Introduction
The supply chain is a massive global network of businesses and people that work together to deliver products and services to consumers. It’s also one of the most profitable industries on Earth, worth trillions in GDP. But this industry can’t function without data storage, processing and analytics (DSPA). Data is essential for coordinating shipments, managing inventory levels at retail outlets, and tracking costs across various points along the way.
Data storage is a challenge.
Data storage is a challenge.
Data is growing exponentially, but storage capacity does not keep up. In fact, it’s growing faster than the ability to process and analyze the data, which in turn is growing faster than the ability to manage it all. This can create an avalanche effect where you end up with too much data that’s too hard-to-manage and difficult-to-analyze–and thus less useful than if it were properly managed at every step along the way.
Processing data is expensive.
The processing of data is expensive. The cost of processing is increasing, driven by the volume and complexity of algorithms being used, as well as by the amount of hardware needed to run them.
Processing data itself can be done in many different ways, but regardless of how it’s done (and there are many), there will always be some cost associated with it. For example:
- If you want to analyze your customers’ behavior on social media platforms like Facebook or Twitter, then you need someone who knows how those platforms work so they can help set up APIs for pulling data from them into your database system (let’s call this person an “API expert”). Then once all this information has been collected into one place where it can be analyzed further by machine learning software such as TensorFlow or Scikit-Learn (two popular open source libraries), these algorithms must then calculate which posts were liked most often based on likes/dislikes ratio over time before finally presenting their findings back at headquarters where human analysts can make sense out everything else going forward.*
Analytics isn’t cheap either.
Analytics isn’t cheap, either. This is a major consideration for businesses looking to implement analytics solutions. In fact, some companies may find that the cost of implementing their chosen solution exceeds their budget–and if you’re not careful with your choices and how much data you collect on an ongoing basis, this could happen to you too.
There are three main areas where costs can add up:
- Hardware – The hardware required to store and process large amounts of data can get expensive very quickly, especially if it needs high-performance computing capabilities such as GPU acceleration or FPGA chipsets (which are typically used for AI applications).
- Software licenses – Software licenses often come at a premium price point compared to open source solutions because they offer greater flexibility and control over how your company conducts its business operations; however, if there’s no benefit lost by using open source software instead then it might be worth considering this route instead!
Data storage, processing and analytics is an expensive but necessary part of the supply chain
The supply chain is a complex beast, and data storage, processing and analytics are all necessary parts of it.
The first step to understanding this is to realize that data isn’t static; it’s constantly changing in response to the environment around it (whether that’s weather or consumer tastes). As such, companies need some way of storing all this information before they can make sense of it–and there are two main ways: either in memory (RAM) or on hard drives. RAM is fast but expensive; hard drives offer more space for less money but take longer for queries because they have slower access times than RAM does. Both methods have pros and cons depending on what kind of query you’re trying to run against your data set–but regardless which one you choose as your primary medium for storing information about your supply chain operations (or any other aspect thereof), there’s no getting around one fact: All this stuff costs money!
Conclusion
Data storage, processing and analytics is an expensive but necessary part of the supply chain.
