Data Governance

One of the most common misconceptions when enterprises begin adopting AI is that having the right tools or sufficiently powerful models will automatically solve their problems.

In reality, the opposite is often true. In the majority of failed projects, the root cause does not lie in the algorithms, but in the data—specifically, data quality.

If input data is inaccurate, incomplete, or inconsistent, then all downstream forecasts, optimizations, and analyses will be flawed. More critically, these errors are often not immediately detectable.

When “Bad” Data Compromises the Entire System

Consider several practical scenarios:

A retail company implements demand forecasting to optimize inventory. The system recommends increasing stock for certain products. After a few weeks, inventory rises significantly, but sales do not follow. The root cause: historical sales data contained numerous “ghost orders” due to system errors, which were not cleaned before being fed into the model.

In a bank’s contact center, data on Average Handling Time (AHT) is inaccurately recorded due to non-compliance with logging procedures. When this data is used to optimize workforce scheduling, it leads to incorrect workload forecasts—resulting in understaffing during peak hours and overstaffing during off-peak periods.

In logistics, a warehouse fails to standardize product codes across systems. The same product is assigned multiple identifiers. As a result, inventory analysis cannot recognize them as a single SKU, leading to misguided replenishment decisions.

The common thread across these cases is that the data appears to exist—but is not reliable enough to support decision-making.

Common Pitfalls in Data Quality

The first mistake is assuming that system-stored data is inherently accurate.
Many organizations believe that data stored in ERP, CRM, or sales systems is ready for immediate use. In reality, such data often contains errors: incorrect entries, missing fields, duplicates, or outdated information.

The second mistake is the lack of standardized data definitions.
Different departments use different coding conventions (for SKUs, customers, channels, etc.), making data integration difficult or impossible without manual reconciliation.

The third mistake is the absence of data quality control processes.
Data is generated daily, yet there are no mechanisms to validate, detect errors, or trigger alerts. Errors accumulate over time and are only discovered after causing significant impact.

The fourth mistake is focusing on models rather than the data pipeline.
Organizations invest in AI and dashboards, but neglect data collection, cleansing, and maintenance—resulting in the classic “garbage in, garbage out.”

What Can Businesses Do Immediately to Improve Data Quality?

Instead of deploying complex systems, organizations can start with practical, low-cost steps:

Step 1: Identify Critical Business Data
Not all data holds equal importance. Focus on datasets that directly impact decision-making, such as sales, customer data, inventory, and operational metrics.

Step 2: Establish Simple Data Validation Rules
Basic rules can be implemented quickly:

• Values must not be negative
• Sales should not spike abnormally
• Mandatory fields must not be empty

These can be enforced in tools like Excel, Google Sheets, or existing systems.

Step 3: Standardize Data Structures and Coding
Each product should have a single unique code.
Each customer should have a unique ID.
Standardization enables integration and reliable analysis.

Step 4: Redesign Data Entry Processes
Data quality starts with people.
Simplify input forms, train employees on data importance, and reduce manual entry through automation where possible. For example, use dropdown lists instead of free-text inputs.

Step 5: Implement Error Detection and Resolution Mechanisms
Errors are inevitable—the key is early detection.
Conduct regular data checks, cross-validate across systems, and flag anomalies.

Step 6: Assign Data Ownership Across Departments
Data quality is not solely an IT responsibility. Each department must be accountable for the data they generate.

Step 7: Measure and Continuously Improve
Data quality is not a one-time achievement.
Track metrics such as error rates, missing data ratios, and consistency levels, and improve them over time.

Investing in Data: The Highest-ROI Yet Most Overlooked Investment

In many cases, improving data quality delivers significantly higher returns than investing in new technologies.
A simple model built on clean data often outperforms a complex model trained on flawed data.

For Vietnamese enterprises—where data is often fragmented and lacks standardization—prioritizing data quality may be the fastest path to unlocking real value from digital transformation.

© Copyright belongs to KisStartup. Any reproduction, citation, or reuse must clearly credit KisStartup.