Mathias Golombek is the Chief Know-how Officer (CTO) of Exasol. He joined the corporate as a software program developer in 2004 after learning laptop science with a heavy deal with databases, distributed methods, software program improvement processes, and genetic algorithms. By 2005, he was accountable for the Database Optimizer group and in 2007 he turned Head of Analysis & Growth. In 2014, Mathias was appointed CTO. On this position, he’s accountable for product improvement, product administration, operations, assist, and technical consulting.
What initially attracted you to laptop 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 basically is all about fixing issues and being inventive and I believe that facet attracted me probably 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 Workforce 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 combating towards huge 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 group. As CTO, I oversee Exasol’s product innovation, improvement and technical assist. It’s been thrilling to see how a lot the Exasol group has grown globally as we work to assist our clients and their evolving wants. The basics are the identical — we’re nonetheless an in-memory database system, however now we’re empowering our clients to harness the ability 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 easy methods 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 funds and time.
As a high-performance analytics database supplier, Exasol has remained forward of the curve on the subject of serving to companies do extra with much less. We assist firms rework enterprise intelligence (BI) into higher insights with Exasol Espresso, our versatile question engine that plugs into present knowledge stacks. World manufacturers together with T-Cellular, Piedmont Healthcare, and Allianz use Exasol Espresso to show larger volumes of knowledge into quicker, deeper and cheaper insights. And I believe we’ve achieved a fantastic job of mastering the fragile steadiness between efficiency, worth and adaptability so clients don’t must compromise.
To assist firms 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 ability of their knowledge for superior AI-driven insights and decision-making. Espresso AI’s capabilities make AI extra reasonably priced and accessible, enabling clients to bypass costly, time-consuming experimentation and obtain fast ROI. This can be a game-changer for enterprises who’re centered 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 crucial for companies right now?
As you acknowledged, the primary 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.Okay., and Germany, almost all (91%) respondents agree that AI is likely one of the most necessary subjects for organizations within the subsequent two years, with 72% admitting that not investing in AI right now will put future enterprise viability in danger. Put merely, in right now’s atmosphere, companies that aren’t eager about AI are already behind.
Companies are dealing with stress from stakeholders to spend money on AI – and there are lots 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, improve productiveness, speed up competitiveness and extra. The listing 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 limitations 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 crucial want for AI funding, companies nonetheless face important limitations to broader implementation. Exasol’s AI and Analytics Report signifies that as much as 78% of decision-makers expertise gaps in no less than one space of their knowledge science and machine studying (ML) fashions, with 47% citing velocity 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 elements hindering widespread AI adoption embody the dearth of an implementation technique, poor knowledge high quality, inadequate knowledge volumes and integration with present methods. On high of that, evolving bureaucratic necessities and rules for AI are inflicting points for a lot of firms with 88% of respondents stating they want extra readability.
As AI deployment grows, it’s going to develop into much more necessary for companies to make sure sturdy knowledge foundations. Exasol gives 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 quicker, higher insights that can inform enterprise selections and positively influence the underside line.
Whereas AI has develop into crucial 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 quicker efficiency or higher insights. As CDOs put together for extra complexity and are tasked to do extra with much less, they have to consider the info analytics stack to make sure productiveness, velocity, and adaptability – all at an inexpensive value.
Espresso AI helps to shut this hole for the enterprise by optimizing knowledge extraction, loading, and transformation processes to provide customers the pliability to right away 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 necessary within the age of AI. How does Exasol contribute to enhancing knowledge literacy amongst its purchasers and the broader neighborhood?
In right now’s data-rich working environments, knowledge literacy expertise are extra necessary than ever – and rapidly turning into a “need to have” reasonably than a “nice to have” within the age of AI. Throughout industries, proficiency in working with, understanding and speaking knowledge successfully has develop into important. However there stays a knowledge literacy hole.
Knowledge literacy is about having the talents to interpret advanced data and the flexibility to behave on these findings. However typically knowledge entry is siloed inside a corporation or solely a small subset of people have the required knowledge literacy expertise to grasp and entry the huge quantities of knowledge flowing by the enterprise. This method is flawed as a result of it limits the period of time and sources devoted to using knowledge and, in the end, the info literacy hole creates a barrier to enterprise innovation.
When persons are knowledge literate, they’ll perceive knowledge, analyze it and apply their very own concepts, expertise and experience to it. The extra folks with the information, confidence and instruments to unravel and take that means from knowledge, the extra profitable a corporation could be. At Exasol, we assist knowledge leaders and companies in driving knowledge literacy and schooling.
Along with the schooling element, 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, reasonably than the expertise. The excessive velocity permits groups to work interactively with knowledge and keep away from being restricted by efficiency limitations. This in the end results in knowledge democratization.
Now could be 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 attributable to knowledge analytics groups. When these conventional silos come crashing down, organizations will understand simply how huge and deep the necessity is for his or her groups and people to make use of knowledge. Even individuals who don’t at present assume they’re an finish person of knowledge will probably be pulled into feed off of knowledge.
With this shift comes a significant problem to anticipate within the coming years – the workforce will have to be upgraded to ensure that each worker to realize the correct talent set to successfully use knowledge and insights to make enterprise selections. As we speak’s workforce received’t know the fitting 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’ve gotten a powerful background in databases, distributed methods, 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 area and understanding the expertise tendencies of the final twenty years. It’s thrilling and rewarding to work with revolutionary clients who flip database expertise into fascinating use circumstances. Our innovation technique doesn’t simply rely upon one particular person, however a big group of refined architects and builders who perceive the way forward for software program, {hardware} and knowledge purposes.
With AI remodeling industries at an unprecedented tempo, what do you consider are the important parts of a future-proof knowledge stack for companies trying to leverage AI and analytics successfully?
The fast adoption of AI has been a primary instance of why it’s necessary for enterprises to remain forward of the evolving tech panorama. The unlucky reality, nonetheless, is that the majority knowledge stacks are nonetheless behind the AI curve.
To future-proof knowledge stacks, companies ought to first consider knowledge foundations to establish gaps, bugs or different challenges. This may assist them guarantee knowledge high quality and velocity – components which might be crucial for driving priceless insights and fueling AI and LLM fashions.
As well as, groups ought to spend money on 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 resolve conventional enterprise issues. Generative AI, like ChatGPT, may even merge with extra conventional AI expertise, equivalent to descriptive or predictive analytics, to open new alternatives for organizations and streamline historically cumbersome processes.
To future-proof knowledge stacks, enterprises also needs to combine AI and BI. Companies have been utilizing BI instruments for many years to extract priceless insights and whereas many enhancements have been made, there are nonetheless BI limitations or limitations that AI will help with. AI can allow quicker 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 offer insights, AI can prolong 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 attain important ROI by your analytics database?
In line with a 2023 Forrester Complete Financial Affect Examine, Exasol clients 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 trade, 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 present 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 group has boosted efficiency, decreased prices, elevated agility and established a stable AI basis, all of which contribute to important ROI on high of an elevated means to higher serve clients.
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 on knee-jerk reactions from organizations that in the end spawned numerous poorly designed and executed automation experiments. 2024 will probably be a change yr for AI experimentation and foundational work. Up to now, the first purposes of GenAI have been for data entry by chatbots, customer support automation, and software program coding. Nonetheless, there will probably 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 the direction of productive implementations of AI.
At Exasol, we’re dedicated to driving innovation and delivering worth to our clients, this consists of serving to them develop and implement AI at scale. With Exasol, clients 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 good interview, readers who want to study extra ought to go to Exasol.
