Alexander Walden of Ververica On How To Leverage Data To Take Your Company To The Next Level

Personalization and Customer Insights: Use data to deeply understand your customers and their preferences. Employ advanced analytics and machine learning to create personalized customer experiences. Tailor marketing campaigns, product recommendations, and customer support interactions to match individual preferences, thereby increasing customer satisfaction and loyalty.

The proper use of Data — data about team performance, data about customers, or data about the competition, can be a sort of force multiplier. It has the potential to dramatically help a business to scale. But sadly, many businesses have data but don’t know how how to properly leverage it. What exactly is useful data? How can you properly utilize data? How can data help a business grow? To address this, we are talking to business leaders who can share stories from their experience about “How To Effectively Leverage Data To Take Your Company To The Next Level”. As part of this series, we had the pleasure of interviewing Alexander Walden, CEO, Ververica.

At Ververica, the Original Creators of Apache Flink®, Alexander Walden is a catalyst for business transformation and innovation in the stream processing sector. In his role as CEO, Alex has drawn on his transformational leadership ability to fuel a diverse and collaborative culture. Continuously innovating and pushing the boundaries of data streaming technologies, providing unparalleled customer service and fostering a vibrant ecosystem of partners and developers are other topics high on his agenda. Before embarking on his journey with Ververica, Alex’s career spanned across various roles in IT architecture, IT sales, and business management within leading software companies, professional services and Cloud providers across the globe.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?

I’ve worked in the field of technology and stream processing for a number of years. My experience has spanned across various roles at IT companies, including architecture and sales — some of which I’ve led in management positions. Before my time at Ververica, I worked at VMware in various roles and previously at T-Systems before that as VP. Throughout my career, I have led organizational transformations, often across international teams, all with the purpose of creating better business value for clients, partners, and C-level. I lead to inspire new ways of thinking, agile methodologies, and mindset change to create new paradigm shifts. Ultimately my goal has always been to disrupt and transform the stream-processing sector in a way that keeps pace with globalization and major black swan events, which we are seeing increasing levels of. And I am proud to say Ververica’s solutions do exactly that. In 2022, I was appointed CEO of Ververica.

It has been said that sometimes our mistakes can be our greatest teachers. Can you share a story about a humorous mistake you made when you were first starting and the lesson you learned from that?

Not sure I would call it “mistake” — but I remember a situation when starting out in my role at T-Systems. Like many technically minded people, I engaged in a conversation where I went on to tell the person I was speaking with all about the tech features of a solution. I could tell by their expression it felt like we were having two separate conversations as I realized later on into the conversation I was not focusing on his business needs. My focus was to detail the features of the solution. It sort of went, “here is a feature, don’t you get it?”. And no — he did not. Luckily the relationship lasted and we went from pure tech to what it really meant for him as a person and the company he was representing. This was a valid lesson learned: always ask what is in it for them (“How can they shine?”) AND the company from a value perspective — whilst also knowing the technological benefits of the solution obviously.

Leadership often entails making difficult decisions or hard choices between two apparently good paths. Can you share a story with us about a hard decision or choice you had to make as a leader?

Even after many years, I find letting go of people the hardest thing. These are hard decisions, especially in larger organizations where individuals may not have seen it coming. In such cases, my approach has always been to speak with the individual first and then see how we can solve the situation in an equally beneficial way. I know this sounds strange — but often, low performers have a strength they can use better elsewhere. And then it is a matter of either guiding the person to that insight or even finding that “new position” for them. Unfortunately, it is often more coaching and mentoring than finding — but this is also a satisfying taking away in the difficulty of such a decision.

Are you working on any new, exciting projects now? How do you think that might help people?

We are indeed working on some exciting projects at Ververica, building upon the latest innovations we’ve recently announced with Ververica Cloud. One key focus is advancing our real-time stream processing technology to enable businesses to drive even greater value from their data.

One of our ongoing projects involves enhancing the capabilities of Ververica Cloud, particularly in the areas of data processing speed and cost-efficiency. We are continually pushing the boundaries of what’s possible with VERA (Ververica Runtime Assembly) technology, which has already demonstrated data processing speeds up to 2x faster than open-source Apache Flink®. We aim to further optimize and fine-tune this technology to empower businesses to process data at unprecedented speeds, delivering insights in milliseconds rather than minutes.

Additionally, we are investing in projects that focus on simplifying the development and deployment of stream-processing applications. Our goal is to make it even easier for organizations to harness the power of real-time data analytics without the complexities of managing underlying infrastructure. We’re doing this by providing streamlined development environments and user-friendly interfaces, allowing users to develop and deploy applications in no time.

These projects are aimed at helping businesses across multiple sectors by enabling them to make faster, data-driven decisions, optimize their operations, and enhance their overall competitiveness. Whether it’s a financial institution seeking to detect fraud in real-time, an e-commerce company personalizing recommendations for its customers, or a manufacturing facility optimizing production processes, our projects aim to provide the tools and technology needed to achieve these goals.

We’re excited about the potential impact these projects can have on organizations striving to harness the full potential of their data.

You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

I believe three character traits have been instrumental to my success:

Resilience: There have been numerous challenges along my journey, but my resilience and positive outlook have always helped me push through. For instance, when we faced a major setback in our early days, we regrouped, adapted, and eventually turned this setback into an opportunity to improve our product and strategy. This experience taught me how to back bounce stronger.

Innovation: In today’s rapidly evolving business landscape, innovation is crucial. I’ve always encouraged a culture of innovation within my team. We’ve experienced many moments of innovation but one in particular comes to mind. We had a breakthrough by harnessing data-driven insights to identify a market gap which led to the development of a groundbreaking product that revolutionized not only our company’s offerings but our entire industry.

Empathy: Understanding the needs and perspectives of both our customers and employees has been pivotal. Empathy helps in forging strong relationships and fostering a positive work culture. I vividly recall a situation where empathizing with a frustrated customer led to a resolution that not only retained their business but also improved our product based on their feedback and consequently grew the account.

Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” For the benefit of our readers, can you help explain what it looks like to use data to make decisions?

Using data to make decisions means leveraging the insights and information derived from various data sources to inform and guide strategic and operational choices. It involves collecting, analyzing, and interpreting data to gain a deep understanding of trends, customer behavior, market dynamics, and internal processes. Data-driven decision-making is characterized by objectivity, evidence-based choices, and a commitment to continuous improvement. It empowers organizations to make informed, efficient, and impactful decisions that drive growth and success. This is the core of Ververica’s business.

Based on your experience. which companies can most benefit from tools that empower data collaboration?

Companies across various industries can benefit from tools that empower data collaboration. However, those in highly competitive markets or rapidly evolving sectors often find such tools particularly valuable. This includes industries like technology, finance and healthcare, eCommerce, and manufacturing. Data collaboration can enhance decision-making, innovation, and customer experiences. Additionally, large enterprises with complex data ecosystems and a need for cross-functional collaboration can derive substantial value from these tools. Ultimately, any organization looking to harness the collective power of data across teams and departments can benefit significantly from data collaboration solutions.

Can you share some examples of how data analytics and data collaboration can help to improve operations, processes, and customer experiences? We’d love to hear some stories if possible.

As mentioned, data analytics and data collaboration have a profound impact on improving operations, processes, and customer experiences. For instance, in operations, data analytics can optimize supply chain management by forecasting demand, reducing wastage, and streamlining logistics. In processes, it can automate routine tasks, identify bottlenecks, and enhance efficiency.

For customer experiences, data collaboration enables a 360-degree view of the customer. This allows companies to personalize marketing efforts, recommend relevant products or services, and address customer needs proactively. Additionally, data-driven insights help companies understand customer feedback, sentiment, and preferences, enabling them to tailor their offerings and support to meet customer expectations effectively.

From your vantage point, has the shift toward becoming more data-driven been challenging for some teams or organizations? What are the challenges? How can organizations solve these challenges?

Yes, from my perspective, the shift toward becoming more data-driven has presented challenges for many teams and organizations. Some of the common challenges include:

Cultural Resistance: One of the primary challenges is the resistance to change within the organizational culture. Some team members or departments may be accustomed to making decisions based on intuition or past practices, and the transition to data-driven decision-making can be met with skepticism.

Data Quality and Integration: Ensuring data accuracy, quality, and consistency across different systems and sources can be a significant hurdle. In many cases, organizations struggle with integrating data from various siloed systems.

Skill Gaps: Not all team members possess the necessary data analytics and interpretation skills. Acquiring or developing these skills can be time-consuming and may require additional training or hiring.

Privacy and Security Concerns: Collecting and using data raises privacy and security concerns. Organizations must navigate complex regulations and ensure data protection while still deriving meaningful insights.

To overcome these challenges, organizations can take several steps:

• First requirement: have insight and understanding that “there is a problem” vs “this is the way we have always done things.” Once that is an established mindset — continue. However, the next steps should at least help in coming to that insight.
• Leadership Commitment: Leadership should champion the data-driven culture and set an example for the entire organization. When leaders emphasize the importance of data-driven decision-making, it helps create buy-in at all levels.
• Education and Training: Invest in training programs to upskill employees in data analysis and interpretation. This can include data literacy courses and workshops to ensure all team members are comfortable working with data.
• Data Governance: Implement strong data governance practices to ensure data quality, security, and compliance. This includes clear data ownership, data access controls, and data auditing processes.
• Change Management: Develop a change management plan that addresses cultural resistance. Encourage open communication, provide opportunities for feedback, and celebrate successes to create a positive atmosphere around data-driven initiatives.
• Technology Solutions: Invest in data analytics tools and platforms that simplify data integration, analysis, and visualization. These tools can make data more accessible and actionable for teams.
• Collaboration: Promote cross-functional collaboration to break down data silos. Encourage teams to share insights and collaborate on data-driven projects.
• Continuous Improvement: Recognize that the shift to a data-driven culture is an ongoing process. Regularly review and refine data strategies to adapt to changing business needs and technological advancements.

By addressing these challenges proactively and fostering a data-driven mindset throughout the organization, teams and organizations can successfully navigate the transition and harness the full potential of data for better decision-making and business outcomes.

Ok. Thank you. Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.

Data-Driven Decision-Making: Base strategic decisions on data insights rather than gut feelings or tradition. Implement a culture of data-driven decision-making across the organization, where data informs choices related to product development, marketing strategies, resource allocation, and more. Encourage teams to back their proposals with data-supported arguments, fostering a culture of accountability and continuous improvement.

Example: A retail company analyzed sales data and customer feedback to identify that a specific product category was declining in popularity. Based on this insight, they decided to revamp the product line, introduce new features, and adjust their marketing strategy. As a result, sales in that category rebounded, demonstrating the effectiveness of data-driven decision-making. Hint: “gut feel” can often be found in data when you combine the right internal and external sources.

Personalization and Customer Insights: Use data to deeply understand your customers and their preferences. Employ advanced analytics and machine learning to create personalized customer experiences. Tailor marketing campaigns, product recommendations, and customer support interactions to match individual preferences, thereby increasing customer satisfaction and loyalty.

Example: An eCommerce platform analyzed user behavior data to personalize product recommendations. By understanding each customer’s browsing and purchase history, the platform suggested relevant products, leading to a significant increase in the average order value and customer retention rates. Hint: life-changing events like a wedding or baby birth are best discovered in real-time and can be discovered when analyzing a customer’s buying behavior.

Operational Efficiency: Leverage data analytics to optimize internal operations and processes. Identify bottlenecks, inefficiencies, and areas for improvement within your supply chain, production, and customer service. Data-driven insights can help streamline processes, reduce costs, and enhance overall efficiency.

Example: A manufacturing company used data analytics to track equipment performance in real-time. By monitoring machine data, they could predict maintenance needs accurately, reducing unplanned downtime by 30% and minimizing maintenance costs.

Predictive Analytics: Harness the power of predictive analytics to anticipate future trends and outcomes. Predictive models can help in demand forecasting, inventory management, and even proactive maintenance for machinery and equipment. By foreseeing potential issues and opportunities, companies can make preemptive moves to stay ahead of the competition.

Example: An airline company employed predictive analytics to optimize flight schedules and crew assignments. By analyzing historical data on flight delays, weather patterns, and crew availability, they could adjust schedules in advance, improving on-time performance and customer satisfaction.

Data Collaboration: Encourage collaboration and data sharing across departments and teams. Break down data silos by using collaborative tools and platforms that enable real-time sharing of insights. Cross-functional teams can work together to solve complex problems and innovate more effectively when they have access to a comprehensive view of the data.

Example: A healthcare organization improved patient care by enabling data collaboration among doctors, nurses, and administrative staff. Electronic health records were integrated, allowing real-time access to patient data. This collaboration resulted in faster decision-making, reduced medical errors, and improved patient outcomes.

These five strategies empower companies to leverage data effectively, driving growth, efficiency, and innovation. By adopting a data-centric approach, organizations can make informed decisions, enhance customer experiences, optimize operations, and stay ahead in today’s competitive business landscape.

Based on your experience, how do you think the needs for data might evolve and change over the next five years?

Anticipating the evolution of data needs over the next five years is crucial for organizations seeking to stay competitive and relevant in a data-centric world. A lot of data has been collected in recent years already — often because we were told “data is the new oil.” But unlike with oil, many have not fully mastered the art of turning it into information and actionable business insights. Instead, many have just created a “data swamp.” Here are some insights into how the conditions for data might evolve:

Greater Emphasis on Real-Time Data: As businesses continue to digitize their operations and customer interactions, the demand for real-time data will intensify. Organizations are increasingly reliant on instant insights to make timely decisions, whether in finance, e-commerce, healthcare, or manufacturing. The ability to capture, process, and act on data in real-time will become a strategic advantage.

Advanced Analytics and AI: The need for advanced analytics, machine learning, and artificial intelligence (AI) will grow significantly. Companies will seek to extract deeper insights from their data to drive innovation, optimize processes, and enhance customer experiences. Machine learning models and AI algorithms will play a pivotal role in automating decision-making processes and predicting future trends.

Data Privacy and Security: With the increasing volume of data, concerns about data privacy and security will continue to escalate. Organizations will need to invest heavily in robust data protection measures, compliance with data regulations (e.g., GDPR, CCPA), and cybersecurity to safeguard sensitive information and maintain customer trust.

Data Quality and Governance: Data quality will remain a paramount concern. Companies will prioritize data governance initiatives to ensure data accuracy, consistency, and reliability. Implementing data stewardship practices and maintaining data catalogs will be essential for managing the growing data landscape effectively.

Edge and IoT Data: The proliferation of edge computing and the Internet of Things (IoT) will generate vast amounts of data at the network’s edge. Organizations will need to adapt their infrastructure and data processing capabilities to collect, analyze, and act on this distributed data effectively.

Data Collaboration and Integration: The ability to break down data silos and foster collaboration across departments and external partners will become a strategic imperative. Data integration platforms and tools that facilitate data sharing and collaboration will be in high demand.

Ethical Data Use: Ethical considerations around data usage and AI algorithms will gain prominence. Organizations will need to establish ethical guidelines and practices for responsible data collection and usage, addressing issues like bias in algorithms and fairness in decision-making.

Personalization and Customer Insights: Customer expectations for personalized experiences will continue to rise. Organizations will require more sophisticated data analytics to deliver tailored products, services, and content. Customer data platforms (CDPs) will be essential for aggregating and analyzing customer data effectively.

Data Sustainability: Sustainability concerns will extend to data centers and data processing. Companies will explore eco-friendly data storage and processing solutions as part of their environmental responsibility initiatives.

Data Talent and Skillsets: The demand for data professionals, including data scientists, data engineers, and data analysts, will persist. Organizations will need to invest in attracting, retaining, and upskilling their data talent to leverage data effectively.

How can our readers further follow your work?

I’m inspired everyday by the outputs happening at Ververica. In 2024, we are in the works of much more to come. To stay tuned, be sure to follow us on: LinkedIn and Twitter, and be sure to sign up for our Blog notifications at: ververica.com/blog

Alexander Walden LinkedIn: https://www.linkedin.com/in/awalden/

Ververica Twitter: https://twitter.com/VervericaData

Thank you so much for sharing these important insights. We wish you continued success and good health!