Mark P. Dangelo: Playing Chicken with the Data Freight Train

The “mastery” of data is not about how much you capture, how large your data warehouses or lakes become, or the native cloud provisioning solutions you deploy—it is about creating, sustaining, and utilizing a data supply chain already being deployed by non-traditional lenders.  To think otherwise is akin to “playing chicken” with a freight train—hoping somehow it will veer from its tracks and spare you.

Mark P. Dangelo: Playing Chicken with the Data Freight Train

The “mastery” of data is not about how much you capture, how large your data warehouses or lakes become, or the native cloud provisioning solutions you deploy—it is about creating, sustaining, and utilizing a data supply chain already being deployed by non-traditional lenders.  To think otherwise is akin to “playing chicken” with a freight train—hoping somehow it will veer from its tracks and spare you.

Mark P. Dangelo: Playing Chicken with the Data Freight Train

The “mastery” of data is not about how much you capture, how large your data warehouses or lakes become, or the native cloud provisioning solutions you deploy—it is about creating, sustaining, and utilizing a data supply chain already being deployed by non-traditional lenders.  To think otherwise is akin to “playing chicken” with a freight train—hoping somehow it will veer from its tracks and spare you.

The Death of Data: Why is BFSI’s Data Increasingly “Obsolete?”

The use cases for data inclusion and storage have centered around “data is an asset.” However, will traditional system approaches (e.g., SaaS) create the data robustness necessary for future operations, decision making, and predictions? Looking forward to leveraging AI-enabled solutions, our traditional data is quickly becoming obsolete as innovations transcend our current implementation methods.

The Death of Data: Why is BFSI’s Data Increasingly “Obsolete?”

The use cases for data inclusion and storage have centered around “data is an asset.” However, will traditional system approaches (e.g., SaaS) create the data robustness necessary for future operations, decision making, and predictions? Looking forward to leveraging AI-enabled solutions, our traditional data is quickly becoming obsolete as innovations transcend our current implementation methods.

Mark P. Dangelo: In an AI Reimagined Financial World, It Begins with the Consumer (Part 3)

Sustainable customer-centric AI for BFSI industry operations requires not just quality data and system interoperability—it demands shifts in mindsets. AI’s implementation must be preceded by a quantum shift of design thinking lead by altered strategy approaches, which move beyond FinTech and RegTech traditional solutions.

Mark P. Dangelo: In an AI Reimagined Financial World, It Begins with the Consumer (Part 2)

What happens when those breathtaking artificial intelligence (#AI) solutions are wrong? Moreover, when AI systems “talk” or cascade to other downstream AI systems, how can erroneous information in across data-driven, data-as-a-product solutions be recalled without influencing or cascading into blackholes of consumer chaos, frustration, and account loss?

Mark P. Dangelo: The Dark Matter Transforming M&A Post-Deal Landscapes, Part 3

Data is the “dark matter” of M&A events. Data is made even more important with widespread digital transformations of processes, predictive analytics, and advanced machine learning. During an M&A event, the cascading challenges to leverage these siloed data innovations across Industry 4.0 ecosystems requires a framework that moves beyond the prescriptive, one-size-fits-all strategy.