Mathias Golombek is the Chief Expertise Officer (CTO) of Exasol. He joined the corporate as a software program developer in 2004 after finding out pc science with a heavy deal with databases, distributed techniques, software program improvement processes, and genetic algorithms. By 2005, he was answerable for the Database Optimizer staff and in 2007 he turned Head of Analysis & Growth. In 2014, Mathias was appointed CTO. On this position, he’s answerable for product improvement, product administration, operations, help, and technical consulting.
What initially attracted you to pc science?
Once I was in fourth grade, my older brother had some classes the place they discovered to program BASIC, and he confirmed me what you are able to do with that. Collectively, we developed an Easter riddle on our Commodore 64 for our youngest brother, and ever since then, I’ve been fascinated by computer systems. Laptop science generally is all about fixing issues and being inventive and I believe that facet attracted me essentially the most to the sector.
Are you able to share your journey from becoming a member of Exasol as a software program developer in 2004 to turning into the CTO? How have your roles developed over time, particularly within the quickly altering tech panorama?
I studied Laptop Science at The College of Würzburg in Germany and began at Exasol as a software program developer in 2004 after graduating. After my first yr with Exasol, I used to be promoted to Head of the Database Optimizer Group after which Head of Analysis and Growth. After that, I served as Head of R&D for seven years earlier than moving into my present position as CTO in 2014.
From the start, I used to be amazed at what Exasol was doing — this German expertise firm preventing in opposition to large names like Microsoft, IBM, and Oracle. I used to be blown away by the chance in entrance of me — as a developer, creating this massively parallel processing (MPP), in-memory database administration system was heaven on earth.
I’ve loved each second of working with this proficient engineering staff. As CTO, I oversee Exasol’s product innovation, improvement and technical help. It’s been thrilling to see how a lot the Exasol staff has grown globally as we work to help our prospects and their evolving wants. The basics are the identical — we’re nonetheless an in-memory database system, however now we’re empowering our prospects to harness the facility of their knowledge for AI implementations.
Exasol has been on the forefront of high-performance analytics databases. Out of your perspective, what units Exasol aside on this aggressive area?
Enterprise leaders are always tasked with navigating find out how to do extra with much less. In recent times, this has develop into much more difficult because the economic system continues to be tumultuous and the proliferation of AI expertise has taken up finances and time.
As a high-performance analytics database supplier, Exasol has remained forward of the curve relating to serving to companies do extra with much less. We assist corporations rework enterprise intelligence (BI) into higher insights with Exasol Espresso, our versatile question engine that plugs into current knowledge stacks. International manufacturers together with T-Cell, Piedmont Healthcare, and Allianz use Exasol Espresso to show larger volumes of knowledge into sooner, deeper and cheaper insights. And I believe we’ve completed an excellent job of mastering the fragile steadiness between efficiency, value and adaptability so prospects don’t must compromise.
To help corporations on their AI journeys, we additionally lately unveiled Espresso AI, equipping our versatile question engine with a brand new suite of AI instruments that allow organizations to harness the facility of their knowledge for superior AI-driven insights and decision-making. Espresso AI’s capabilities make AI extra inexpensive and accessible, enabling prospects to bypass costly, time-consuming experimentation and obtain instant ROI. It is a game-changer for enterprises who’re targeted on driving innovation and delivering worth within the age of AI.
The 2024 AI and Analytics Report by Exasol highlights underinvestment in AI as a pathway to enterprise failure. Might you increase on the important thing findings of this report and why investing in AI is essential for companies immediately?
As you said, the principle takeaway from Exasol’s 2024 AI and Analytics Report is that underinvestment in AI results in enterprise failure. Based mostly on our survey of senior decision-makers in addition to knowledge scientists and analysts throughout the U.S., U.Ok., and Germany, almost all (91%) respondents agree that AI is among the most vital subjects for organizations within the subsequent two years, with 72% admitting that not investing in AI immediately will put future enterprise viability in danger. Put merely, in immediately’s surroundings, companies that aren’t desirous about AI are already behind.
Companies are dealing with stress from stakeholders to put money into AI – and there are a lot of the reason why. Funding in AI has already helped organizations throughout industries – from healthcare to monetary companies and retail – unlock new income streams, improve buyer experiences, optimize operations, enhance productiveness, speed up competitiveness and extra. The record solely grows from there as companies are beginning to discover particular methods to leverage AI to suit distinctive enterprise wants.
The identical report mentions main boundaries to AI adoption, together with knowledge science gaps and latency in implementation. How does Exasol deal with these challenges for its purchasers?
Regardless of the essential want for AI funding, companies nonetheless face vital boundaries to broader implementation. Exasol’s AI and Analytics Report signifies that as much as 78% of decision-makers expertise gaps in at the very least one space of their knowledge science and machine studying (ML) fashions, with 47% citing pace to implement new knowledge necessities as a problem. An extra 79% declare new enterprise evaluation necessities take too lengthy to be applied by their knowledge groups. Different components hindering widespread AI adoption embrace the dearth of an implementation technique, poor knowledge high quality, inadequate knowledge volumes and integration with current techniques. On prime of that, evolving bureaucratic necessities and laws for AI are inflicting points for a lot of corporations with 88% of respondents stating they want extra readability.
As AI deployment grows, it’s going to develop into much more vital for companies to make sure robust knowledge foundations. Exasol affords flexibility, resilience and scalability to companies adopting an AI technique. As roles such because the Chief Knowledge Officer (CDO) proceed to evolve and develop into extra advanced –– with rising moral and compliance challenges on the forefront –– Exasol helps knowledge leaders and helps them rework BI into sooner, higher insights that may inform enterprise choices and positively affect the underside line.
Whereas AI has develop into essential to enterprise success, it’s solely as efficient because the instruments, expertise and other people powering it on the backend. The survey outcomes emphasize the numerous hole between present BI instruments and their output – extra instruments doesn’t essentially imply sooner efficiency or higher insights. As CDOs put together for extra complexity and are tasked to do extra with much less, they need to consider the info analytics stack to make sure productiveness, pace, and adaptability – all at an inexpensive price.
Espresso AI helps to shut this hole for the enterprise by optimizing knowledge extraction, loading, and transformation processes to provide customers the flexibleness to instantly experiment with new applied sciences at scale, no matter infrastructure restriction – whether or not on-premises, cloud, or hybrid. Customers can cut back knowledge motion prices and energy whereas bringing in rising applied sciences like LLMs into their database. These capabilities assist organizations speed up their journey towards implementing AI and ML options whereas making certain the standard and reliability of their knowledge.
Knowledge literacy is turning into more and more vital within the age of AI. How does Exasol contribute to enhancing knowledge literacy amongst its purchasers and the broader neighborhood?
In immediately’s data-rich working environments, knowledge literacy expertise are extra vital than ever – and shortly turning into a “must have” somewhat than a “good to have” within the age of AI. Throughout industries, proficiency in working with, understanding and speaking knowledge successfully has develop into very important. However there stays an information literacy hole.
Knowledge literacy is about having the talents to interpret advanced info and the power to behave on these findings. However typically knowledge entry is siloed inside a corporation or solely a small subset of people have the mandatory knowledge literacy expertise to grasp and entry the huge quantities of knowledge flowing by way of the enterprise. This strategy is flawed as a result of it limits the period of time and assets devoted to using knowledge and, finally, the info literacy hole creates a barrier to enterprise innovation.
When individuals are knowledge literate, they will perceive knowledge, analyze it and apply their very own concepts, expertise and experience to it. The extra folks with the data, confidence and instruments to unravel and take which means from knowledge, the extra profitable a corporation will be. At Exasol, we help knowledge leaders and companies in driving knowledge literacy and training.
Along with the training part, companies ought to optimize their tech stacks and BI instruments to allow knowledge democratization. Knowledge accessibility and knowledge literacy go hand in hand. Funding in each is required to additional knowledge methods. For instance, with Exasol, our tuning-free system allows companies to deal with the info utilization, somewhat than the expertise. The excessive pace permits groups to work interactively with knowledge and keep away from being restricted by efficiency limitations. This finally results in knowledge democratization.
Now’s the time for knowledge democratization to shift from a subject of debate to motion inside organizations. As extra folks throughout numerous departments achieve entry to significant insights, it’s going to alleviate the normal bottlenecks brought on by knowledge analytics groups. When these conventional silos come crashing down, organizations will notice simply how large and deep the necessity is for his or her groups and people to make use of knowledge. Even individuals who don’t at the moment suppose they’re an finish consumer of knowledge shall be pulled into feed off of knowledge.
With this shift comes a significant problem to anticipate within the coming years – the workforce will should be upgraded to ensure that each worker to achieve the correct talent set to successfully use knowledge and insights to make enterprise choices. Right this moment’s workforce received’t know the precise inquiries to ask of its knowledge feed, or the automation powering it. The worth of having the ability to articulate exact, probing and business-tethered questions is growing in worth, making a dire want to coach the workforce on this functionality.
You could have a robust background in databases, distributed techniques, and genetic algorithms. How do these areas of experience affect Exasol’s product improvement and innovation technique?
My background is a basis of working in our discipline and understanding the expertise tendencies of the final twenty years. It’s thrilling and rewarding to work with modern prospects who flip database expertise into attention-grabbing use instances. Our innovation technique doesn’t simply rely on one particular person, however a big staff of subtle architects and builders who perceive the way forward for software program, {hardware} and knowledge functions.
With AI reworking industries at an unprecedented tempo, what do you imagine are the important elements of a future-proof knowledge stack for companies trying to leverage AI and analytics successfully?
The fast adoption of AI has been a main instance of why it’s vital for enterprises to remain forward of the evolving tech panorama. The unlucky fact, nevertheless, is that almost all knowledge stacks are nonetheless behind the AI curve.
To future-proof knowledge stacks, companies ought to first consider knowledge foundations to determine gaps, bugs or different challenges. This can assist them guarantee knowledge high quality and pace – components which are essential for driving beneficial insights and fueling AI and LLM fashions.
As well as, groups ought to put money into the instruments and applied sciences that may simply combine with different options within the stack. As AI is paired with different applied sciences, like open supply, we’ll see new fashions emerge to unravel conventional enterprise issues. Generative AI, like ChatGPT, may also merge with extra conventional AI expertise, corresponding to descriptive or predictive analytics, to open new alternatives for organizations and streamline historically cumbersome processes.
To future-proof knowledge stacks, enterprises must also combine AI and BI. Companies have been utilizing BI instruments for many years to extract beneficial insights and whereas many enhancements have been made, there are nonetheless BI limitations or boundaries that AI might help with. AI can allow sooner outcomes, improve personalization and rework the BI panorama right into a extra inclusive and user-friendly area. Since BI sometimes focuses on analyzing historic knowledge to supply insights, AI can lengthen BI capabilities by serving to anticipate future occasions, producing predictions and recommending actions to affect desired outcomes.
Productiveness, flexibility, and cost-savings are highlighted as 3 ways Exasol helps world manufacturers innovate. Are you able to present an instance of how Exasol has enabled a consumer to realize vital ROI by way of your analytics database?
In accordance with a 2023 Forrester Whole Financial Impression Research, Exasol prospects obtain as much as a 320% ROI on their preliminary funding over three years by enhancing operational effectivity, database efficiency, and providing a easy and versatile knowledge infrastructure.
One buyer for instance, Helsana, a frontrunner in Switzerland’s aggressive healthcare business, got here to Exasol to fill a necessity for a contemporary knowledge and analytics platform. Earlier than Exasol, Helsana relied on numerous reporting instruments with knowledge warehouses constructed on completely different applied sciences and ETL instruments which created a tangled, inefficient structure. In comparison with the corporate’s current legacy answer, Exasol’s Knowledge Warehouse demonstrated a 5 to tenfold efficiency enchancment.
Now, Exasol is central to Helsana’s AI journey, serving because the repository for the structured knowledge that Helsana makes use of throughout all of its AI fashions and offering the
basis for its analytics. With Exasol, the Helsana staff has boosted efficiency, diminished prices, elevated agility and established a stable AI basis, all of which contribute to vital ROI on prime of an elevated skill to higher serve prospects.
Trying forward, what are the upcoming tendencies in knowledge analytics and enterprise intelligence that Exasol is making ready for, and the way do you intend to proceed driving innovation on this area?
The yr 2023 launched AI on a large scale, which brought about knee-jerk reactions from organizations that finally spawned numerous poorly designed and executed automation experiments. 2024 shall be a metamorphosis yr for AI experimentation and foundational work. Thus far, the first functions of GenAI have been for info entry by way of chatbots, customer support automation, and software program coding. Nevertheless, there shall be pioneers who’re adopting these thrilling applied sciences for an entire plethora of enterprise decision-making and optimizations. Trying past 2024, we’ll begin to see a much bigger push in direction of productive implementations of AI.
At Exasol, we’re dedicated to driving innovation and delivering worth to our prospects, this contains serving to them develop and implement AI at scale. With Exasol, prospects can marry BI and AI to beat knowledge silos in an built-in analytics system. Our flexibility round deployment choices additionally allow organizations to resolve the place they need to host their analytics stack, whether or not it’s within the public cloud, non-public cloud or on-premises. With Exasol’s Espresso AI, we’re positioned to empower enterprises to harness the worth of AI-driven analytics, no matter the place organizations fall of their AI journey.
Thanks for the nice interview, readers who want to study extra ought to go to Exasol.