estimates of up to 200 pieces of data accompanying one shipment), 3PLs that handle inventory for retailers and wholesalers are uniquely placed to use the information for their own purposes, and to combine that information in conjunction with a customer’s data to provide extended value. It is not difficult for me to foresee a day when 3PLs, forwarders and consolidators become data merchants.
Big data concepts and technologies can be used by logistics companies to enhance planning from sales forecasting to capacity management. These service providers have an unparalleled position to provide additional value to their customers as managers of big data and even as big data solutions providers. At its core, big data is about mining large databases for pattern recognition — and companies that see how to do that will capture additional value for themselves as this sector continues to evolve.
Kewalram has spent decades developing freight forwarding and NVO information technology, and now provides systems consulting and training to logistics services providers. He can be reached by email.
This column was published in the June 2014 issue of American Shipper.