Artificial intelligence (AI) is transforming industries by automating processes, enabling smarter decisions, and unlocking new avenues for innovation. According to recent Semarchy research, 74% of businesses are investing in AI this year to boost performance and efficiency. Although AI's effectiveness relies heavily on quality data, 98% of organizations report that poor data quality undermines their AI initiatives. The result? Inaccurate AI models, biased outcomes, and operational challenges. Put simply, AI's inherent speed, agility, and precision can become liabilities if it's fed flawed data.
Posted October 09, 2025
AI adoption is rapidly accelerating, but data quality and data governance remain two of the biggest challenges to successful implementation. While AI depends on vast amounts of data, the quality and accuracy of the information it processes directly determine its effectiveness. Strong data governance allows organizations to mitigate various threats, while poor data quality leads to inaccurate insights and inefficiencies.
Posted August 14, 2025
DataOps, an adaptation of what's traditionally known as DevOps, has evolved into an essential component of modern business operations. DataOps applies the concepts that have fostered more agility and value creation in software development to the data ecosystem. This adaptation enables organizations to bring the same efficiency and responsiveness to their data operations that DevOps brought to software delivery.
Posted June 02, 2025