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 concentrate on databases, distributed techniques, software program growth processes, and genetic algorithms. By 2005, he was answerable for the Database Optimizer crew and in 2007 he grew to become Head of Analysis & Improvement. In 2014, Mathias was appointed CTO. On this position, he’s answerable for product growth, product administration, operations, help, and technical consulting.

What initially attracted you to pc science?

After I was in fourth grade, my older brother had some classes the place they realized 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 generally is all about fixing issues and being inventive and I believe that side attracted me essentially the most to the sphere.

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 advanced 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 12 months with Exasol, I used to be promoted to Head of the Database Optimizer Group after which Head of Analysis and Improvement. After that, I served as Head of R&D for seven years earlier than entering 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 towards massive 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 gifted engineering crew. As CTO, I oversee Exasol’s product innovation, growth and technical help. It’s been thrilling to see how a lot the Exasol crew 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 information 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 how you can do extra with much less. Lately, this has turn into much more difficult because the financial 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 in terms 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 current information stacks. International manufacturers together with T-Cell, Piedmont Healthcare, and Allianz use Exasol Espresso to show larger volumes of information into sooner, deeper and cheaper insights. And I believe we’ve completed an awesome job of mastering the fragile stability between efficiency, worth and suppleness so prospects don’t must compromise.

To help 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 facility of their information 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 fast ROI. This can be 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. May you increase on the important thing findings of this report and why investing in AI is important for companies at present?

As you acknowledged, 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 information scientists and analysts throughout the U.S., U.Okay., and Germany, practically all (91%) respondents agree that AI is among the most essential subjects for organizations within the subsequent two years, with 72% admitting that not investing in AI at present will put future enterprise viability in danger. Put merely, in at present’s surroundings, companies that aren’t occupied with AI are already behind.

Companies are dealing with stress from stakeholders to put money into AI – and there are various explanation 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 information science gaps and latency in implementation. How does Exasol tackle these challenges for its shoppers?

Regardless of the important want for AI funding, companies nonetheless face vital limitations to broader implementation. Exasol’s AI and Analytics Report signifies that as much as 78% of decision-makers expertise gaps in a minimum of one space of their information science and machine studying (ML) fashions, with 47% citing velocity to implement new information necessities as a problem. An extra 79% declare new enterprise evaluation necessities take too lengthy to be applied by their information groups. Different components hindering widespread AI adoption embrace the dearth of an implementation technique, poor information high quality, inadequate information volumes and integration with current techniques. On prime 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’ll turn into much more essential for companies to make sure sturdy information 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 turn into extra advanced –– with rising moral and compliance challenges on the forefront –– Exasol helps information leaders and helps them rework BI into sooner, higher insights that may inform enterprise selections and positively impression the underside line.

Whereas AI has turn into important 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 information analytics stack to make sure productiveness, velocity, and suppleness – all at an inexpensive price.

Espresso AI helps to shut this hole for the enterprise by optimizing information extraction, loading, and transformation processes to provide customers the pliability to instantly experiment with new applied sciences at scale, no matter infrastructure restriction – whether or not on-premises, cloud, or hybrid. Customers can scale back information 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 guaranteeing the standard and reliability of their information.

Knowledge literacy is changing into more and more essential within the age of AI. How does Exasol contribute to enhancing information literacy amongst its shoppers and the broader neighborhood?

In at present’s data-rich working environments, information literacy abilities are extra essential than ever – and shortly changing into a “must have” somewhat than a “good to have” within the age of AI. Throughout industries, proficiency in working with, understanding and speaking information successfully has turn into very important. However there stays an information literacy hole.

Knowledge literacy is about having the abilities to interpret advanced data and the power to behave on these findings. However usually information entry is siloed inside a corporation or solely a small subset of people have the required information literacy abilities to know and entry the huge quantities of information flowing by means of the enterprise. This strategy is flawed as a result of it limits the period of time and sources devoted to using information and, in the end, the information literacy hole creates a barrier to enterprise innovation.

When individuals are information literate, they will perceive information, analyze it and apply their very own concepts, abilities and experience to it. The extra folks with the data, confidence and instruments to unravel and take that means from information, the extra profitable a corporation might be. At Exasol, we help information leaders and companies in driving information literacy and training.

Along with the training part, companies ought to optimize their tech stacks and BI instruments to allow information democratization. Knowledge accessibility and information literacy go hand in hand. Funding in each is required to additional information methods. For instance, with Exasol, our tuning-free system permits companies to concentrate on the information utilization, somewhat than the expertise. The excessive velocity permits groups to work interactively with information and keep away from being restricted by efficiency limitations. This in the end results in information democratization.

Now’s the time for information democratization to shift from a subject of dialogue to motion inside organizations. As extra folks throughout varied departments achieve entry to significant insights, it’ll alleviate the standard bottlenecks attributable to information 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 information. Even individuals who don’t presently suppose they’re an finish consumer of information shall be pulled into feed off of information.

With this shift comes a serious problem to anticipate within the coming years – the workforce will have to be upgraded to ensure that each worker to achieve the correct talent set to successfully use information and insights to make enterprise selections. Immediately’s workforce received’t know the appropriate inquiries to ask of its information feed, or the automation powering it. The worth of with 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 got a robust background in databases, distributed techniques, 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 area and understanding the expertise traits of the final twenty years. It’s thrilling and rewarding to work with progressive prospects who flip database expertise into fascinating use circumstances. Our innovation technique doesn’t simply depend upon one particular person, however a big crew of subtle architects and builders who perceive the way forward for software program, {hardware} and information functions.

With AI remodeling industries at an unprecedented tempo, what do you imagine are the important parts of a future-proof information stack for companies seeking to leverage AI and analytics successfully?

The fast adoption of AI has been a chief instance of why it’s essential for enterprises to remain forward of the evolving tech panorama. The unlucky reality, nonetheless, is that the majority information stacks are nonetheless behind the AI curve.

To future-proof information stacks, companies ought to first consider information foundations to determine gaps, bugs or different challenges. This may assist them guarantee information high quality and velocity – components which are important for driving worthwhile 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 resolve conventional enterprise issues. Generative AI, like ChatGPT, may even merge with extra conventional AI expertise, similar to descriptive or predictive analytics, to open new alternatives for organizations and streamline historically cumbersome processes.

To future-proof information stacks, enterprises must also combine AI and BI. Companies have been utilizing BI instruments for many years to extract worthwhile insights and whereas many enhancements have been made, there are nonetheless BI limitations or limitations that AI will 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 usually focuses on analyzing historic information to supply 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 international manufacturers innovate. Are you able to present an instance of how Exasol has enabled a consumer to attain vital ROI by means of your analytics database?

In line with a 2023 Forrester Complete Financial Impression Examine, Exasol prospects 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 information infrastructure.

One buyer for instance, Helsana, a pacesetter in Switzerland’s aggressive healthcare business, got here to Exasol to fill a necessity for a contemporary information and analytics platform. Earlier than Exasol, Helsana relied on varied reporting instruments with information warehouses constructed on totally 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 information that Helsana makes use of throughout all of its AI fashions and offering the

basis for its analytics. With Exasol, the Helsana crew has boosted efficiency, decreased prices, elevated agility and established a strong 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 traits in information analytics and enterprise intelligence that Exasol is making ready for, and the way do you intend to proceed driving innovation on this area?

 The 12 months 2023 launched AI on a large scale, which triggered knee-jerk reactions from organizations that in the end spawned numerous poorly designed and executed automation experiments. 2024 shall be a metamorphosis 12 months for AI experimentation and foundational work. To date, the first functions of GenAI have been for data entry by means 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 an even bigger push in the 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 information 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.