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Mathias Golombek, Chief Technology Officer of Exasol – Interview Series




Mathias Golombek is the Chief Technology Officer (CTO) of Exasol. He joined the company as a software developer in 2004 after studying computer science with a heavy focus on databases, distributed systems, software development processes, and genetic algorithms. By 2005, he was responsible for the Database Optimizer team and in 2007 he became Head of Research & Development. In 2014, Mathias was appointed CTO. In this role, he is responsible for product development, product management, operations, support, and technical consulting.

What initially attracted you to computer science?

When I was in fourth grade, my older brother had some lessons where they learned to program BASIC, and he showed me what you can do with that. Together, we developed an Easter riddle on our Commodore 64 for our youngest brother, and ever since then, I have been fascinated by computers. Computer science in general is all about solving problems and being creative and I think that aspect attracted me the most to the field.

Can you share your journey from joining Exasol as a software developer in 2004 to becoming the CTO? How have your roles evolved over the years, especially in the rapidly changing tech landscape?

I studied Computer Science at The University of Würzburg in Germany and started at Exasol as a software developer in 2004 after graduating. After my first year with Exasol, I was promoted to Head of the Database Optimizer Team and then Head of Research and Development. After that, I served as Head of R&D for seven years before stepping into my current role as CTO in 2014.

From the beginning, I was amazed at what Exasol was doing — this German technology company fighting against big names like Microsoft, IBM, and Oracle. I was blown away by the opportunity in front of me — as a developer, creating this massively parallel processing (MPP), in-memory database management system was  heaven on earth.

I’ve enjoyed every moment of working with this talented engineering team. As CTO, I oversee Exasol’s product innovation, development and technical support. It’s been exciting to see how much the Exasol team has grown globally as we work to support our customers and their evolving needs. The fundamentals are the same — we’re still an in-memory database system, but now we’re empowering our customers to harness the power of their data for AI implementations.

Exasol has been at the forefront of high-performance analytics databases. From your perspective, what sets Exasol apart in this competitive space?

Business leaders are constantly tasked with navigating how to do more with less. In recent years, this has become even more challenging as the economy continues to be tumultuous and the proliferation of AI technology has taken up budget and time.

As a high-performance analytics database provider, Exasol has remained ahead of the curve when it comes to helping businesses do more with less. We help companies transform business intelligence (BI) into better insights with Exasol Espresso, our versatile query engine that plugs into existing data stacks. Global brands including T-Mobile, Piedmont Healthcare, and Allianz use Exasol Espresso to turn higher volumes of data into faster, deeper and cheaper insights. And I think we’ve done a great job of mastering the delicate balance between performance, price and flexibility so customers don’t have to compromise.

To support companies on their AI journeys, we also recently unveiled Espresso AI, equipping our versatile query engine with a new suite of AI tools that enable organizations to harness the power of their data for advanced AI-driven insights and decision-making. Espresso AI’s capabilities make AI more affordable and accessible, enabling customers to bypass expensive, time-consuming experimentation and achieve immediate ROI. This is a game-changer for enterprises who are focused on driving innovation and delivering value in the age of AI.

The 2024 AI and Analytics Report by Exasol highlights underinvestment in AI as a pathway to business failure. Could you expand on the key findings of this report and why investing in AI is critical for businesses today?

As you stated, the main takeaway from Exasol’s 2024 AI and Analytics Report is that underinvestment in AI leads to business failure. Based on our survey of senior decision-makers as well as data scientists and analysts across the U.S., U.K., and Germany, nearly all (91%) respondents agree that AI is one of the most important topics for organizations in the next two years, with 72% admitting that not investing in AI today will put future business viability at risk. Put simply, in today’s environment, businesses that are not thinking about AI are already behind.

Businesses are facing pressure from stakeholders to invest in AI – and there are many reasons why. Investment in AI has already helped organizations across industries – from healthcare to financial services and retail – unlock new revenue streams, enhance customer experiences, optimize operations, increase productivity, accelerate competitiveness and more. The list only grows from there as businesses are starting to find specific ways to leverage AI to fit unique business needs.

The same report mentions major barriers to AI adoption, including data science gaps and latency in implementation. How does Exasol address these challenges for its clients?

Despite the critical need for AI investment, businesses still face significant barriers to broader implementation. Exasol’s AI and Analytics Report indicates that up to 78% of decision-makers experience gaps in at least one area of their data science and machine learning (ML) models, with 47% citing speed to implement new data requirements as a challenge. An additional 79% claim new business analysis requirements take too long to be implemented by their data teams. Other factors hindering widespread AI adoption include the lack of an implementation strategy, poor data quality, insufficient data volumes and integration with existing systems. On top of that, evolving bureaucratic requirements and regulations for AI are causing issues for many companies with 88% of respondents stating they need more clarity.

As AI deployment grows, it will become even more important for businesses to ensure strong data foundations. Exasol offers flexibility, resilience and scalability to businesses adopting an AI strategy. As roles such as the Chief Data Officer (CDO) continue to evolve and become more complex –– with growing ethical and compliance challenges at the forefront –– Exasol supports data leaders and helps them transform BI into faster, better insights that will inform business decisions and positively impact the bottom line.

While AI has become critical to business success, it’s only as effective as the tools, technology and people powering it on the backend. The survey results emphasize the significant gap between current BI tools and their output – more tools does not necessarily mean faster performance or better insights. As CDOs prepare for more complexity and are tasked to do more with less, they must evaluate the data analytics stack to ensure productivity, speed, and flexibility – all at a reasonable cost.

Espresso AI helps to close this gap for the enterprise by optimizing data extraction, loading, and transformation processes to give users the flexibility to immediately experiment with new technologies at scale, regardless of infrastructure restriction – whether on-premises, cloud, or hybrid. Users can reduce data movement costs and effort while bringing in emerging technologies like LLMs into their database. These capabilities help organizations accelerate their journey toward implementing AI and ML solutions while ensuring the quality and reliability of their data.

Data literacy is becoming increasingly important in the age of AI. How does Exasol contribute to enhancing data literacy among its clients and the wider community?

In today’s data-rich working environments, data literacy skills are more important than ever – and quickly becoming a “need to have” rather than a “nice to have” in the age of AI. Across industries, proficiency in working with, understanding and communicating data effectively has become vital. But there remains a data literacy gap.

Data literacy is about having the skills to interpret complex information and the ability to act on those findings. But often data access is siloed within an organization or only a small subset of individuals have the necessary data literacy skills to understand and access the vast amounts of data flowing through the business. This approach is flawed because it limits the amount of time and resources dedicated to utilizing data and, ultimately, the data literacy gap creates a barrier to business innovation.

When people are data literate, they can understand data, analyze it and apply their own ideas, skills and expertise to it. The more people with the knowledge, confidence and tools to unravel and take meaning from data, the more successful an organization can be. At Exasol, we support data leaders and businesses in driving data literacy and education.

In addition to the education component, businesses should optimize their tech stacks and BI tools to enable data democratization. Data accessibility and data literacy go hand in hand. Investment in both is needed to further data strategies. For example, with Exasol, our tuning-free system enables businesses to focus on the data usage, rather than the technology. The high speed allows teams to work interactively with data and avoid being restricted by performance limitations. This ultimately leads to data democratization.

Now is the time for data democratization to shift from a topic of discussion to action within organizations. As more people across various departments gain access to meaningful insights, it will alleviate the traditional bottlenecks caused by data analytics teams. When these traditional silos come crashing down, organizations will realize just how wide and deep the need is for their teams and individuals to use data. Even people who don’t currently think they are an end user of data will be pulled into feed off of data.

With this shift comes a major challenge to anticipate in the coming years – the workforce will need to be upgraded in order for every employee to gain the proper skill set to effectively use data and insights to make business decisions. Today’s workforce won’t know the right questions to ask of its data feed, or the automation powering it. The value of being able to articulate precise, probing and business-tethered questions is increasing in value, creating a dire need to train the workforce on this capability.

You have a strong background in databases, distributed systems, and genetic algorithms. How do these areas of expertise influence Exasol's product development and innovation strategy?

My background is a foundation of working in our field and understanding the technology trends of the last two decades. It’s exciting and rewarding to work with innovative customers who turn database technology into interesting use cases. Our innovation strategy doesn’t just depend on one individual, but a large team of sophisticated architects and developers who understand the future of software, hardware and data applications.

With AI transforming industries at an unprecedented pace, what do you believe are the essential components of a future-proof data stack for businesses looking to leverage AI and analytics effectively?

The rapid adoption of AI has been a prime example of why it’s important for enterprises to stay ahead of the evolving tech landscape. The unfortunate truth, however, is that most data stacks are still behind the AI curve.

To future-proof data stacks, businesses should first evaluate data foundations to identify gaps, bugs or other challenges. This will help them ensure data quality and speed – elements that are critical for driving valuable insights and fueling AI and LLM models.

In addition, teams should invest in the tools and technologies that can easily integrate with other solutions in the stack. As AI is paired with other technologies, like open source, we’ll see new models emerge to solve traditional business problems. Generative AI, like ChatGPT, will also merge with more traditional AI technology, such as descriptive or predictive analytics, to open new opportunities for organizations and streamline traditionally cumbersome processes.

To future-proof data stacks, enterprises should also integrate AI and BI. Businesses have been using BI tools for decades to extract valuable insights and while many improvements have been made, there are still BI limitations or barriers that AI can help with. AI can enable faster outcomes, enhance personalization and transform the BI landscape into a more inclusive and user-friendly domain. Since BI typically focuses on analyzing historical data to provide insights, AI can extend BI capabilities by helping anticipate future events, generating predictions and recommending actions to influence desired outcomes.

Productivity, flexibility, and cost-savings are highlighted as three ways Exasol helps global brands innovate. Can you provide an example of how Exasol has enabled a client to achieve significant ROI through your analytics database?

According to a 2023 Forrester Total Economic Impact Study, Exasol customers achieve up to a 320% ROI on their initial investment over three years by improving operational efficiency, database performance, and offering a simple and flexible data infrastructure.

One customer for example, Helsana, a leader in Switzerland’s competitive healthcare industry, came to Exasol to fill a need for a modern data and analytics platform. Before Exasol, Helsana relied on various reporting tools with data warehouses built on different technologies and ETL tools which created a tangled, inefficient architecture. Compared to the company’s existing legacy solution, Exasol’s Data Warehouse demonstrated a five to tenfold performance improvement.

Now, Exasol is central to Helsana’s AI journey, serving as the repository for the structured data that Helsana uses across all of its AI models and providing the

foundation for its analytics. With Exasol, the Helsana team has boosted performance, reduced costs, increased agility and established a solid AI foundation, all of which contribute to significant ROI on top of an increased ability to better serve customers.

Looking ahead, what are the upcoming trends in data analytics and business intelligence that Exasol is preparing for, and how do you plan to continue driving innovation in this space?

 The year 2023 introduced AI on a wide scale, which caused knee-jerk reactions from organizations that ultimately spawned countless poorly designed and executed automation experiments. 2024 will be a transformation year for AI experimentation and foundational work. So far, the primary applications of GenAI have been for information access through chatbots, customer service automation, and software coding. However, there will be pioneers who are adopting these exciting technologies for a whole plethora of business decision-making and optimizations. Looking beyond 2024, we’ll start to see a bigger push towards productive implementations of AI.

At Exasol, we’re committed to driving innovation and delivering value to our customers, this includes helping them develop and implement AI at scale. With Exasol, customers can marry BI and AI to overcome data silos in an integrated analytics system. Our flexibility around deployment options also enable organizations to decide where they want to host their analytics stack, whether it’s in the public cloud, private cloud or on-premises. With Exasol’s Espresso AI, we are positioned to empower enterprises to harness the value of AI-driven analytics, regardless of where organizations fall in their AI journey.

Thank you for the great interview, readers who wish to learn more should visit Exasol.

A founding partner of unite.AI & a member of the Forbes Technology Council, Antoine is a futurist who is passionate about the future of AI & robotics.

He is also the Founder of, a website that focuses on investing in disruptive technology.