Big Data is, well, big... wait, I mean to state is it's huge.

So huge in fact that Cisco has recently coined a new term for the growth of data: “The Zettabyte Era,” where a zettabyte equals 1 trillion gigabytes.

That being stated, let's take a step back.

The past few years have seen exponential growth in the volume of unstructured data that is available for analysis. This includes steaming videos, e-mail, audio files, visual presentations and any other type of data that is resistant to easy integration within databases. Turning such massive amounts of data into useable information and knowledge presents many challenges. However, there are some key steps you can take to facilitate your mastery of Big Data.

Inference

Not all data is useful data. It is up to you to determine the relevance of the Big Data you encounter. To help you, there are many advanced analytic tools and techniques available to parse through troves of granular data that would otherwise remain opaque. It is important to understand that through these inferences you may reap the benefits of increased process effectiveness and visibility as well consumer preferences, queries and spending habits.

When considering the benefits of implementing a robust data analytics program, actionable knowledge must be derived from algorithms and rule sets, rather than from the data itself. Proper data inference and extraction will need to rely on a focused program to effectively extrapolate information. With a methodical approach, this is not an impossible or overly daunting task.

Find a Chief!

With the massive amount of big data out there waiting to be examined, it is important to empower a Chief Data of Analytics Officer with a strong background in supply chains and analytics. It is crucial that they lead your model analytic centers with clearly defined strategies and initiatives. Within each business unit, data needs to be compiled and examined with an appropriate analytic strategy. Each business unit has its own unique data priorities, which may or may not be applicable to other business units. Additionally, each business unit will have its own goals to accomplish with data metrics and targets that may not be relevant to one another. A C-level executive is imperative in sorting and coordinating a business wide deep data analysis that respects the functions and utilities of individual business units.

Your Supply Chain Matters

Since 1990 the ratio of trade to global GDP has increased over 20%. So, how does this affect your supply chain? It is in every businesses’ best interest to consider global markets when constructing their logistics. Any holes in your logistical data will inevitably impose additional costs upon yourself and others through inefficiency. This problem only intensifies in a global context with the additional costs of international trade. Transportation, storage, administrative costs, customer service and inventory carrying costs are all very expensive, and you cannot afford to be ignorant regarding the nitty-gritty of your upstream and downstream logistical functions. Although the effects on your company can be far reaching, impacting customers, retailers, manufacturers and providers, it is necessary to start with internal metrics of key assets.

Time and Space is Critical

Big Data, by its nature, is pulled from a variety of sources. It must be processed and distributed at specific intervals depending on what is being analyzed in order to arrive at actionable intelligence. Changes must be made in real time to adapt to consumer needs but also over longer intervals of time as trends emerge from disparate data sets. Supply chain management must consider data regarding the logistics of both time and space. Temporal data is anything that is time sensitive in nature. Geospatial data tracks location. For example, a courier will want to know the current location of any packages that need to be delivered, as well as any road blocks of traffic obstructions that may lead inefficiencies in their route. A proper handling of multi-dimensional data will increase the efficiency of your supply chain.

In the end, these strategies are merely a jumping off point to get started. It is almost impossible to effectively build a cost efficient platform to manage Big Data from scratch, especially given the amount of data being arrived at. As your company and its data demands grow, a third party provider becomes essential.  Proper analysis will turn massive amounts nebulous data into actionable intelligence which leads to effective decision making, reduced logistical costs and increased profit margins.