On Data Platforms. Interview with Gokhan Uluderya
” The most successful people that I have worked with and the most successful organizations I have been a part of have been the ones that embrace learning and change as opposed to resisting it.”
Q1. You have 20+ years experience in product and engineering leadership. What are the main lessons you have learned?
Gokhan Uluderya: I think there are two very important lessons that I have learned throughout my career: first one is change is inevitable and is a constant in our lives and second one is that learning is a lifelong process. The most successful people that I have worked with and the most successful organizations I have been a part of have been the ones that embrace learning and change as opposed to resisting it. They had one thing in common and that was growth mindset: constantly learning from others, from one’s mistakes, open to change even if it went against the grain, and using these learnings to reinvent, transform, and innovate.
This is particularly important in technology sector because we are making progress at break-neck speed and we are surrounded by brilliant minds that are innovative, dedicated, and very passionate to make a difference in our lives and leave a mark in history. We are not only inventing new technology but also redefining how we live our lives, how we run our businesses, how we socialize with other people, and how we interact with the nature and the machine. Where there is constant change, there is also constant learning. This is true for organizations as well; small and large. Our organizations are constantly evolving. The organizational norms and behaviors are changing. Technological advancements are also impacting how organizations operate.
So, one of the biggest lessons for me as product and engineering leader is that we all need to continuously learn and reinvent ourselves, our organizations, and processes. Experimenting, making mistakes, failing are some of the most effective ways to learn and innovate. We should encourage these behaviors within our teams and constantly remind ourselves that very impactful things happen with small and consistent forward progress.
Q2. What is the main impact on customers’ businesses of all the transformations and disruptions that we have seen in the last 25 yrs?
Gokhan Uluderya: The pace at which disruptive transformations are happening has been accelerating exponentially in the last 25 years.
Think about it… When I started my career, mainframe-based architectures were still prominent. We went from mainframe-based architectures to SOA then SaaS, followed by the Cloud revolution. Mobile computing, IoT and edge computing brought amazing capabilities and experiences to the fingertips of every user and almost every device imaginable.
The cloud computing model enabled the builders and innovators in a fundamentally different way. The powerful compute cluster that would take 6-12 months to procure and provision in a classic enterprise environment with a very hefty price tag was now available in the cloud with a few clicks for a few hours of use for any developer. These transformations enabled big data and analytics workloads moving to the cloud and led to AI/ML being done at very large scale thanks to the scale and flexibility cloud provides. All these brought us to the age of generative AI and we are now moving fast towards artificial general intelligence.
The advancements in AI are changing the entire technology and business landscape in fundamental ways. It is changing the way we consume technology and the way we live our lives. AI-driven automation, decisioning, hyper-personalization complemented by natural-language understanding and generative AI is moving the effectiveness of technology to a whole new level.
These transformations are very exciting, and they create a lot of new opportunities. Consumers feel the impact from to shopping to dining, in communications and social interactions, in health services they receive, and in the cars they drive. Businesses feel the impact in all business functions; sales, marketing, customer service, supply chain and logistics, hiring practices are all being re-imagined with AI.
Every business is trying to adapt to and adopt the innovation coming down the pipe and fear-of-missing-out is driving them to make big decisions and investments faster than they are used to.
Q3. What are the challenges, the lessons learned, and the recommendations for both customers and tech companies that are going through these transformations and disruptions?
Gokhan Uluderya: In this kind of a rapid-changing landscape, it is hard to keep up with the change on all fronts. It is easy to make mistakes and some mistakes can be costly. Winners and losers can change very quickly. As technology vendors, we must keep in mind that our duty to our customers is not only to bring them new capabilities and innovation but also do it in a responsible, trusted way and help them along their journeys as they try to adapt and transform.
One of the key lessons learned and our recommendations to our customers is to play the long game, keep calm, and make steady progress. It is easy to go after the shiny objects and hype and fall victim to FOMO. We advise our customers to have a long-term view and strategy and evaluate the incoming disruptions carefully through that lens. It is generally a great strategy to adopt and transform in a spiral pattern: have a strong business value hypothesis, start with a small pilot, validate technology and solution, prove business value and adoption before moving to the next cycle up in the spiral.
Be agile but not random. I have seen a lot of organizations confuse agility with having no strategy, vision, or plan. Some think it is a process thing like “scrum is agile, but waterfall is not”. Agile doesn’t mean “go where the wind blows on any given day”. Agility gives the best outcomes when an organization has done enough homework to develop a long-term conviction and a plan and is able to make the right changes to their roadmap based on their experiments and learnings throughout that journey.
It also makes a huge difference to have a trusted partner that is a companion to you on your journey rather than just a vendor. Look for those trusted partners that have a vested interest in making you successful with your journey rather than vendors who are motivated mainly by selling you more.
Q4. You are the Head of Product at InterSystems. What are your responsibilities?
Gokhan Uluderya: As Head of Product for Data Platforms at InterSystems, I am responsible for leading the teams that drive our innovation in Data and AI technology space. Our Data and AI technologies power the most mission critical workloads in healthcare, finance, CPG, and supply chain. In our portfolio, we have several products that are industry-leading solutions. InterSystems IRIS and IRIS for Health lead in the multi-model, translytical data platform space. Our Health Connect product line is a leading interoperability platform solving some of the most complex integration and interoperability problems in healthcare space. We also provide several Cloud Data Servies such as IntegratedML, Cloud SQL, Cloud Document as SaaS offers. InterSystems Data Fabric Studio is one of our more recent innovations and it enables our customers to solve the enterprise Data & AI problem by building smart data & AI fabrics.
My team builds our core capabilities in this space and helps our customers be successful with our data & AI products and with their data & AI strategy.
Q5. What is your experience in working with data, AI, and analytics?
Gokhan Uluderya: Before joining InterSystems, I spent close to 5 years at Salesforce and 14 years at Microsoft at various roles related to data, AI, and analytics. At Microsoft, I was one of the founding members of Azure Machine Learning services as part of the product leadership team starting in 2015. During my tenure at Azure Machine Learning, I also received my data science certification from Harvard University to deepen my knowledge of the theory of AI and ML.
During my time at Salesforce, I was the VP of Product and GM for Marketing Cloud Personalization and VP of Product for Commerce Cloud leading hi-scale commerce services and Commerce Einstein teams. We used AI/ML to build hyper-personalized experiences for consumers and AI/ML-driven decisioning and automation for back-office applications.
Now in my role at InterSystems, I am building data & AI technologies that solve very important problems in healthcare and life sciences, finance, CPG, and supply chain management.
Q6. InterSystems has been recognized as a Leader in The Forrester Wave™: Translytical Data Platforms, Q4 2024. What is a Translytical Data Platform? And why does it matter?
Gokhan Uluderya: A translytical data platform supports transactional and analytical workloads in real-time from a single system. It also provides support for multi-model data from the same system for structured, semi-structured, unstructured data. InterSystems IRIS is a leading translytical data platform that reduces architectural complexity and provides unparalleled performance and scale for operational and analytical workloads delivering real-time processing. We currently have customers that go up to 1.6 Billion transactions per second on a single deployment in the lab environment and more than 150M transactions per second in a single deployment of IRIS in production. Many data platforms may claim translytical support by bolting multiple technologies together, but InterSystems IRIS has a unique, differentiated architecture that makes it translytical natively in one system and for that reason it provides an unparalleled price-performance value in addition to performance, scale, and reliability.
Q7. Your data platform supports multi-model natively. What does it mean and what are the main benefits?
Gokhan Uluderya: Multi-modelity is the ability to support structured, unstructured, semi-structured data models from the same system. These data models can be relational, document, columnar, vector, graph, time-series etc. There are many database engines in the marketplace today that are specialized in one or many of these data models. What we mean by supporting multi-model natively is the fact that our data platform has a unique and differentiated architecture that provides these data models from one unified common data plane. Different data models are represented in a unified object-based architecture and easily projected into different data models.
This is very significant in the world we live in today because to be able to get that customer 360, business 360, or data 360 view you need to bring many different data types together. AI and Generative AI capabilities made this trait even more important because AI can consume all of the data together to make decisions. For example, to provide AI-assisted experience for a patient, you need to have a complete view of their interactions such as the relational data in an EMR, handwritten note from a doctor, X-ray images or summary document from another visit, or voice recording from the last encounter.
Being able to manage all these data on InterSystems IRIS natively and being able to use them for decisioning is a very powerful proposition for our customers.
Q8. What plans and vision do you have for the future?
Gokhan Uluderya: The AI wave we are going through right now is a game changer. This is not because AI is a new concept or technology; it is because Generative AI and particularly ChatGPT democratized AI in a very short period. AI-driven experiences were already part of many of our experiences, however progress in natural language understanding, generative AI, and AI-driven reasoning coming together in ChatGPT suddenly brought AI conversation to our kitchen tables.
Our vision is that this democratization and proliferation of AI-driven experiences will continue at a very high speed, and it will continue to revolutionize all aspects of life and business. AI-based experiences, automation, reasoning and decisioning, “artificially intelligent” machines will become ubiquitous. I also believe this will remind everyone once again how invaluable trusted, private data is and what kind of a differentiator it is; especially in the AI-driven world.
To get the right outcome from any AI system, to make any important decision, we need to feed it with clean, comprehensive, and more importantly trusted data. As a data and AI technology company, our plan is to empower our customers to build data & AI solutions in a trusted, reliable, cost-effective way. We will give our customers the data & AI fabric that allows them to build and capitalize on their data assets. We will optimize and automate their data & AI workflows using AI. And most importantly, we will infuse AI-driven experiences into all our solutions and applications in healthcare, finance, CPG, and supply chain.
Qx. Anything else you wish to add?
Gokhan Uluderya: I would like to encourage our readers to take a look at InterSystems IntelliCare. InterSystems IntelliCare is EHR reimagined using the power of AI and GenAI. It empowers clinicians, enhances patient experiences, elevates business operations, and minimizes resource utilization using AI and GenAI.
It is a great example of our vision in action: InterSystems data and AI technologies revolutionizing our experiences in healthcare which we all deeply care about.
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Gokhan Uluderya , Head of Product, InterSystems Data Platforms.
Executive leader with 20+ years of product and engineering leadership experience at leading software and technology companies (Microsoft, Salesforce, and Verizon) delivering multiple 0-1 products and services that have grown to be highly-successful businesses.
Experienced in designing and developing large-scale, cloud services (SaaS, PaaS), on-premises server products, and enterprise applications driving product strategy, roadmap, and delivery managing geo-distributed, cross-disciplinary teams (product, engineering, research, customer success, design, UA, and enablement)
A technical innovator in Data, AI/ML, Personalization space and a business leader driving a business unit as general manager responsible for P&L and C-Suite relationships.
The Forrester Wave™: Translytical Data Platforms, Q4 2024
Nov 6, 2024 — This report shows how each provider measures up and helps data and technology professionals select the right one for their needs.
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