What Is Data Standardization and Why Do Vietnamese Businesses Often Overlook This Step?

In many businesses and organizations today, the volume of stored data continues to grow: customer records, contracts, financial reports, operational data, internal Excel files, and more. However, when it comes to compiling reports or integrating new systems, many organizations encounter issues such as inconsistent figures, duplicate information, or time-consuming manual processing.

Data exists, but it cannot be effectively utilized. Systems are in place, yet operations are not fully optimized. The root cause often lies not in the software itself, but in the quality of the input data. This is when businesses need to revisit a fundamental step: data standardization. So what exactly is data standardization, and why do many Vietnamese businesses still overlook this important step?

What Is Data Standardization? Understanding It Correctly to Implement It Effectively

Data standardization is the process of cleaning data, unifying formats, removing duplicates, and ensuring consistent data structures before the data is entered into management systems or used for analysis and reporting. In simple terms, data standardization ensures that all information within a system follows the same “language” and a shared standard.

Without standardization, even small differences in how data is recorded can lead to discrepancies when compiling reports, analyzing data, or integrating multiple systems.

It is important to clearly distinguish that data standardization is not the same as overall data governance. While data governance focuses on policies, roles, and control mechanisms, data standardization is a technical step aimed at improving the quality of input data. This step forms an essential foundation before implementing ERP, CRM, DMS, or automation solutions.

Why Data Standardization Determines the Success of Digital Transformation?

During digital transformation initiatives, many organizations invest in modern technology systems but fail to properly process their existing data. As a result, a common situation occurs: new systems are deployed, but the outcomes fall short of expectations.

A fundamental principle in data management is that the quality of input data determines the quality of output results. If the data is inaccurate or inconsistent, reports and analyses will not accurately reflect operational realities.

When data is standardized, organizations can:

• Reduce errors in reports and statistics
• Shorten the time required for record processing and data entry
• Improve integration between different systems
• Build a foundation for data analytics, AI, and process automation

In particular, solutions such as RPA, data analytics, or AI applications can only achieve their full potential when data has already been cleaned and standardized. Otherwise, automation processes may amplify existing errors rather than improve efficiency.

Therefore, data standardization is not merely a minor technical task, but a prerequisite for ensuring that digital transformation is both sustainable and effective.

Why Do Vietnamese Businesses Often Overlook Data Standardization?

Although the importance of data standardization is increasingly recognized, many Vietnamese businesses still do not prioritize this step in their digital transformation journey.

One common reason is the tendency to focus on technology first and address data later. When implementing new software systems, businesses often prioritize deployment speed and system functionality, while existing data is transferred “as is” into the new environment without proper cleaning or standardization.

Another challenge is the sheer volume of historical data. Many organizations have accumulated years of records in the form of paper documents, scanned files, and scattered spreadsheets. Reviewing, cleaning, and standardizing all this data requires time, resources, and a structured process—something not every organization is ready to invest in from the outset.

In addition, many businesses lack common standards for data formats and have not clearly defined who is responsible for data management. When each department enters data according to its own methods, inconsistencies gradually accumulate and eventually become a major operational bottleneck.

More importantly, data standardization is often viewed as a supporting technical task rather than a strategic priority. As a result, this foundational step is frequently overlooked, despite its significant impact on the success of digital transformation.

Where Should Businesses Start with Data Standardization?

To implement data standardization effectively, businesses do not necessarily need to standardize all systems at once. What matters most is having a clear roadmap and focusing on the right priorities.

The first step is to assess the current state of the data: where the data is stored, in what formats it exists, and how accurate it is. This review helps determine the scope and priorities of the standardization process.

Next, businesses should identify the most critical data groups, typically including customer data, record data, or core operational data. These datasets have a direct impact on reporting and management decisions.

After that, organizations need to establish common standards for data formats and structures across the entire organization. These standards ensure that newly generated data follows the same rules, preventing inconsistencies in the future.

Finally, the process of cleaning and standardizing data should be carried out systematically, combining quality control procedures with appropriate supporting tools. Data standardization is not a one-time task but a continuous process aimed at maintaining stable data quality over time.

When data is properly cleaned and standardized, businesses not only reduce errors and optimize operational costs but also build a strong foundation for ERP, CRM, DMS, data analytics, and automation solutions. Conversely, if data remains inconsistent and poorly controlled, technology investments may fail to deliver the expected results.

👉 If your organization is struggling with fragmented, duplicated, or inconsistent data, now is the right time to review and rebuild your data foundation.

👉 Contact BPO.MP for consultation on data cleansing, standardization, and processing solutions tailored to your organization’s needs.

BPO.MP works alongside businesses and public sector organizations to build standardized data foundations—ready for sustainable digital transformation and long-term operational efficiency.