Why standardization is needed

Check out more of his content on Data Science topics on Medium. When and Why to Standardize Your Data? A simple guide on when it is necessary to standardize your data. Zakaria Jaadi. September 4, Updated: April 7, COM Standardization is an important technique that is mostly performed as a pre-processing step before many Machine Learning models, to standardize the range of features of input data set.

Standardization Standardization comes into picture when features of input data set have large differences between their ranges, or simply when they are measured in different measurement units e. How to standardize data? Z-score Z-score is one of the most popular methods to standardize data, and can be done by subtracting the mean and dividing by the standard deviation for each value of each feature.

There exist other standardization methods but for the sake of simplicity, in this story i settle for Z-score method. When to standardize data and why? So before which ML models and methods you have to standardize your data and why? Frankly, standardization is often the only option given the limits of a universal technology standard. Standardization lag: It takes time to understand a problem domain well enough to make a proper standard. Understanding usually requires experimentation, and thus cutting edge technology usually involves lots of entities mostly corporate competing with each other to do the best job of applying new technology to customer needs.

This competition is essential to properly understanding the problem domain, meaning a proper standard can lag the first appearance of a technology by several years. Likewise, even with understanding, the creation of a standard takes time much as chefs working together on a lasagna takes time. Put smart programmers in a room, and odds are they will give you different ways of doing the same thing. A resolution requires debate, analysis, and compromise, and such things take time.

Customers are faced with a choice. They can opt to forego altogether use of a new technology until a proper standard is developed. That, however, is a catch, as how is anyone going to know what works if people are waiting until that mythical standard gets snared in a research trap. The result is often that standardization sets in so as to create a "de facto" standard centered around one company's implementation of a new technology.

This is why, in most cases, a new standard enters a market where one company has managed to gain a majority market share in technology covered by the new standard. Standards are incomplete: As noted, it takes years before a problem domain is sufficiently well understood as to be a base upon which a standard can be built.

Technology, however, does not stand still as humans fumble for understanding. This means that even if some aspect of a technology has been codified in a shared standard, newer aspects won't be, creating the same impulse to standardize on a de facto implementation as exists in item 1.

Furthermore, there is simply too much to standardize. Ignoring for the moment the churn created by new technology, imagine trying to standardize every interface in an operating system, every API in a software application, every document format, and every network protocol. How would we even gather enough information to manage that? Likewise, what if there is disagreement? Hubble-like Visibility — a standardized system gives you visibility across sites to make the kind of comparisons you otherwise would not be able to make.

The Hubble space telescope could see unimaginably far into space. With standardization across your enterprise, you can see across that enterprise. Cloud Power — a standardized system in the cloud is much less costly. No servers to stand up, maintain, or protect; a single, unified data repository which alone is worth the work of standardization ; and easy access for users from all types of devices.

Why is standardization important? It's essential in any system, and is especially important to an enterprise platform like InfinityQS software. And the term has a few meanings which I alluded to earlier , depending upon your focus: Naming conventions—making sure parts, features, processes, and more are named the same in every department or shift.

Standardizing your naming conventions saves a lot of troubles down the road. This saves time and eliminates confusion. Standardizing software products makes sense for many reasons, not the least of which is collaboration: since everyone has the same tools and capabilities, teams can freely share information. When shop floor operators learn something new about the software, they can share it with others. Standardization makes it easier to rotate staff because they have a clear blueprint guiding them and allowing them to pick up other tasks more easily.

Is everyone performing a certain task in exactly the same way? If so, then output and ultimately your end product will be of a consistently high quality. In effect, this establishes an internal system of quality standards that can help set you apart from your competition. Most manufacturers have to comply with international, national, or sector-specific standards, for example ISO In such a case, standardization acts as a control mechanism to help you comply with all the rules, regulations, and requirements.

When everyone in your organization is performing a task in the same way, it then becomes easier to spot any bottlenecks or sources of waste. Why not make a start with one of our Excel skills matrix templates or take a more professional approach with a look at our special-purpose skills management software?

Jochem is a business development manager at AG5.