The "1-10-100 rule" is a concept in data quality management that suggests the cost of addressing a data quality issue increases as the issue moves through different stages of the data lifecycle.
Here's how the rule is typically defined:
- $1: It costs $1 to verify a data quality issue when the data is first captured at the point of entry.
- $10: If the issue is not caught at the point of entry and makes it into downstream systems, it will cost $10 to cleanse and correct the data.
- $100: If the issue is not addressed at the point of entry or downstream and remains in the system, it will cost $100 to remediate the impact of that bad data on downstream systems, such as lost revenue or damaged reputation.
The idea behind the 1-10-100 rule is to emphasize the importance of catching and addressing data quality issues early in the data lifecycle, as the cost increases dramatically as the issue moves downstream.?
Investing in proactive data quality management at the point of entry can save significant costs associated with data cleansing and remediation downstream.?
The 1-10-100 rule is crucial because it highlights the significant cost implications of poor data quality. When data quality issues are not detected and corrected at the point of entry, they can propagate downstream, causing errors and inaccuracies in downstream systems and impacting business decisions and outcomes.
Here are a few reasons why the 1-10-100 rule is important:
- Cost savings: By investing in proactive data quality management at the point of entry, organizations can save significant costs associated with data cleansing and remediation downstream. Detecting and correcting data quality issues early on can help prevent downstream errors and inaccuracies and ultimately save organizations money.
- Improved decision-making: High-quality data is essential for making informed business decisions. Poor data quality can lead to inaccurate reporting, flawed analysis, and bad decision-making. By prioritizing data quality, organizations can ensure that they base decisions on accurate and reliable data.
- Better customer experience: Poor data quality can impact customer experiences in a number of ways. For example, inaccurate customer information can lead to failed deliveries, incorrect billing, and other issues that negatively affect the customer experience. By improving data quality, organizations can ensure that they provide accurate and consistent customer experiences.
- Regulatory compliance: Many industries are subject to regulatory requirements that mandate high-quality data. By investing in data quality management, organizations can ensure that they meet these requirements and avoid costly fines and penalties.
Overall, the 1-10-100 rule emphasizes prioritizing data quality throughout the data lifecycle. By investing in proactive data quality management at the point of entry, organizations can save costs, improve decision-making, provide better customer experiences, and ensure compliance with regulatory requirements.
Ready to learn more? Contact Raminder Dhiman, [email protected], to learn more about how the Data Quality Engine and to set up a demo.
