Data Standardization: How It’s Done & Why It’s Important

What Is Data Standardization?

Data standardization converts data into a standard format that computers can read and understand. This is important because it allows different systems to share and efficiently use data. Without data standardization, it would not be effortless for different approaches to communicate and exchange information.

Data standardization is also essential for preserving data quality. When data is standardized, it is much easier to detect errors and ensure that it is accurate. This is essential for making sure that decision-makers have access to accurate and reliable information.

Overall, data standardization is critical to ensuring that data is usable and accessible. Without it, we would be unable to use and manage data effectively.

Why Is Data Standardization Important?

Data standardization is essential because it allows different systems to exchange data consistently. Without standardization, it would be challenging for computers to communicate with each other and exchange data. Standardization also makes it easier to process and analyze data and store it in a database.

With this approach, businesses can make better decisions based on their data. When data is standardized, companies can compare and analyze it more easily to make insights that they can use to improve their operations.

Data standardization has many benefits, but one of the most important is that it helps businesses avoid making decisions based on inaccurate or incomplete data. Data standardization ensures that companies have a complete and accurate picture of their data, allowing them to make better decisions to improve their bottom line.

How to Standardize Data

There are a few different ways to standardize your data, but the most important thing is consistency when it comes to formatting your data. Knowing the following items is essential before opting for any data standardization method.

Determine Your Requirement

Businesses have a lot of data, so it's important to standardize that data to use it effectively. First, look at the types of data you have and how it's currently organized. Is it all in one place? Are there different formats? Is it accurate and up-to-date? Once you understand your current data situation, you can start to identify areas where standardization would be beneficial.

Next, consider your business goals and the decisions you need to make. What kinds of data would you need to make those decisions? Would standardization help you to access and analyze that data more effectively?

Assess Data Entry Points

Several things need to be determined when evaluating data entry points during the data standardization process. To simplify the process, it is helpful first to identify all potential data entry points and evaluate their feasibility.

Some factors to consider when assessing data entry points include:

• The data source: Is the data reliable and accurate?
• The data format: Can the data be easily converted into the desired format?
• The data volume: Is the volume of data manageable?
• The data entry points: Are the data entry points clearly defined and easy to use?

By taking these points in account, you can simplify the data standardization process and ensure that your data is of the highest quality.

Define Data Standards

When handling data, it is crucial to establish standards for how that data is organized and formatted. This ensures that everyone in your organization works with the same assumptions and that data can be easily shared between different departments and systems.

Data standards are rules or guidelines that dictate how data should be organized and formatted. By establishing data standards, you can ensure that your data is consistent and easy to work with.

You need to decide what format your data should be in. Data can be formatted as text, numbers, dates, or any other data type. 

Clean Your Data

One of the essential steps in data standardization is to clean your data. This means removing any invalid, incorrect, or duplicate data points. Invalid data does not meet the field in which it is being entered. For example, a phone number field should only contain numbers and perhaps a dash or parentheses. Any other characters in that field would be invalid. Incorrect data does not accurately represent what it is supposed to mean. For example, an area that is supposed to contain a person's last name may instead include their first name. Duplicate data is data that is identical to another data point in the same dataset.

Once you have cleaned your data, you can begin the data standardization process. This means setting consistent rules for how data should be entered and encoded.

Normalize Your Data With a Data Automation Platform

When it comes to data, there are a lot of different formats and types that you might encounter. This can make it challenging to analyze and make sense of all the information. However, there are ways to standardize data to be easier to work with. One method to do this is to use a data automation platform.

A data automation platform can help you to normalize your data so that it is all in the same format. This can make it much easier to work with and analyze. You can also use a data automation platform to standardize data types. This can help you ensure that all of your data is in the same format, making it much easier to work with.

Standardizing Data in Excel

Excel STANDARDIZE is available under Excel Statistical Functions. It returns a normalized value, which is also called Z-score.

The mean and standard deviation are the basis of the z-score. The z-score (or standard score) is a method to standardize scores across the same scale. It divides a score's deviation by the standard deviation in a data set. The resulting score is the standard deviation of a data point from the mean.

Zero is the average of all z-scores for a dataset. A negative z score indicates that the value is lower than the mean. A positive z score indicates that the value is higher than the mean.

 Z-Score Formula = STANDARDIZE(x, mean, standard_dev)

Here: X= data value that you need to normalize.

Mean=  Distribution arithmetic mean 

Standard_dev= Distribution standard deviation.

Normalization vs. Standardization

Data normalization and data standardization are two commonly used methods for dealing with data that cannot easily be analyzed. Both ways are used to transform data into a more uniform and consistent format, but they differ in how they achieve this.

Data normalization typically involves scaling data down to a smaller range of values, such as between 0 and 1. Data standardization, on the other hand, transforms data with a mean of 0 and a standard deviation of 1.

So, which method should you use? Depending on what outcomes you need with it. Data standardization is likely the best option if you need to compare data from different sources. If you want to ensure all your data is in the same format, then data normalization may be the better choice.

FAQ's

1. What is a data standardization example?

Data standardization is converting data into a standard format that computers can easily understand and use. For example, when you standardize data, you might convert all measurements into the metric system or all dates into a single format (such as YYYY-MM-DD).

2. What is the purpose of data standardization?

The primary purpose of data standardization is to improve data quality, reduce costs, and better decision-making. Data standardization can also help to enhance communication and collaboration between different teams and departments.

3. Why is data standardization important in healthcare?

Data standardization is critical in healthcare because it helps improve patient care, increase operational efficiency, and reduce costs. When data is standardized, it is easier to exchange and use, which leads to better decision-making and improved patient outcomes.

4. What are data standardization and normalization?

Data standardization converts data into a standard format. This is often done to improve the compatibility of data between different systems. Data normalization, however, ensures that data is consistent and free of errors. This is usually done by ensuring that information is stored in a standard format and adheres to rules.

Conclusion

Data standardization is the method of organizing data so that it can be easily accessed and used by businesses. This process is essential because it allows companies to make better decisions, improve efficiency, and save money. There are a few different ways to standardize data; the most effective method will vary depending on the type and amount of data. However, all businesses can benefit from data standardization, which is a crucial part of data management. Additionally, enroll in our Big Data PG Program if you want to upskill yourself in this Data Revolutionary era.