57% of survey respondents in a recent report identified data quality as a challenging aspect to data preparation, indicating a growing complexity in ensuring data's fit for business needs.
Organizations often face root causes of data quality issues, including integration problems, capturing inconsistencies, and data duplication, exacerbating the challenges in achieving accurate data.
To address data quality challenges, companies are increasingly adopting approaches like data products and data as a product (DaaP), each with distinct advantages and pitfalls.
Achieving perfect data quality is nearly impossible; however, organizations can identify and focus on their most critical data quality needs to improve usability.
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