AI/ML
Migrating from Azure Data and AI Stack to Microsoft Fabric: A practical overview
As the landscape of data platforms continues to evolve, Microsoft has introduced Microsoft Fabric as a comprehensive solution for data integration, analytics,…
Maximizing Project Success through JIRA Data Upcycling
JIRA has long been a staple in the world of project management, particularly for software development teams. Its robust features provide excellent…
Cloud data modernization for business growth: Insights from industry leaders
Srinath Reddy B Head of Data Platforms & Engineering at Dun & Bradstreet Mayur Rustagi CTO and Co-Founder at Sigmoid According…
Why banks must embrace proactive risk management with AI-driven RegTech
Large firms spend up to $10,000 per employee annually on compliance, averaging around $300 million annually.¹ This significant investment could be redirected…
How to elevate digital banking experiences for customers with data analytics
From data silos and security concerns to poor data quality and ineffective segmentation, retail banks today face a myriad of challenges in…
3 data and analytics trends shaping the MedTech industry
In the MedTech industry, one of the key challenges lies in balancing cutting-edge advancements with the practical realities of cost, regulation, and…
Driving regulatory success in capital markets with data governance
The ever-evolving regulatory landscape of capital markets feels like a tightrope walk. One misstep with data management, and brand reputation along with…
How data-driven strategies can strengthen risk management in investment banking
For decades, risk management relied heavily on historical data and reactive measures. Loan approvals were based on heuristic records, fraud detection focused…
6 data engineering fundamentals to accelerate generative AI deployment in banking
Generative AI-based models are quickly becoming essential solutions for enhancing operational efficiency, improving customer experiences, and mitigating risks in the banking and…