In an exclusive interview at Dublin Tech Summit, BNS UEP co-founders Shawn Butler and Kika von Klück discuss the transformative power of data in the finance sector.
In today's data-driven world, where data is driving innovation and decision-making, it is more important than ever to recognize its vital role in the financial sector. Dublin Tech SummitThere, Bob’s Guide met with two visionary leaders, co-founders of BNS UEP, Shawn Butler and Kika von Klueck.
Sean ButlerHead of Architecture and Analytics, and Kika von KlückThe research and innovation leader shared his insights in an exclusive interview, highlighting the transformative power of data. BNSUEP Specializing in DataOps, data integrity governance and innovation, we ensure data remains impactful throughout its lifecycle.
In our conversation, we discussed the evolution of data management, the difference between synthetic and real-time data, the impact of generative AI, ethical considerations, how data can drive business sustainability, and more. These insights highlighted the importance of advanced data solutions and ethical AI development in the future of the financial industry.
The evolution of data
Sean Butler began his career in technology in 1996, when internet speeds were very different. “Back then we had old-fashioned phone lines and T1s, with a maximum bandwidth throughput of 1.5 megabits.”
Over the years, Butler has had the opportunity to witness data transmission evolve from simple one-way pathways to the complex interconnected systems we rely on today.
“Right now, data is being sent everywhere, and this is considered a fully meshed topology,” he noted.
Butler's fascination with data began around 2016-2017. “I was always interested in data, numbers, and transmission throughput, but I really understood it when I started thinking about key performance indicators,” he explains. Metrics such as customer retention and satisfaction became essential to measure the efficiency and performance of applications. Understanding these metrics allows companies to improve their services and customer experience, improving overall performance.
“KPIs like customer retention and satisfaction help us understand how well we're doing and where we need to improve.”
The importance of data to drive the business world
Data is essential to business operations across all sectors. “Data is absolutely essential,” Butler stressed. “From a business perspective, every sector, including NGOs and non-profits, rely on data to continue to operate.”
Butler explained that the application allows businesses to make informed decisions by providing visibility into key metrics such as customer retention, satisfaction and growth trends. This visibility is essential to understand how a business is performing and identify areas where improvements can be made. He also highlighted an often overlooked aspect of data risk management.
“Data risk monitoring is crucial. Any vulnerabilities in applications or databases can have devastating consequences for any business,” he said. He went on to explain that companies need to focus on mission-critical applications and databases because “they are not only revenue catalysts, but also operational catalysts. If an application goes down, it can lead to data loss. Corporate Compliance It creates problems and affects the company's reputation.”
Data also plays a key role in compliance and regulatory obligations. For example, the advent of regulations such as GDPR requires companies to pay close attention to data privacy and protection. Proper data management can help companies comply with these regulations and avoid costly fines and reputational damage.
Data Solutions for Better Business Decisions
Today's data tools are becoming more sophisticated, starting with data observability. “Observability is essential from the inception of code in development to deployment to production,” Butler explains. Tools that integrate with code repositories like GitHub and GitLab and ticketing systems like Jira can help continuously monitor the health and usability of your data. This ensures that your data is accurate and trustworthy, which is essential for making informed business decisions.
Advances in data architecture, such as consolidating data lakes and data warehouses into data lake houses, will further facilitate data management.
“We're seeing solutions emerge that accelerate the execution of pipelines into data warehouses and data lakes, and integrate them into what we're now calling data lake houses,” Butler said. This integration enables efficient data storage, management and analysis, helping companies derive insights more quickly and accurately.
These advanced tools and solutions enable businesses to streamline data processing, reduce inefficiencies and improve decision-making. This not only increases operational efficiency but also Innovation and Growth.
Synthetic and real-time data
Synthetic data and real-time data have different purposes. Butler explained that synthetic data is often generated for testing purposes and is generated by machine learning AI agents. “Synthetic data is used to train models and understand data characteristics,” he said. This type of data helps understand different variables and train models under supervised learning and reinforcement learning frameworks.
“Synthetic data allows us to simulate different scenarios and test how our models react to different inputs,” Butler explains. “This is essential for developing robust, reliable AI systems that can deal with the complexities of the real world.”
Real-time data, on the other hand, is generated from a variety of sources in a real-time production environment. “Real-time data is natural data that hasn't been manipulated by AI or synthetic processes,” Butler explains. This data is essential for making immediate, informed decisions because it reflects the current state of operations.
“Real-time data is the lifeblood of operational decision-making. It provides real-time insight into the business, allowing leaders to quickly respond to changing conditions and make data-driven decisions.”
Understanding the differences between synthetic and real-time data can help businesses better leverage each data type to meet their specific needs. Synthetic data is extremely useful for development and testing, while real-time data is essential for operational decision-making and responding to real-world events.
Synthetic data also allows for broader testing without compromising sensitive information, which is especially useful in industries such as finance and healthcare where data privacy is essential.
The impacts and ethical considerations of generative AI
Generative AI and prompts have significant implications for data manipulation and bring ethical considerations to the forefront, with Butler warning about bias and preconceptions in AI models.
“If I have biases and I give instructions to an AI, my biases will come out,” he explained, emphasizing the need for critical discussion and inclusivity in AI training to avoid creating biased algorithms. “Diversity and inclusivity are essential to avoid training algorithms that exclude different groups of people,” he stressed.
“The challenge with generative AI today is that there are a lot of biases and preconceived ideas and a lack of critical debate. If I'm leaning one way or the other and I already have biases about political affiliation, ethics, culture, business morals, then when I give instructions to an AI, my biases are bound to come out.”
Butler also stressed the importance of transparency and accountability in AI development: “We need to ensure that our AI systems are fair, unbiased, and inclusive,” he said. This includes regularly Examining potential bias in AI models By making the necessary adjustments to promote equity and inclusion, companies can build trust with their customers and stakeholders.
The role of data in sustainable business
Data plays a key role in driving sustainability in business. “It starts with data inventory and quality,” Butler emphasized. With accurate data (for example, financial growth and carbon emissions metrics), companies can make strategic decisions to reduce their environmental impact. “Carbon reduction is one way of making the planet a better place,” he added.
Leveraging data can help companies optimize processes, reduce energy consumption and minimize their carbon footprint. “Not only is this good for the environment, it also improves a company's reputation and bottom line,” Butler says. For example, companies can use data to monitor energy usage and identify areas where they can reduce waste and improve efficiency.
Additionally, data can help companies track their progress toward sustainability goals. Analyzing data on emissions, resource use, and other environmental metrics can help companies evaluate their performance and make informed decisions about how to improve. This continuous improvement process is key to achieving long-term sustainability.
Sustainable and regenerative business models
Kika von Klück explained the difference between sustainable and regenerative business models. “Sustainable means you can keep growing, but in nature you can't grow forever,” said von Klück. A regenerative model, on the other hand, takes into account different forms of capital other than profits to ensure that all parts of the business are healthy.
“Regenerative business models must take into account different forms of capital, not just profit,” von Klück explains. This holistic approach includes employees, communities and the environment to foster long-term resilience and adaptability.
By adopting regenerative practices, companies can create more value for all stakeholders and contribute to the well-being of society and the planet. This approach also helps companies build stronger relationships with customers and communities, and enhance their brand and reputation.
From sustainable to renewable: The role of data
Data is crucial in the transition from sustainable to regenerative business models. “Data helps us measure the evolution from sustainability to regeneration,” von Klück noted. For example, financial institutions can use data to track the impact of their investments on environmental and social factors.
“Understanding key behavioral indicators and key risk indicators can enable companies to make more informed and ethical decisions,” von Klück explained.
Real-time data from a variety of sensors will play a key role in this transition. “Real-time data comes from a world of sensors and connectivity. Only through collaboration and connectivity have we been able to find rapid ways to fight diseases like COVID-19,” she said, citing the rapid global response to the pandemic as an example of how data and collaboration can solve urgent problems.
“Using data to measure the evolution from sustainability to regeneration helps financial institutions understand the impact of their investments,” explained von Klück. This approach allows companies to identify emerging trends, mitigate risks and foster long-term sustainability.
Data can also enable financial institutions to make more strategic investments that promote environmental and social well-being. By analyzing data on community engagement, employee happiness, and other social indicators, companies can ensure they are making a positive contribution to society.
Watch the full video here:
Harnessing Data for Innovation and Sustainability
Insights from Shawn Butler and Kika von Klück highlight the transformative power of data in finance. From enhancing operations and managing risk to driving sustainable and regenerative business models, data is at the heart of informed decision-making.
As technology continues to evolve, data observability, ethical AI, and comprehensive data management will become increasingly important and shape the future of the financial industry.
