‘Big Data’ has become one of the biggest buzzwords lately. Everyone is talking about it and everyone wants it. The problem is, while most companies know that they need it, they don’t really know what it is or what to do with it once they have it. While having access to “big data” is all well and good and easy enough if you’re willing to pay for it, turning it into something usable is a different matter altogether. This is especially true when you’re trying to figure out what you need to run your supply chain more efficiently.
So what is “big data” and how can it be used?
A Little Bit of Big Data
Big data, as the name implies, is just what you’d think it is. A seemingly incomprehensible amount of information, collected from a multitude of different sources, that can all come streaming in faster than most people can comprehend. That creates the biggest issue with big data, the sense of “analysis paralysis” as most companies end up with more information than they know what to do with.
A seemingly incomprehensible amount of information, collected from a multitude of different sources
According to the Harvard Business Review, the key take away from using big data isn’t about having access to all the data available, it’s about knowing what to do with it and accessing the key pieces of data you need to meet your goals.
Boiling it All Down
The first step is to establish your Key Performance Indicators (KPIs). Fine tuning the KPIs makes it easier to drill down to the necessary details while bypassing all the superfluous information that could be flooding your systems.
These are a few questions you should be asking when trying to analyze your company’s processes.
What tools do you utilize to capture your data? When those tools capture the data, what happens with it?
Is Big Data a Good Choice for Every Company?
Absolutely! Having the right systems in place can help your company target weak points and waste and turn the losses to profits. Mostly it’s about having the right data, at the right time, in the right hands. In the case of the manufacturer, big data has helped reduce waste and variability in their production processes and and has dramatically improved product quality. Capitalizing on this data is key for future success.
There are many critical steps to be taken to successfully utilize big data.
A manufacturing company will have to invest many resources into each facet of the business to truly develop efficient processes, manage the supply chain risk and ensure better quality assurance. Big data can distinguish analytics such as “who is likely to buy more product in the United States” instead of asking “who is likely to buy more product in the United States.”
Data Alone is Nothing > Business Intelligence and the 3PL
Data Alone is Nothing, Decisions Driven by Data is Everything!
In order to stay competitive in today’s marketplace the use of big data converted into business intelligence is becoming more of a necessity.
BlueGrace uses the information as a foundation for productive discussions with supply chain partners. Basically, BlueGrace takes the complicated data and makes it easy to read so users can make well calculated decisions data.
Without business intelligence, you waste time, money and opportunity. Make sure you find a partner that is as invested in your business as much as you are.
“The reality is that our relentless focus on the importance of big data is often misleading. Yes, in some situations, deriving value from data requires having an immense amount of that data. But the key for innovators across industries is that the size of the data isn’t the most critical factor — having the right data is.” -Harvard Business Review
BlueGrace Logistics offers complete, customized transportation management solutions that provide clients with the bandwidth to create transparency, operate efficiently, and drive direct cost reductions. For more information on how we can help take your hard to understand and complicated data and turn it into easy to read and well calculated decisions data, feel free to contact us using the form below: