Good data quality is the foundation of a stable business, of a well-run project, of almost any initiative that is to be successfully implemented. Poor data can lead to significant business consequences for organizations. Faulty data is often cited as the cause of operational breakdowns, flawed processes, inaccurate analysis, and poorly thought-out business strategies. Examples of the economic damage caused by data quality issues range from additional costs when products are shipped to the wrong customer addresses, lost production, lost sales opportunities due to incorrect or incomplete customer records, and fines for improper financial reporting or regulatory compliance.
There are many examples that seem funny at first glance. Like bridges that don’t meet in the middle due to faulty data, property taxes on ramshackle buildings that are calculated wrong by a factor of 1000 or Mars missions that fail because different measurement systems are used. But at the end of the day, all these mistakes cause immense costs, lead to project failure or create subsequent problems that are hardly manageable.
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