Businesses often face challenges related to poor-quality data, which can cost them over $5 million each year. Undefined data retention periods lead to increased infrastructure costs and slow database performance. Properly setting Time to Live (TTL) is crucial; it influences how long data is stored. Archiving data efficiently using formats like Apache Avro with compression can mitigate storage costs. However, achieving a balance between data accessibility and storage costs is essential. Furthermore, standardization is critical to avoid the financial drawbacks associated with poor data management practices.
Over a quarter of data and analytics professionals worldwide estimate that poor-quality data costs companies over $5 million annually, with 7% putting the figure at $25 million or more.
If the TTL for active data is much longer than actually needed, the system starts to slow down. Even when querying only the last six months, the database may have to scan excessive volumes of information.
Archival formats like Apache Avro or Protobuf allow data to be stored more compactly. Combined with compression algorithms, they help reduce storage costs.
Poor data quality and lack of standardization can lead to serious financial losses, particularly observed in cases like Unity Software's advertising system.
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