Mathias Golombek is the Chief Expertise Officer (CTO) of Exasol. He joined the corporate as a software program developer in 2004 after learning pc science with a heavy give attention to databases, distributed programs, software program growth processes, and genetic algorithms. By 2005, he was accountable for the Database Optimizer workforce and in 2007 he grew to become Head of Analysis & Improvement. In 2014, Mathias was appointed CTO. On this function, he’s accountable for product growth, product administration, operations, assist, 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. Pc science normally is all about fixing issues and being inventive and I feel that side 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 changing into the CTO? How have your roles developed through the years, particularly within the quickly altering tech panorama?
I studied Pc 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 Crew after which Head of Analysis and Improvement. After that, I served as Head of R&D for seven years earlier than moving into my present function as CTO in 2014.
From the start, I used to be amazed at what Exasol was doing — this German know-how firm preventing towards 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 workforce. As CTO, I oversee Exasol’s product innovation, growth and technical assist. It’s been thrilling to see how a lot the Exasol workforce 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 the way to do extra with much less. Lately, this has grow to be much more difficult because the financial system continues to be tumultuous and the proliferation of AI know-how has taken up price range and time.
As a high-performance analytics database supplier, Exasol has remained forward of the curve in relation to serving to companies do extra with much less. We assist firms remodel enterprise intelligence (BI) into higher insights with Exasol Espresso, our versatile question engine that plugs into current knowledge stacks. World 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 feel we’ve finished an amazing job of mastering the fragile stability between efficiency, value and adaptability so clients don’t need to compromise.
To assist firms on their AI journeys, we additionally just 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 rapid ROI. It is 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. May you develop on the important thing findings of this report and why investing in AI is crucial for companies right this moment?
As you said, 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 among the most necessary matters for organizations within the subsequent two years, with 72% admitting that not investing in AI right this moment will put future enterprise viability in danger. Put merely, in right this moment’s surroundings, companies that aren’t eager about AI are already behind.
Companies are dealing with strain from stakeholders to spend money on AI – and there are a lot of explanation why. Funding in AI has already helped organizations throughout industries – from healthcare to monetary providers and retail – unlock new income streams, improve buyer experiences, optimize operations, enhance 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 boundaries to AI adoption, together with knowledge science gaps and latency in implementation. How does Exasol tackle these challenges for its purchasers?
Regardless of the crucial 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 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 carried out by their knowledge groups. Different elements hindering widespread AI adoption embody the shortage of an implementation technique, poor knowledge high quality, inadequate knowledge volumes and integration with current programs. On high of that, evolving bureaucratic necessities and laws for AI are inflicting points for a lot of firms with 88% of respondents stating they want extra readability.
As AI deployment grows, it can grow to be much more necessary for companies to make sure robust knowledge foundations. Exasol provides flexibility, resilience and scalability to companies adopting an AI technique. As roles such because the Chief Knowledge Officer (CDO) proceed to evolve and grow to be extra complicated –– with rising moral and compliance challenges on the forefront –– Exasol helps knowledge leaders and helps them remodel BI into sooner, higher insights that may inform enterprise selections and positively impression the underside line.
Whereas AI has grow to be crucial to enterprise success, it’s solely as efficient because the instruments, know-how 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 have to consider the info analytics stack to make sure productiveness, velocity, and adaptability – all at an affordable value.
Espresso AI helps to shut this hole for the enterprise by optimizing knowledge extraction, loading, and transformation processes to present customers the flexibleness 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 changing 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 this moment’s data-rich working environments, knowledge literacy expertise are extra necessary than ever – and rapidly changing into a “need to have” somewhat than a “nice to have” within the age of AI. Throughout industries, proficiency in working with, understanding and speaking knowledge successfully has grow to be important. However there stays an information literacy hole.
Knowledge literacy is about having the abilities to interpret complicated 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 required knowledge literacy expertise to know and entry the huge quantities of knowledge flowing by way of the enterprise. This method 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 persons are knowledge literate, they’ll perceive knowledge, analyze it and apply their very own concepts, expertise and experience to it. The extra individuals with the data, confidence and instruments to unravel and take that means from knowledge, the extra profitable a corporation may 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 give attention to the info utilization, somewhat than the know-how. The excessive velocity 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 individuals throughout numerous departments achieve entry to significant insights, it can alleviate the standard bottlenecks attributable to knowledge analytics groups. When these conventional silos come crashing down, organizations will understand simply how vast 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 assume they’re an finish consumer of knowledge might be pulled into feed off of knowledge.
With this shift comes a serious problem to anticipate within the coming years – the workforce will should be upgraded to ensure that each worker to realize the correct ability set to successfully use knowledge and insights to make enterprise selections. Right now’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 rising in worth, making a dire want to coach the workforce on this functionality.
You’ve gotten a robust background in databases, distributed programs, and genetic algorithms. How do these areas of experience affect Exasol’s product growth and innovation technique?
My background is a basis of working in our discipline and understanding the know-how developments of the final twenty years. It’s thrilling and rewarding to work with progressive clients who flip database know-how into attention-grabbing use circumstances. Our innovation technique doesn’t simply rely on one particular person, however a big workforce 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 seeking to leverage AI and analytics successfully?
The fast adoption of AI has been a first-rate 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 determine gaps, bugs or different challenges. This can assist them guarantee knowledge high quality and velocity – components which are crucial for driving useful 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 also merge with extra conventional AI know-how, resembling 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 useful insights and whereas many enhancements have been made, there are nonetheless BI limitations or boundaries that AI will help with. AI can allow sooner outcomes, improve personalization and remodel 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 shopper to realize vital ROI by way of your analytics database?
In response to a 2023 Forrester Complete Financial Affect Examine, Exasol clients obtain as much as a 320% ROI on their preliminary funding over three years by bettering 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 current legacy resolution, 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 workforce has boosted efficiency, diminished prices, elevated agility and established a stable AI basis, all of which contribute to vital ROI on high of an elevated capability to higher serve clients.
Wanting forward, what are the upcoming developments in knowledge analytics and enterprise intelligence that Exasol is getting ready for, and the way do you propose 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 might be a metamorphosis yr for AI experimentation and foundational work. To date, the first purposes of GenAI have been for info entry by way of chatbots, customer support automation, and software program coding. Nevertheless, there might be pioneers who’re adopting these thrilling applied sciences for a complete plethora of enterprise decision-making and optimizations. Wanting 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 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 determine the place they need to host their analytics stack, whether or not it’s within the public cloud, personal 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.
