Further, large supply chains have particular cost sensitivities—for both the buyer and the seller—when making any changes at all to something as critical as a security mechanism; when enacting something as dramatic as moving all connections to the cloud; and when considering something as tenuous as putting mutual faith in a cloud-service provider that’s able to live up to a service level agreement (SLA).
In another scenario, retail giant Amazon suffered an infrastructure networking problem in 2011 that had ripple effects across the industry, serving as a stark reminder of the importance of being very careful about SLA’s from your cloud provider and their recommendations for an architectural approach. Companies that weathered Amazon's little hiccup painlessly were the ones that had followed recommendations for high SLA requirements, which included distributing the workload across multiple availability zones, even if a single-availability zone would have been adequate 99.9 percent of the time. Those companies knew that even though they were using a cloud infrastructure for which they didn’t have high upfront costs, they also had no control over the availability of that infrastructure. By taking the right architectural approach, they were relatively unscathed when Amazon’s failure occurred.
Your success in analyzing cloud-borne big data in order to glean high-value nuggets requires that you keep this approach firmly in mind. Also keep in mind that availability risk is only one high-profile risk that requires a new mindset when moving to the cloud. The other elephant in the room is data privacy risk, and best practices for managing privacy in the cloud center on encryption technologies—more of a data-centric approach than the network-access-centered approach classically used on-premise. These practices introduce new operational disciplines around key management (key storage, rotation, escrow, etc). While some careful design is needed to avoid the introduction of operational risks, a focus on data protection effectively decouples you from network-access risks at the infrastructure provider.
Once security concerns are handled, your big data is finally cloud-borne, and all your SLA’s are in place—with all the attendant cost savings of going cloud. So what kind of transactional dynamics can your supply chain organization look forward to? What kind of fruit will this big data tree bear?
Benefits within reach
Imagine looking at order flow, transaction delays and performance over time—actually digesting every single bit of data that’s been recorded, not just a sample—and correlating it by time, day or season. Then, imagine making predictions based on mined intelligence about throttling lead times at will, anticipating load spikes, picking new product winners and losers, remediating vendor compliance atrophy and optimizing the supply chain by blending different data streams against each other in a way only big data analytics can. Picture being able to make predictions—including risk management predictions—about the data you collect from your thousands of connections. Visualize being able to manage your supply chain’s community using the vast quantities of data that were always there—but that no one was ever able to make heads or tails of because the technology didn’t exist.
It exists now. And cloud services governed by the right software make it possible. With the right SLA’s and security assurances, they make it not only possible, but prudent—an upset to the on-premise, data-forfeiting paradigm that has existed for so long.
You’ve got big data. You’ve always had it. Your predecessors had it. But now you know how to unlock it and use it effectively. What amazing things will you do with it first?
John Thielens is Chief Security Officer for Phoenix-based Axway, a software company which provides technology solutions that integrate, manage, secure and govern the business-critical interactions that accelerate enterprise performance.