Not all data is created equal. And the more information you have, the more challenging it becomes to standardize and leverage this data for meaningful business outcomes. And so, leaders turn to data standardization for help. In the world of IT operations, data standardization is the process of transforming data into a consistent format, structure, and naming convention. With this in mind, let’s discuss how to work with data standardization solutions, including the benefits and challenges of data standardization and the solutions available in the market.

What is Data Standardization?

Data standardization transforms data into a consistent format, structure, and naming convention. Data standardization involves various activities, including:

• Data Formatting: Data formatting involves ensuring that the data is in the correct format, such as date, time, or numeric format.
• Data Structure: Data structure involves ensuring the data is structured consistently, such as having the same fields and field names.
• Data Naming Conventions: Data naming conventions ensure that the data is named consistently, such as using the same abbreviations or acronyms.

Why is Data Standardization Important?

Data standardization is essential for several reasons:

• Data Analysis: Data standardization makes it easier to analyze data by ensuring it is consistent with a format, structure, and naming convention. This makes it easier to compare and analyze data across different sources.
• Data Management: Data standardization makes it easier to manage data by ensuring consistency and organization. This makes it easier to search and retrieve data when needed.
• Data Quality: Data standardization improves data quality by ensuring the data is accurate and complete. This reduces errors and inconsistencies in the data.
• Data Integration: Data standardization makes integrating data from different sources easier by ensuring that the data is in a consistent format, structure, and naming convention.

Challenges of Data Standardization when working with complex tools

If your tool is already complicated, data standardization can present several challenges, including:

• Complexity: Data standardization can be complex, especially for large data sets or data from various sources.
• Time-Consuming: Data standardization can be time-consuming, requiring significant resources and effort.
• Cost: Data standardization can be costly, requiring specialized software, hardware, and expertise.
• Resistance to Change: Data standardization can face opposition from stakeholders accustomed to their data formats, structures, and naming conventions.

Solutions for Data Standardization

There are various solutions available for data standardization, including:

1. Manual Data Standardization: Manual data standardization involves manually reviewing and transforming data into a consistent format, structure, and naming convention. This can be time-consuming and prone to errors but may be suitable for smaller data sets or data with simple structures.
2. Automated Data Standardization: Automated data standardization involves using software tools to automatically transform data into a consistent format, structure, and naming convention. This can be more efficient and accurate than manual data standardization and is suitable for larger data sets or data with complex structures.
3. Hybrid Data Standardization: Hybrid data standardization involves a combination of manual and automated data standardization. This can be useful for data sets with both simple and complex structures, with automated tools used for large-scale standardization and manual review for more complex data sets.
4. Outsourcing Data Standardization: Outsourcing data standardization involves hiring external experts to perform data standardization. This can be useful for businesses with limited resources or expertise in data standardization.

Data Standardization: Getting Started

It’s time to get out from underneath that big pile of data. For IT Ops, standardization is critical for businesses to manage and analyze data effectively. Data standardization involves transforming data into a consistent format, structure, and naming convention. Data standardization improves data analysis, management, quality, and integration. However, there are still challenges to overcome. As we discussed earlier, resistance to change can be a daunting task to manage.

This is where the power of a demo can really help. The best way to understand data standardization is to visualize it. Schedule a time with our data experts to see what data standardization looks like and how many customers have already seen the benefit.