Named one of the hottest trends from the 2015 Gartner Supply Chain Executive Conference, big data has rapidly become synonymous with big revenue. But what does having big data really mean for an organization?

Very little, according to Forbes, until it is utilized in a meaningful way as part of a process that improves business. Reviewing data is one thing, but synthesizing large amounts of data in a meaningful way will create huge new revenue generation opportunities.

As part of our continuing blog series, The Future of Supply Chain Innovation, we’re taking a closer look at big data and its impact on the supply chain. What’s all the buzz about?

Big Data: The Holy Grail of Forecasting

If you’re searching for a universal definition of big data, you won’t find one. Every organization defines big data different, and they use it in unique ways that help to elevate their particular business. Forbes describes big data as “a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.” Gartner describes it as “high volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

The real value of big data is that it broadens the business opportunities of every organization. It allows you to see the things you couldn’t possibly see any other way. It means business decisions are no longer made blind, but with a 360-degree view, providing complete awareness of everything happening around you in real-time. This 360-degree view isn’t just limited to your operations. It touches everything associated with your warehouse and provides a complete upstream and downstream analysis.

This perspective provides complete awareness and amplified intelligence about what’s happening in your industry and the larger global market, including:

  • Consumer Buying Trends and Behavior
  • Manufacturing Results and Trends
  • Shipping Data
  • Inventory Optimization
  • Labor management
  • Purchasing
  • Product Fulfillment

3 Steps to Implementing Big Data in the Supply Chain

Implementing big data in your supply chain is hard work. Organizations that design an advanced analytics process around a clear strategy are the ones that will become industry leaders.

There are 3 key steps to implementing a big data strategy in your supply chain:

  1. Hire the right people: Taking a thoughtful approach to data starts with a dedicated team of experts who can develop the right methods to support your data.
  2. Develop a strategy: Approaching big data in the supply chain requires developing tactics to support a clear, consistent strategy. Making sense of a high volume of data can be complex, and setting strategic goals that inform daily decisions across the entire operation is critical.
  3. Develop a data pipeline: Focus on the source. Identifying, gathering, generating and supporting the large amounts of data in your organization in a way that aligns with your strategy will set you apart from the competition.

Supply Chains and the Big Data Difference

A recent Accenture study found that 97% of surveyed executives understand the clear ROI benefits of big data in their supply chain, yet only 17% have implemented it in at least one function. The reason? Making sense of large amounts of data in an organization takes ample resources, improved skills, and a clear, consistent strategy.

As supply chains become more complex, instituting a process to use advanced analytics will separate organizations from those that don’t. Staying competitive means moving beyond people-based solutions. Soon, organizations will no longer be able to keep up with the volatile nature of their supply chains and adapt on the fly. Their ability to generate, synthesize, and act upon huge volumes of data will become integral to their success.

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