There has been a massive spike in supply chain data in recent times and it has become a huge challenge for enterprises to cope with ever-growing volumes of unstructured and structured data. These bits of data are compiled from a number of sources ranging from ERP systems within the enterprise to the supplier's business, orders and shipment, weblogs for customer shopping patterns logistics, GPS, sensors such as RFID and Electronic On board recorders, mobile devices and social channels among others. Looked at via the traditional tools used to manage supply chain data, we are about to witness spread sheet bedlam.

Which brings us to this question - just how much has supply chain data grown?

A Supply Chain Insights LLC survey[i] released last year found that 8% of respondents had a petabyte of data in a single database while 47% of the companies surveyed said that they expected a petabyte of data in the future. For practically all of them, Big Data was a new term in their vocabulary. But it is a term that is cropping up frequently in a number of supply chain conversations. This is because Big Data in real time can help supply chains respond to and reach customers in newer ways than before - for instance, it can track shipments by the minute thus providing deep visibility and control thereby helping prevent bottlenecks and optimizes costs. However, the bigger question is: how can Big Data in supply chains help provide a competitive edge?

Organizations that create the infrastructure to capture, process, analyze and distribute the data across their supply chains will be able to adjust their capacities and inventories in real time, without missing potential business opportunities. They will be able to optimize processes and create analytical engines that help deliver accurate decisions. Even more alluring for margin-pressed businesses is the fact that Big Data from supply chain actors can help them respond with better pricing. However, given today's complex supply chains spread across the globe, the single biggest lever they can operate is to manage logistics effectively, thereby reducing costs, time-to-market and carbon footprints.

Today's customers are difficult to predict. Forecasting their needs and preferences is risk-laden and businesses are increasingly shifting to demand-driven production models. This means it is more important than ever before to tap data sources in real time to predict market trends. Supply chains that can sense and respond to demand will help businesses integrate accurate and finely-tuned production schedules, procurement plans, staffing, distribution models, pricing structures and marketing and promotion strategy and much more. So, what's preventing everyone from harnessing the power of Big Data to transform their businesses?

Big Data places new demands on data manipulation and intelligence extraction, requiring an enterprise-wide cultural change towards data and analytics. The sheer volume and velocity of data being generated calls for new analytical tools and skill sets that are currently not easily accessible. Besides, Big Data in supply chains cannot be seen in isolation. Ideally, it must be viewed as an enterprise-wide program to build Business Intelligence (BI) in supply chains. Businesses must focus on building clean and consistent data models with excellent data governance structures across functions in order to make an impact on their supply chains.

For those who have been paying attention to the Big Data conversations revolving around supply chains, one thing would have become clear - smart supply chain professionals are hooked to the idea of Big Data as a game changer. They understand that Big Data holds infinite possibilities but also poses significant challenges. Thus, Big Data adoption can create a first-mover advantage, and more importantly leads to long term innovations and benefits when specifically applied to supply chains.

Where do you begin your Big Data journey? I suggest we begin by adopting the following steps:

1. Begin by capturing all operational data - within the enterprise, and across partners and your business eco-system that includes customers. Ensure the data is clean and not altered during its journey across systems.

2. Create a centralized data repository that provides one version of the truth across the enterprise.

3. Ensure that data capture and analysis is done in real time. Data latency does have an impact on the accuracy of decision making.

4. Hire and train specialized data and analytical skills.

5. If you can't achieve #4, ensure you have a reliable technology partner who can.

Do write in your thoughts!