Supply chains in today's dynamic environment involve multiple stakeholders and span continents. Not only are they complex, but they're also under pressure to run faster and at lower costs while overcoming all potential disruptions. This is where access to high-quality data and Master Data Management can be a game-changer. While siloed data can create hurdles and slow down a … [Read more...] about Master Data Management: Building Stronger, Resilient Supply Chains
data quality
How Generative AI Amplifies Cyberfraud Risks and Puts Data Integrity on the Line
Generative AI is no longer a novelty. It has become a core driver of innovation across industries, reshaping how organizations create content, deliver customer service, and generate insights. Yet the same technology that fuels progress also presents new vulnerabilities. Cybercriminals are increasingly weaponizing generative AI, while organizations face mounting challenges in … [Read more...] about How Generative AI Amplifies Cyberfraud Risks and Puts Data Integrity on the Line
AI’s Achilles’ Heel: The Data Quality Dilemma
As AI has gained prominence, all the data quality issues we've faced historically are still relevant. However, there are additional complexities faced when dealing with the nontraditional data that AI often makes use of. AI Data Has Different Quality Needs When AI makes use of traditional structured data, all the same data cleansing processes and protocols that have been … [Read more...] about AI’s Achilles’ Heel: The Data Quality Dilemma
Why Data Quality Is the Keystone of Generative AI
As organizations race to adopt generative AI tools-from AI writing assistants to autonomous coding platforms-one often-overlooked variable makes the difference between game-changing innovation and disastrous missteps: data quality. Generative AI doesn't generate insights from thin air. It consumes data, learns from it, and produces outcomes that reflect the quality of what … [Read more...] about Why Data Quality Is the Keystone of Generative AI
6 Common Mistakes to Avoid When Developing a Data Strategy
In a tech-driven industry, having a solid strategy is essential for success. Organizations that invest in a clear, well-structured approach to data are better equipped to protect sensitive information and unlock the full potential of their machine learning (ML) models.A thoughtful strategy ensures data is accessible and aligned with business goals, which leads to more reliable … [Read more...] about 6 Common Mistakes to Avoid When Developing a Data Strategy
