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growth through the market centric data layer
Data and AI

Wachstum dank einer marktorientierten Datenschicht

Führungskräfte im Finanzdienstleistungssektor stehen vor einer gewaltigen Herausforderung: Differenzierung in einer stark regulierten Branche.

Tarife, Gebühren, Produkte und Compliance-Anforderungen schaffen oft eine homogene Landschaft, in der die Wettbewerber miteinander verschmelzen.

In einem so stark regulierten und standardisierten Markt ist es vor allem ein einzigartiges Kundenerlebnis, die einer Organisation einen Wettbewerbsvorteil verschaffen kann. Banken können dies erreichen, indem sie ihre Vertriebskanäle verbessern und ihre Dienstleistungen mithilfe von Technologie und KI nahtlos in den digitalen Alltag ihrer Kunden integrieren. Betrachten wir die Musikindustrie: Der Wechsel vom Walkman zu Streaming-Diensten hat das Produkt über den Musikinhalt hinaus erweitert. Weitere Serviceelemente wie personalisierte Wiedergabelisten, Empfehlungen und Kundensupport, sind inzwischen für das das Nutzererlebnis unerlässlich. Das gewünschte Erlebnis verlagerte sich von einem produktorientierten Modell hin zu einem integrierten, kundenorientierten Modell.

Das Gleiche gilt für Banken, die ihre Dienstleistungen in das digitale Leben ihrer Kunden integrieren wollen. Die Banken müssen ihren Kundenservice neu konzipieren und dabei Daten, Technologie und KI in den Mittelpunkt stellen. Dies wirft eine entscheidende Frage auf: “Welchen Ansatz sollten Banken in Bezug auf Daten und KI für Kundenservicezentren verfolgen, um zukünftig ein produkt- und serviceintegriertes, kundenzentriertes Erlebnis zu bieten?”

Im Interview mit Steve Jones (EVP- Chief Data Architect) und Chandramouli Venkatesan (VP- Portfolio Development Lead, Digital Front Office Transformation) erfahren Sie mehr über das Thema der marktzentrierten Datenschicht und wie sie Ihrem Unternehmen helfen kann, die Kundenzentrierung zu verbessern und eine wahrhaft digital gesteuerte Zukunft anzustreben.

Lesen Sie jetzt das Original-Interview.

How important is the role of real-time data as its fusion with AI is reshaping business strategies and decision-making, guiding industries into the future of operational efficiency and strategic agility?

Steve Jones: In an era where the pace of organizational decisions keeps accelerating, the role of real-time operational data has evolved from supportive to absolutely necessary. Traditional, slower-paced data analytics are giving way to a dynamic new environment where data and AI support immediate decision-making and action. In the high-stakes world of finance, algorithmic trading acts as the perfect example where algorithms make split-second decisions based on real-time market data. Their success hinges on the accuracy and completeness of that data. Similarly, crafting exceptional customer experiences requires a data-driven approach that leverages real-time insights to personalize interactions and anticipate customer needs.

A market-centric data layer entails the acknowledgment within the business that decision-making information isn’t solely internal. Embracing this becomes a fundamental competency, ensuring standardized acquisition, universal availability, and transparent governance and accountability. The MCDL serves as a reflection of how the business is perceived in the world, ensuring decisions are made with external objectivity rather than internal subjectivity.

How is real-time data transforming decision-making and AI use in businesses today?

Steve Jones: We’re experiencing a shift in how companies manage and utilize data, with a growing emphasis on the integration of a real-time operational data layer. These systems are not just enhancing the speed and accuracy of decision-making processes but are also a must for the effective deployment of AI in business operations. The differentiation for organizations today is in the ability to react faster than other people operationally to make the right decision faster. That’s the mentality of the operational data pattern. It’s about having all the information to make a customer decision right there, in the moment. We call this “decision context.”

While businesses have traditionally recognized the need for accurate data, in the past, when projects went live, the first compromise often made was on data accuracy. This practice was okay in environments where it was acceptable if data processing was delayed by some time. However, when it comes to modern AI systems, which require immediate, precise data to function effectively – it’s the exact opposite.

Let me share an interesting example.

A while ago I booked a flight with one of the major American airlines. I was initially supposed to travel from Phoenix to Dallas and then to London and Stockholm. Due to a delay in Phoenix, the airline rebooked me via another city without coordinating with British Airways. Consequently, upon arriving in London, I discovered British Airways had cancelled their onward ticket to Stockholm because they were not informed of the changes, resulting in a four-hour delay. The problem here was that the decision context in which the American airline company made its decision wasn’t sufficient for the whole decision, and that’s where we need to think.

How do real-time, AI-driven data layers improve industry-wide decision-making?

Steve Jones: The adoption of robust market-centric data layer capable of supporting real-time, AI-driven decision-making is essential. The key here is replicating the success of algorithmic trading. Algorithmic trading relies on having the right decision context – all the necessary information – to make the right choice quickly. The success of algorithmic trading shows us we can extend this mentality to other areas of the business. Extending these data-driven decision-making frameworks to other business areas can enhance operational efficiency and strategic agility across various industries. Like for banks using the same core system (like Guidewire), differentiation comes from the decisions made within that system, not the system itself.

As businesses continue to evolve their data management strategies, there is a growing dialogue around the terminology used to describe market-centric data layers. The discussion often focuses on whether traditional terms adequately reflect the impact of these systems on user experience and operational efficiency. There is a push to adopt terminology that more accurately describes the functional and strategic use of data in business environments.

Let’s come to our original vision of creating a product-service integrated client centric experience in Banking. In this context let’s discuss the state of contact centers. There’s a wealth of data from websites, apps, and other interactions that’s simply not accessible to the agents or the channels. How do you assess this situation?

Steve Jones: The problem is every channel and division has its own data silo. However, we need to and consider the operational data view from the customer’s perspective. Instead of focusing on omnichannel from a business standpoint, we should build a customer-centric operational data layer. This is why we want to put it above the application data layer. This allows us to make better decisions and differentiate ourselves not through the channel itself, but through the ability to provide a consistent customer experience.

It’s the combination of internal and external data that’s crucial. Here’s where “market-centric data layer” (MCDL) becomes interesting. Traditionally, data systems are built around internal operations. The “market-centric data layer” (MCDL) emphasizes building data systems around the market, with the customer at its core. The customer is a key part of this market, and understanding customer behavior within that market context is crucial. This market-centric approach aligns perfectly with the concept of customer journey mapping, which emphasizes building journeys around the customer, not around products. A focus on product journeys often misses the mark on what customers truly want. So, the MCDL directly supports this customer-centric approach. This is the future of engagement: competing in the market of ideas for customers and doing so in an outbound way.

I think that the concept of “market-centric data layer” describes it well. You compete for the customer in the market and not on your back end. The company with the most accurate view on the customer can make the most accurate decisions and therefore be more competitive. And the customer is one of the most competitive and challenging market-centric data areas that you have as a business.

How do you see the future of this Market-centric data layer?

Steve Jones: I believe that the market of ideas is going to be immense. Imagine an avatar that’s available 24/7 and is designed for the role of a digital financial advisor, able to actively promote your brand. A person wondering about their life insurance in the middle of the night could ask it questions and get answers when the avatar collects the necessary data. Right after that, the avatar would be able to make changes in the policies.

The move towards Market-centric data layer represents a significant evolution in the way businesses manage and leverage data. This technology is set to become a fundamental element of business operations, driving innovation, enhancing decision-making accuracy, and ensuring operational agility in an increasingly data-driven world. As companies continue to realize the benefits of real-time data, the landscape of business decision-making will undoubtedly continue to transform, enabling businesses to respond more effectively to the challenges and opportunities of the digital age.

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Ulrich Windheuser

Vice President | Head of Enterprise, Data & Analytics, Capgemini Invent
Ulrich Windheuser hat mehr als 25 Jahre Erfahrung in Banking. Funktional haben ihn stets die Herausforderungen der Finance/Risk-Integration getrieben, insbesondere forderten ihn das Schaffen einer einheitlichen Datenplattform mit hoher Datenqualität heraus. Auf dieser Basis freut er sich auf die neuen, darüber hinausgehenden Herausforderungen, um Banken zu mehr datengetriebenen Geschäftsmodellen zu verhelfen. Aktuell leitet er in Deutschland die Capability Unit Enterprise, Data & Analytics. Er hat an der Mercator Universität Duisburg Mathematik studiert und an der Universität Kaiserslautern in Technomathematik promoviert.


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