AiThority Interview with Nelson Petracek, CTO at TIBCO Software

Hi Nelson, please tell us about your journey in technology. What inspired you to start at TIBCO Software?

I started my career as a developer, learning how to fix bugs in other people’s code. It was a great way to start, as you quickly learn what not to do! After working on a number of development projects and spending time with one US-based software company (a TIBCO competitor at the time, actually), I joined TIBCO as a technology specialist working with customers and partners in the field. TIBCO had great technology, an excellent reputation, and was known for solving complex real-time data problems across a wide customer base. This was very appealing to me as a young developer, and in many ways this exact set of characteristics continues today.

You are a global leader in Cloud Integration and Analytics. How much has the industry evolved during the pandemic? How did you manage to stay on top of your game?

It is commonly stated that the pandemic has accelerated the “shift towards digital,” with more enterprises focused on digital transformation, and this is something we are definitely seeing with our customers and partners. Enterprises that had not previously prioritized digital transformation activities have been forced to revamp business operations to implement new capabilities, which commonly includes technologies such as cloud and analytics, but is also growing to include IoT, automation, blockchain, XR, graphs, and more.

At TIBCO, we’re focused on evolving and growing our rich technology stack both organically and through acquisitions, with an emphasis on providing our customers and partners with the right set of capabilities to handle today’s complex data challenges. We maintained our heritage as the industry leaders in real-time data, while ensuring organizations have the open, “anywhere and everywhere” cloud-native capabilities needed to deliver today’s modern applications. Our TIBCO LABS program also contributes to this focus by promoting a culture of collaboration and innovation with our customers, and enabling further capabilities across our TIBCO Cloud platform.

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Could you please tell us about the AI engine that drives TIBCO Cloud? How can businesses benefit from TIBCO Cloud?

TIBCO Cloud provides companies with a set of capabilities, all in one place, that enables solutions to be built to solve today’s complex data challenges. The platform helps organizations capitalize on data and insights by connecting to data wherever it is located, making predictions in real time, and unifying information assets. Businesses benefit from streamlined and automated key processes, seamless connectivity to key capabilities, greater access to data, and simplified polycloud architectures. With TIBCO Cloud, businesses don’t have to worry about technology limiting their digital business initiatives.

How do AI and Machine Learning capabilities make data integration and intelligence so much more effective?

AI and machine learning allows us to process large amounts of data at scale to uncover new insights and make informed decisions, bridging the gap between data analytics, automation, and data science. Cloud-based AI/ML, in particular, clears the path for mass adoption amongst data scientists and non-experts to accelerate data integration and data intelligence. It simplifies the user experience by suggesting the best actions or remediation steps to take in a particular context, and reduces the amount of time and effort needed to build complex digital applications. 

Please help us arrive at the best definition for Auto ML Augmentations? Where are Auto ML engineering / development trends heading into 2022?

In 2022, I expect to see accelerated AutoML adoption. AutoML is driving the democratization of data science for business users and citizen data scientists, which is important as organizations look to enable more of their workforce to make smarter, faster decisions based on high quality data. However, AutoML is not enough, as it typically does not solve what I call the analytics pipeline “book-ends.” The need to ensure that AutoML processes receive high-quality, timely, and contextual data is extremely important, as is the need to manage and monitor the AutoML derived models once they are created and deployed into production. Both of these areas will need to see greater emphasis as part of an overall AutoML strategy before organizations can realize AutoML’s full potential.

The standards of “Data integration” have evolved at a great pace due to heightened focus on digital transformation and security requirements. Could you highlight the biggest trends associated with these aspects of data integration and transformation?

Security and privacy will continue to receive a large amount of attention in the upcoming year.  Ransomware attacks; data breaches; data leakage; regulatory requirements; software vulnerabilities, see the latest Log4j example; hybrid work structures; and other topics are all driving the need to think differently when it comes to security. The adoption of zero trust architectures, the mapping of sensitive data — including the type of data, where the data is, and how it is stored, advancements in DevSecOps strategies, and data lineage tracking and tracing are all key trends associated with these concerns, especially as organizations move to polycloud approaches and consume even more cloud applications.

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One user case study related to AutoML augmentation that makes TIBCO Cloud so much more powerful?

One great example comes from the pharmaceutical industry, where a customer utilized AutoML and hybrid cloud capabilities to realize a number of benefits related to the manufacturing of certain products.  Improved regulatory compliance, increased productivity, and a reduction in scrapped batches are just a couple of positive outcomes, with savings estimated at over $100 million per year. That’s a fantastic ROI, and just one example of the power of TIBCO’s integrated approach to connectivity, data management, and analytics.

Which industries have been at the forefront in the adoption of AutoML platforms? What kind of IT expertise and budget should a company have to fully benefit from investing in AutoML augmentation platforms?

AutoML platforms have been heavily adopted in data-rich industries, such as supply chain, manufacturing, and financial services. The supply chain and manufacturing industries are leveraging AutoML platforms, for example, to try and bridge the gap between supply and demand. AutoML can reduce response times to sudden changes, increase accuracy of forecasting and distribution, and improve equipment usage, allowing organizations to improve their overall processes and efficiency. As another example, the financial services industry leverages AutoML to optimize a variety of tasks, including fraud detection, anti-money laundering, and market forecasting. But regardless of the vertical, AutoML can likely provide value, and thus should be on the research list for most organizations. Budget and required skill sets will vary depending on factors such as organizational priorities and maturity, but at the very least organizations should plan to research and test the boundaries of this capability for possible inclusion in their technology portfolio.

Cloud integration is still a nascent science. How do you help, train and support your customers manage the unique operational challenges for your customers?

We provide a combination of activities for training and supporting our customers. To start, we have, of course, various training courses, professional services-led workshops, and open source / community assets for customers wishing to learn best practices and product-specific functions. Conferences, meetups, and user groups are helpful, and our partners provide a number of services designed to help our customers meet their data and analytics challenges.

For organizations consuming our technology through the TIBCO Cloud platform, many of the operational considerations are actually “abstracted away” from the user. Functions such as containerization, scaling, fault tolerance, disaster recovery, CI/CD, user/subscription management, monitoring, and other tasks are handled by our platform, leaving the customer to focus on the needs of their application. This simplifies the operational landscape for our customers and greatly improves their ability to deliver critical functions to their users in a timely manner.

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Expert tip on how to build an AI-focused Cloud modernization technology platform:

Many organizations focus on model development, but in many ways, data and model discovery is just as important. By providing a catalog of data and model assets to your users, you can start to answer a number of questions, including: What data do you have? How good is it, and what is the associated business and technical metadata? What analytical models are in production, and how were these models trained? What is the current performance of these models, and is retraining necessary? What are the ethical implications of your models? These questions, plus many others, are supported by a proper reference, metadata, and master data management strategy, combined with modern data catalogs and ModelOps technologies.

Your plans for 2022: What kind of future do you foresee for the Cloud Integration and Analytics market?

In 2022, predictive analytics will drive new, emerging use cases around the next generation of digital applications. The technology will become more immersive and embedded, where predictive analytics capabilities will be blended seamlessly into the systems and applications with which we interact. Predictive analytics will drive use cases in next-gen apps like metaverse applications (the convergence of digital and physical worlds, powered by technologies such as IoT, digital twins, AI/ML, and XR) and the next generation of composable applications in the cloud.

Thank you, Nelson! That was fun and we hope to see you back on AiThority.com soon

[To share your insights with us, please write to sghosh@martechseries.com]