From Discovery to Action

Last month, we explored the power of data discovery, emphasizing the importance of visibility in understanding the current state of your data. However, discovering your data is only the first step. Once you have identified the issues, the real challenge begins—fixing them. Poor data quality slows operations, leads to inaccurate decision-making, and creates inefficiencies across an organization. The old adage that I hardly ever hear anymore is still true, “garbage in, garbage out.” In order to avoid the garbage out, you must rise to the challenge and fix your data. The good news is that many of these issues can be resolved quickly and at scale. This month, we will focus on how to clean and fix your data efficiently, ensuring that it becomes a valuable asset rather than garbage.

Identifying Data Issues

Before you can fix your data, you must first understand where the problems lie. Without proper visibility, errors remain hidden, causing inefficiencies and inaccurate reporting. Use this checklist to identify common data issues:

• Inconsistent Formatting – Variations in state names, dates, or other key fields (e.g., "Texas" vs. "TX").
• Missing Data – Blank fields in CRMs, ERPs, or databases.
• Duplicates – Multiple records for the same customer, vendor, or product.
• Conflicting Entries – Contradictory information across different systems.
• Data Entry Errors – Incorrect or inconsistent inputs due to lack of standardization.
• Outdated Records – Stale data that hasn’t been updated in years.

The first step is to recognize these issues. Once you have identified them, you can take targeted action to clean and standardize your data, ensuring your organization has a reliable foundation for decision-making.

Applying Quick Fixes at Scale

Once the problems are identified, the next step is implementing scalable solutions. Many data quality issues can be resolved in bulk during the transformation process in your data warehouse. A single SQL script fixes millions of records in seconds. This approach eliminates the need for manual corrections and ensures uniformity across the dataset. However, not all problems can be solved through a simple query, and more importantly, many problems need to be fixed at the source system before they arrive in the data warehouse.

Addressing Source System Issues

Most data problems start at the source—CRMs, ERPs, and other operational systems. If sales reps leave fields blank or enter inconsistent product descriptions, those errors flow straight into the data warehouse. Fixing them downstream is a temporary band-aid, not a solution. The only real fix is at the source: mandatory fields, validation rules, and user training. Cleaning data at the source eliminates recurring issues, improves reporting, and strengthens overall data integrity.

Sometimes, businesses need immediate fixes. A quick SQL script in the data warehouse can temporarily clean incomplete records, but without addressing the root cause, the same errors will keep appearing. Companies that rely on constant patching instead of fixing data at its origin create unnecessary complexity and waste resources. The best approach is proactive: clean existing data while ensuring new data is entered correctly from the start. Anything else just delays the problem.

Taking the Next Step Toward Data Quality

Organizations that take data quality seriously will see the impact across every aspect of their business. Trusting your data warehouse used for reporting, forecasting, and strategic decisions is critical to long-term success. If your organization has not yet begun the process of fixing its data, now is the time to start. Begin by looking at how to fix the data within your warehouse during the data transformation process using basic queries to identify common inconsistencies.

March is the perfect time to take control of your data and ensure it works for you, not against you. Next month, we will focus on becoming a data-driven leader and how organizations can leverage clean, accurate data to drive their business forward.

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