Watch: BNS on data’s role in business, AI ethics, and sustainable models

In today’s data-driven world, where information fuels innovation and decision-making, recognising its crucial role in the financial sector is more important than ever. This was evident at the Dublin Tech Summit, where Bobsguide met with two visionary leaders, Shawn Butler and Kika von Klück, co-founders of BNS UEP.

Shawn Butler, head of architecture and analytics, and Kika von Klück, who leads research and innovation, shared their insights in an exclusive interview, highlighting the transformative power of data. BNS UEP specialises in DataOps, data integrity governance, and technological innovation, ensuring data remains impactful throughout its lifecycle.

Our conversation covered the evolution of data management, the differences between synthetic and real-time data, the impact of generative AI, ethical considerations, and how data drives business sustainability. These insights underscored the importance of advanced data solutions and ethical AI development in the financial industry’s future.

The evolution of data

Shawn Butler started his technology career in 1996, a time when internet speeds were a far cry from what they are today. “Back then, we were dealing with plain old telephone service lines and T1 lines. The most you could get was 1.5 megabits in terms of bandwidth throughput.”

Over the years, Butler had the chance to witness data transmission evolve from simple, one-way paths to the complex, interconnected systems we rely on now.

“Data is being transmitted all over the place now, in what we consider to be a fully meshed topology,” he noted.

Butler’s fascination with data truly sparked around 2016-2017. “I’ve always been into data, numbers, and transmission throughput, but it really clicked when I started thinking about key performance indicators,” he explained, Metrics like customer retention and satisfaction became crucial for measuring the efficiency and performance of applications. By understanding these metrics, businesses can improve their services and customer experiences, leading to better overall performance.

“KPIs, such as customer retention and satisfaction, allow us to see how well we are doing and where we need to improve.”

The importance of data in keeping the business world running

Data is vital for business operations across all sectors. “It’s absolutely essential,” Butler emphasised. “From a business perspective, any sector, including NGOs or nonprofits, relies on data to keep the lights on.”

Butler explained that applications offer visibility into key metrics such as customer retention, satisfaction, and growth trends, enabling businesses to make informed decisions. This visibility is crucial for understanding how a business is performing and where it can improve. He also highlighted the often-overlooked aspect of data risk management.

“Data risk monitoring is crucial. If an application or database has vulnerabilities, it could be catastrophic for any business,” he said. He further explained that businesses must focus on mission-critical applications and databases, as “they’re not just revenue catalysts; they are also operational catalysts. “If an application goes down, it could lead to data loss, regulatory compliance issues, and even affect the company’s reputation.”

Data also plays a significant role in compliance and regulatory mandates. For example, with the advent of regulations like GDPR, companies must be vigilant about data privacy and protection. Proper data management helps businesses comply with these regulations and avoid hefty fines and reputational damage.

Data solutions for better business decisions

Today’s data tools are sophisticated, starting with data observability. “Observability is essential, starting from the inception of code in development to its deployment in production,” Butler explained. Tools that integrate with code repositories like GitHub or GitLab, and ticketing systems like Jira, help monitor data health and usability continuously. This ensures that data is accurate and reliable, which is crucial for making informed business decisions.

Advancements in data architecture, such as the convergence of data lakes and warehouses into data lake houses, further aid in data management.

“We’re seeing solutions that expedite pipelines running into data warehouses and data lakes, converging them into what we now call data lake houses,” Butler said. This convergence allows for efficient data storage, management, and analytics, enabling businesses to derive insights more quickly and accurately.

By using these advanced tools and solutions, businesses can streamline their data processes, reduce inefficiencies, and improve decision-making. This not only enhances operational efficiency but also drives innovation and growth.

Synthetic vs. real-time data

Synthetic and real-time data serve different purposes. Butler described synthetic data as being generated for testing purposes, often by AI agents in machine learning. “Synthetic data is used to train models and understand data characteristics,” he said. This type of data helps in understanding various variables and training models under supervised and reinforcement learning frameworks.

“Synthetic data allows us to simulate various scenarios and test how our models respond to different inputs,” Butler explained. “This is crucial for developing robust and reliable AI systems that can handle real-world complexities.”

Real-time data, on the other hand, is generated from various sources in real-time production environments. “Real-time data is natural data that has not been manipulated by AI or synthetic processes,” Butler clarified. This data is crucial for making immediate, informed decisions as it reflects the current state of operations.

“Real-time data is the lifeblood of operational decision-making. It provides a real-time snapshot of the business, enabling leaders to respond quickly to changing conditions and make data-driven decisions.”

By understanding the differences between synthetic and real-time data, businesses can better utilise each type to meet their specific needs. Synthetic data is invaluable 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 extensive testing without compromising sensitive information. This is particularly useful in industries like finance and healthcare, where data privacy is essential.

The impact of generative AI and ethical considerations

Generative AI and prompting have profound implications for data manipulation, bringing ethical considerations to the forefront. Butler warned about biases and prejudices in AI models.

“If I have a bias and prompt the AI, my biases will come out,” he explained, highlighting the need for critical discourse and inclusivity in AI training to avoid creating biased algorithms. “Diversity and inclusion are essential to avoid training algorithms that exclude different groups of people,” he stressed.

“The challenge right now with Generative AI is that there are different biases and prejudices, and there is a lack of critical discourse. If I’m leaning one way and already have a bias in terms of political affiliations, ethics, culture, or business morals, certainly when I prompt the AI, my biases are going to come out.”

Butler also highlighted 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 involves regularly reviewing AI models for potential biases and making necessary adjustments to promote fairness and inclusivity. By doing so, businesses can build trust with their customers and stakeholders.

The role of data in sustainable business

Data plays a crucial role in driving sustainability in business. “It begins with data inventory and quality,” Butler emphasised. Accurate data –like financial growth and carbon emissions metrics, for instance– enables businesses to make strategic decisions that reduce environmental impact. “Carbon reduction is one way we can make the planet a better place,” he added.

By leveraging data, businesses can optimise processes, reduce energy consumption, and minimise their carbon footprint. “This not only benefits the environment but also enhances the company’s reputation and bottom line,” Butler said. For instance, companies can use data to monitor their energy usage and identify areas where they can reduce waste and improve efficiency.

Furthermore, data helps businesses track their progress towards sustainability goals. By analysing data on emissions, resource usage, and other environmental metrics, companies can assess their performance and make informed decisions about how to improve. This continuous improvement process is key to achieving long-term sustainability.

Sustainable vs. regenerative business models

Kika von Klück explained the difference between sustainable and regenerative business models. “Sustainable means you can sustain growth, but eternal growth is impossible in nature,” von Klück said. Regenerative models, on the other hand, ensure all parts of a business are healthy, considering multiple forms of capital beyond profits.

“In a regenerative business model, you make sure that you’re taking into account multiple forms of capital, not only profits,” von Klück explained. This holistic approach includes employees, communities, and the environment, fostering long-term resilience and adaptability.

By adopting regenerative practices, businesses 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 their customers and communities, enhancing their brand and reputation.

From sustainable to regenerative: the role of data

Data is pivotal in transitioning from sustainable to regenerative business models. “Data helps measure the evolution from sustainability to regeneration,” von Klück noted. Financial institutions, for example, can use data to track the impact of their investments on environmental and social factors.

“Understanding key behaviour indicators and key risk indicators allows businesses to make more informed and ethical decisions,” von Klück explained.

Real-time data from various sensors plays a critical role in this transition. “Real-time data comes from sensors and a world of connection. It was only through collaboration and connection that we could come to a fast way of combating a disease like COVID-19,” she said, referring to the swift global response to the pandemic as an example of how data and cooperation can solve urgent problems.

“Using data to measure the evolution from sustainability to regeneration helps financial institutions understand the impact of their investments,” von Klück explained. This approach allows businesses to identify emerging trends, mitigate risks, and promote long-term sustainability.

By leveraging data, financial institutions can also make more strategic investments that promote environmental and social well-being. By analysing data on community engagement, employee well-being, and other social metrics, companies can ensure they are making positive contributions to society.

Watch the full video here:

Harnessing data for innovation and sustainability

The insights from Shawn Butler and Kika von Klück highlight the transformative power of data in the financial sector. From enhancing business operations and managing risks to fostering sustainable and regenerative business models, data is at the heart of informed decision-making.

As technology continues to evolve, the importance of data observability, ethical AI, and comprehensive data management will only grow, shaping the future of the financial industry.