If you keep up with news related to big data, you’ve probably already heard about AI penning a horror movie, and how Amazon is reinventing the concept of e-commerce with its data-generated product recommendations. You probably already know that there are new IT security solutions based on big data, that use historical trends to identify network and system intrusions. Big data is making an impact in manufacturing, healthcare, marketing, finance, transportation, and education. But aside from the headline-generating buzz, what are some of the most practical ways that organizations are putting big data to good use? Here are some excellent examples.
1. Stocking Retail Stores Ahead of Big Events
If you run a retail store, you’d probably guess (correctly) that before certain events you need to stock up on certain supplies. For example, ahead of the Super Bowl, you’ll need to stock up on lots of nacho fixin’s, paper products, and garbage bags. Before a hurricane hits, you’d want plenty of flashlights, batteries, and … strawberry Pop Tarts? That’s what one major retailer found after analyzing the sales data following numerous hurricanes. Big data can help retailers identify those unexpected and undetected trends so that they can have what customers need when they need it.
2. Finding the Ideal Price Points
If products are priced too low, the company works in too narrow a profit margin. Plus, customers might undervalue the product, because the price is so low. Conversely, over-pricing products simply means fewer sales. Big data is used to determine the ideal pricing point for various products, so that companies can maximize profits while keeping sales strong, even when the economy is tight.
3. Identifying Stocking Errors
The points we’ve already discussed usually require historical data, or perhaps a combination of historical and real-time streaming data. But having real-time data from POS is incredibly valuable. For example, one company used real-time monitoring of their POS and noticed that right before Halloween, a particular candy wasn’t selling at all in a couple of stores. Within moments, they had contacted the store managers, and it turns out that the candy had simply not been put on the shelves. It was a simple, easily corrected stocking error, that without big data could have hurt the store’s overall sales for the holiday. If unfound, it could have led to under-ordering of that particular candy next year, potentially damaging the relationship of the retailer and vendor.
4. Improving Product Design
Rolls Royce is well-known for their powerful and durable automotive engines, but they also design and manufacture high-quality airplane propulsion systems. When they are designing a new engine or propulsion system, they run their design through numerous big data analytics to identify any unforeseen or unexpected problems with the design. The end result is lower costs of testing and production, without sacrificing anything in terms of quality, because they are able to avoid potential errors and problems in the design process.
5. Supporting Products After the Sale
After Rolls Royce sells their engines and propulsion systems, they use a complex system of IoT devices to monitor the performance of the product and report back to the engineers. This allows the company to optimize their maintenance routines and scheduling, while staying ahead of potential problems that could damage their valuable customer relationships.
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