On the Challenges Facing Financial Institutions. Interview with Joe Lichtenberg
“There are three factors C-suite executives need to consider when addressing operational resilience: the need to make better business decisions faster; improved automation and the elimination of manual processes; and the ability to respond to unexpected volume and valuation volatility.” –Joe Lichtenberg.
I have interviewed Joe Lichtenberg, responsible for product and industry marketing for data platform software at InterSystems.
Q1. What are the main challenges financial institutions are facing right now?
Joe Lichtenberg: As financial services organizations are pushed to rapidly adapt due to the pandemic, they also want to gain a competitive edge, deliver more value to customers, reduce risk, and respond more quickly to the needs of distributed businesses. To not only stand out, but ultimately survive, financial services organizations have relied on their digital capabilities. For instance, many have adapted faster than anticipated and found ways to supplement traditional face-to-face customer service. As the volume of complex data grows and the need to use data for decision-making accelerates, it is becoming more difficult to reach their business goals and deliver differentiated service to customers at a faster rate.
Q2. What do you suggest to C-suite executives that could help them re-evaluate their operational resilience in light of increasing volumes and volatility, and the shift to a remote working environment (especially due to the COVID-19 crisis)?
Joe Lichtenberg: There are three factors C-suite executives need to consider when addressing operational resilience: the need to make better business decisions faster; improved automation and the elimination of manual processes; and the ability to respond to unexpected volume and valuation volatility. Executives need to prioritize their organizations’ ability to access and process a single representation of accurate, consistent, real-time and trusted data. The volatility and uncertainty fueled by the pandemic pushed organizations to rely on the vast amounts of data available to them to properly bolster resilience and adaptability.
From scenario planning to modeling enterprise risk and liquidity, regulatory compliance, and wealth management, access to accurate and current data can enable organizations to make smarter business decisions faster. Organizations need to streamline and accelerate operations by eliminating manual processes where possible and automating processes. Not only will this help increase speed and agility, but it will also reduce the delays and errors associated with manual processes. Finally, executives must look to ensure they have sufficient headroom, processing capabilities, and systems in place to foster agility and reliability and to respond to unexpected volatility.
Q3. What are the key challenges they face to keep pace with the ongoing market dynamics?
Joe Lichtenberg: The more data sources organizations have, the more complex their practices become. As data grows, so does the prevalence of data silos, making access to a single, trusted, and usable representation of the data challenging. Additionally, analytics are more difficult to perform with disorganized data, causing results to be less accurate, especially in regards to visibility, decision support, risk, compliance, and reporting. This issue is extremely important as organizations perform advanced analytics (e.g. machine learning), where access to large sets of clean, healthy data is required in order to build models that deliver accurate results.
Q4. Do you believe that capital markets are paying the price for delaying core investments into their data architectures?
Joe Lichtenberg: Established capital markets organizations have delayed some of their investments in data architectures for a variety of reasons, and that move has ultimately kept operational costs in check, as large changes could drastically disrupt workflows and set them back further in the short term. These organizations typically have well-established core infrastructures in place that have served them well. Over the years, they have been expanded on, which means introducing significant changes is a complicated and complex process. However, the combination of unprecedented volatility, rising customer expectations, and competition from niche financial technology companies – that are providing new services – are straining the limits of these systems and pushing established firms to modernize faster, using microservices, APIs, and AI. In fact, in some cases financial organizations are outsourcing non-core capabilities to FinTechs. The FinTechs, not burdened by legacy infrastructure, are able to innovate quickly but may not have the breadth, depth, or resilience of the established firms. As capital markets firms modernize their data architecture, replacing these systems can lead to greater downtime that can slow and stall modernization efforts. Implementing a data fabric enables organizations to modernize without costly rip-and-replace methods and empowers them to address siloed legacy applications while existing systems remain in place.
Q5. What is a “data fabric”?
Joe Lichtenberg: A data fabric is a reference architecture that provides the capabilities required to discover, connect, integrate, transform, analyze, manage, and utilize enterprise data assets. It enables the business to meet its myriad of business goals faster and with less complexity than legacy technologies. It connects disparate data and applications, including on-premises, from partners, and in the public cloud. An enterprise data fabric combines several data management technologies, including database management, data integration, data transformation, pipelining, API management, etc.
A data fabric addresses many of the limitations of data warehouses and data lakes and brings on a new wave of redesign to the modern data architecture to create a more dynamic system according to Gartner. A smart data fabric extends the capabilities to include a wide array of analytics capabilities – eliminating the complexity and delays associated with traditional approaches like data lakes that require moving data to yet another environment.
Q6. How can firms eliminate the friction that has been built up around accessing information, reduce the cost and complexity of data wrangling?
Joe Lichtenberg: Building a smart enterprise data fabric as a data layer to serve the organization enables firms to reduce complexity, speed development, accelerate time to value, simplify maintenance and operations, and lower total cost of ownership. Additionally, it enables organizations to execute analytics and programmatic actions on demand, by utilizing clean and current data that resides within the organization
Q7. What are your recommendations for firms that need to work with competitive insights?
Joe Lichtenberg: Competitive insights require access to accurate and current data that may reside in different silos in order to get maximum value. A data fabric provides the necessary access to this required data, but a smart data fabric takes this a step further. It incorporates a wide range of analytics capabilities, including data exploration, business intelligence, natural language processing, and machine learning, that enable organizations to visaulize, drill into and explore, and combine the data from different sources. This helps not just skilled developers, data stewards and analysts, but a wide range of users that are close to the business to gain new insights to guide business decisions and create intelligent prescriptive services and applications.
Q8. Where do you see Artificial intelligence and machine learning technologies playing a role for financial institutions?
Joe Lichtenberg: Advanced analytics are essential to the future success of financial institutions. AI, ML, and natural language processing (NLP) tools are already being utilized in various areas of financial services. Although some may argue that niche FinTechs lead the way in the adoption of these tools, established organizations are also utilizing AI and machine learning to increase wallet share, enhance customer engagement, and guide strategic decisions. However, these tools are only as effective as the data that powers them. Without healthy data, they can’t deliver accurate results. That is why it’s essential to place an emphasis on the quality of data that is collected and fed into these powerful tools.
Q9. The next generation of technology advancement must be built on strong data foundations. Artificial intelligence and machine learning require a high volume of current, clean, normalised data from across the relevant silos of a business to functions. How is it possible to deliver this data without requiring an entire structural rebuild of every enterprise data store?
Joe Lichtenberg: This is a common initiative for which a smart, enterprise data fabric is being used. But implementing such a reference architecture can be complex, requiring implementing and integrating many different data management technologies. A modern data platform that combines multiple layers and capabilities in a single product, reducing complexity by minimizing the number of products and technologies required, are helping to deliver critical business benefits with a simpler architecture, faster time to value, and lower total cost of ownership. For example, modern data platforms combine horizontally scalable, transactional and analytic database management capabilities, data and application integration functionality, data persistence, API management, analytics, machine learning, and business intelligence in a single product built from the ground up on a common architecture. Not only can the implementation of a smart enterprise data fabric with a modern data platform at the core help firms address current pain points, it accelerates the move toward a digital future without the costly rip-and-replace of their current operational infrastructure.
Joe Lichtenberg is responsible for product and industry marketing for data platform software at InterSystems. Joe has decades of experience working with various data management, analytics, and cloud computing technology providers.
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