From Lost Opportunities to Lost Revenue: The True Cost of Bad Data

Bad data, including inaccurate, incomplete, or unreliable information, can negatively impact decision-making and business operations. Common types of bad data include incomplete, inaccurate, duplicate, outdated, non-standardized, biased, missing, and inaccessible data. The 1 x 10 x 100 rule suggests that addressing issues early in the process is more cost-effective than later stages. Data engineers spend approximately one week per month addressing data quality issues, leading to direct expenses of $35,000 per engineer and missed opportunities. Bad data can have significant business impacts such as poor decisions, missed sales opportunities, reputation damage, wasted operational costs, regulatory compliance issues, negative customer experience, lack of innovation, and long-term financial impact. Business processes that are impacted by bad data include strategic planning, customer relationship management (CRM), supply chain management, financial reporting, marketing and sales, quality control and manufacturing, compliance and risk management, logistics and transportation, healthcare and life sciences, and energy and utilities. Mitigating the impact and cost of bad data involves using a comprehensive data reliability solution that identifies bad data and data pipeline issues, alerts teams to data incidents, and quickly remedies data problems.