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Data scientists have three requirements for efficiently performing data science and advanced analytics: (1) access to wide-ranging enterprise data, (2) flexible modeling, and (3) easy data preparation.

Presented at FST Future of Financial Services SydneyThe challenges that Crédit Agricole faced in leveraging dataHow Crédit Agricole implemented logical data fabric using data virtualizationRecipes for success, for similar implementations

This report is no longer available, but we invite you to read our blog, 'My Take on the 2024 Gartner® Critical Capabilities for Data Integration Tools Report,' authored by our Senior Director of Brand & Corporate Marketing, for a summary and key insi

How organizations can use new solutions and practices to meet expanding data demands

MDM implementations require substantial investments in not only time and money, but also in hardware, physical space, and resources. Unfortunately, many companies that have already made these investments are unable to unlock the full potential of their customer, product, supplier and other data, once mastered. 

Many supply chain and manufacturing companies are looking for ways to accelerate the provision of data-driven intelligence to their users, to meet business goals. More often than not, however, they struggle to find the optimal mix of technologies for effective data management, which is critical in a wide variety of supply chain use cases.

The BFSI sector has faced a dramatic upsurge of digitalisation initiatives during the pandemic period. Data management is an important aspect to be addressed while an institution undergoes massive digitalisation. Case studies in this talk will discuss how data virtualisation can resolve some of the challenges BFSI sector faces such as proper management of data for complying with regulations, data privacy laws etc.

Large and complex data servicing platforms can be an expensive investment and frankly can take a long time to deploy. But data virtualization is a key to ensure that your business teams can extract immediate value from insights that were previously unattainable. In this session, Patrick Meneses, Chief Data and Analytics Officer of MDI Novare Technologies will discuss how key architectural decisions are critical to ensuring that while you keep an eye on the long road, you can also reap immediate benefits from your investments. The trade offs of traditional data model design techniques versus new virtual business lead data models will keep your organization humming on new innovation.

Security, data privacy, and data protection represent concerns for organizations that must comply with policies and regulations that can vary across regions, data assets, and personas.

Panel by Sathish TK, CTO at IQZ Systems; Aaron Werman, Senior Director of Data and Analytics at Techwave; Paresh Sansare, Cloud Data Practice Lead at CloudEQS; and moderated by Gary Shiller, Director - Partner & Channel Sales at Denodo.