Executives often harbor skepticism toward organizational data. Understanding the financial impact of bad data is a crucial first step in earning their trust.
Why Executives Distrust Their Data
The value of enterprise data is determined by a variety of factors, including accuracy, clarity, and community input. Any deficiencies in these areas can turn valuable data into a liability. Inaccurate data can distort summaries or bias models, leading to poor decisions, missed opportunities, damaged reputations, customer dissatisfaction, and increased risks and expenses.
Such errors can have a significant impact on business decisions and, ultimately, the bottom line. As data volumes and sources grow, managing quality becomes increasingly vital. Unfortunately, data errors are common, leading to widespread mistrust. According to a Harvard Business Review study, only 16% of managers fully trust their data.
A study by New Vantage Partners highlights more reasons for executive concern, especially among those leading data-driven transformations. It identifies cultural resistance, lack of organizational alignment, and agility as major barriers to adopting new data management technologies. Notably, 95% of surveyed executives cited cultural challenges, stemming from people and processes, as the main hurdles. There is a clear need for tools that can be easily adopted to improve data management processes.
The Cost of Poor Data
Despite low trust in data quality, executives acknowledge its importance. Organizations are beginning to understand the high costs associated with poor data quality. Experian Plc. found that bad data costs companies 23% of revenue globally. IBM estimates the total cost of poor data quality to the U.S. economy at $3.1 trillion per year.
These costs primarily arise from initial errors that trigger costly reactionary responses. According to 451 Research, 44.5% of respondents manage data quality by identifying errors through reports and then taking corrective action. Another 37.5% rely on manual data cleansing processes.
Highly skilled data analysts spend valuable time manually fixing errors. Syncsort reports that 38% of data-driven analysts spend over 30% of their time on data remediation. Similarly, an MIT study found that knowledge workers waste up to 50% of their time on mundane quality issues, and for data scientists, this figure can reach 80%. This time could be better spent uncovering insights, solving complex business challenges, or generating revenue.
