FIMA Virtual Event March 2021

Interested in demonstrating thought leadership as chairperson? Contact charlie.mcniff@wbresearch.com 

Shanthi Pudota joined Discover Financial Services to drive major transformation as Vice President and Head of Enterprise Data Management and Governance in early 2019. Starting a new leadership role in data is always a challenge, from getting executive buy in, to securing budget to changing culture, these things take time and commitment. Find out how Shanthi is driving these initiatives while continuing to deliver value, including:

·        Establishing an enterprise wide data strategy that can grow organically

·        Leveraging AI and machine learning to deliver value on the back end while re-platforming

·        Communicating these programs in a way that satisfies regulators and gets teams on board

·        Engaging the team and keeping up the momentum while remote

BNY Mellon, a global investment company and one of the oldest financial institutions that prides itself to power the success across the financial world for individuals and institutions through unique insights, thinking and actions, wanted to create an enterprise data platform (EDP). The EDP provides a massive analytics and reporting platform, enabling us to generate actionable insights to improve processes and business performance.

Please join us and listen to this presentation to learn

• What core values, feature and functionalities does the EDP offer
• What constitutes the EDP and the underlying modern infrastructure
• How BNY Mellon uses EDP to unlock the value of data for actionable insights

Risk, resiliency and controls continue to drive every data organization. With investments in metadata collection, organizations have an opportunity to leverage this to better track, understand and support a more resilient environment. Find out how to:

• Engage the organization to utilize and trust metadata
• Leverage use cases where metadata can resolve issues and be used by the entire organization
• Utilize flexible data tagging to look at data from a privacy and security perspective
• Collect metadata to automate activity and control data in the cloud

Interested in demonstrating thought leadership as a keynote presenter? Contact charlie.mcniff@wbresearch.com

Since the morning was jam-packed with detailed presentations and thought-provoking panel discussions, here’s your opportunity to be part of the discussion! Open to financial services participants only, this is your chance to connect and learn from your peers in a moderated interactive discussion. Interested in showcasing your thought leadership as a roundtable discussion host? Contact charlie.mcniff@wbresearch.com  

Topics May Include:

1. Driving Operational Efficiency in Data Management to Reduce Time of Delivery
2. Leveraging Data Virtualization to Increase Efficiency

Historically data quality has generally been a rear view mirror process in financial services. With an increased demand for analytics and the ongoing process of risk assessments, data quality is under significant scrutiny. Find out how organizations are prioritizing data validation up front and establishing single versions of mastered content as opposed to data copies across the organization. Panelists will discuss the latest evolutions in data quality, including:

• Instrumentation metrics and KPIs to understand what the operations team is doing daily
• Self-diagnosing data sets to identify what’s wrong, and using the data itself to fix it
• Emerging quality standards to make data more useful for analytics and regulatory purposes
• Leveraging data quality automation to increase efficiency

In a recent survey of more than 100 C-level executives at global financial services organizations, we learned that most firms are still performing most of their compliance activities manually, and 90% of firms are allocating at least 40% of their total data practice’s budget just to compliance initiatives. To streamline and automate compliance activities, leading firms are now implementing an enterprise data fabric to bring together data from across the enterprise, reducing manual effort, increasing accuracy, lowering latencies, and simplifying operations. An enterprise data fabric is also being used by firms as an enabler for more strategic initiatives, such as enterprise risk and liquidity management, portfolio management, KYC, hyper personalization, and others. In this session we will present a subset of the research findings, describe what top analysts are calling “the future of data management,” and how it is being used to streamline both compliance initiatives and accelerate strategic business initiatives at top financial services firms.

Join this session to find out:

·        The current state of data governance and data management maturity in financial services

·        The top business benefits firms are looking to gain from data management advancements

·        Technologies in use to meet these requirements

As data organizations continue to mature, the focus is shifting to cloud migration and more offensive data management. Data governance has tremendous potential to support and enable the business, including analytics and revenue growth. Find out how you can push data governance to the next level, including how to:

·        Further institutionalize the process of governance to enable digital ambitions

·        Get stewards to become more active when stewardship is not their sole function and timing is limited

·        Set up governance for unstructured data and data in the cloud

·        Leverage new use cases for data governance to support the business

The CDO is helping the business to answer a diverse range of questions. Data consumers have a range of needs, from trading and portfolio management to risk analytics. But how confident are you that you know exactly what the users want and how to get it to them? Do your data users understand the level of self-service they want? Panelists will discuss how they are enabling self-service analytics, including:

• Handling the challenges of data accuracy and self-service
• Knowing the right level of modularity and formats that an average business user can understand
• Understanding the transformations happening at the self-service level
• Educating users on the flexibility they have and standardization
• Providing a frictionless and better usability experience to users

Data management in financial services is highly complex, and the technology that supports it is constantly evolving. Across the industry firms are migrating to the cloud, investing in metadata management, analytics and more. At the same time data privacy and cyber security are under greater scrutiny than ever before. Join us for the FIMA Virtual Demo Series to see concise virtual demos and have the opportunity to ask questions in real time. Hear about the latest solutions to ensure data is fit for purpose and protected, and opportunities to leverage data as a strategic asset. Interested in showcasing your solution in an interactive virtual demo? Contact charlie.mcniff@wbresearch.com

Hear from 4 leading technology providers who will each share a 10 minute virtual demo followed by 5 minutes Q&A. Technology categories may include:

• Data lineage and metadata management
• Data governance
• Data cataloging
• Data Privacy
• Analytics
• Data virtualization

The industry is undergoing significant transformation with the shift to the cloud. Traditionally, data from third parties has been delivered to a data center for computing. However we’re now seeing a significant migration of data provisioning from vendors being available in the cloud, yet systems and processing remain in the data center. How can you bridge the gaps between the cloud and on-prem computing?

Find out:

• The technology solutions necessary to support the data consumption of the future
• Where it makes sense to leverage the cloud
• Robust governance and data lifecycle management strategies for the cloud
• Avoiding data duplication across the lake and the cloud

Interested in demonstrating thought leadership as a keynote presenter? Contact charlie.mcniff@wbresearch.com

As the data organization becomes more enmeshed in machine learning and AI projects, it puts new requirements on data preparation to be served good results. Data organizations have a new opportunity to engage with teams focused on predictive analytics, shifting from the supply to demand side to be more nimble. Find out how to better support predictive analytics, including:

·        Creating and testing hypotheses to produce good analytics

·        Understanding how the data organization fits into the process to create better practices going forward

·        Knowing how people consume and understand data at the end of the consumption pipeline to inform ongoing requirements for data quality and cleansing