With a keen eye for turning vast datasets into compelling visual stories, Chloe Maraina has established herself as a leading voice in Business Intelligence. Her expertise lies at the intersection of data science and enterprise strategy, offering a clear vision for the future of data management. Today, we delve into the strategic implications of Rocket Software’s intent to acquire Vertica, exploring how this move is set to reshape the landscape of enterprise analytics. Our conversation will touch on the promise of modernizing core systems without disruption, the tangible business value of AI-driven decision-making, and the powerful synergies created by integrating high-performance analytics with application modernization.
Your strategy emphasizes “modernization without disruption.” How will integrating Vertica’s analytics database specifically achieve this, and what are the first practical steps a customer can take to run next-generation AI directly on their core systems’ trusted data? Please share an example.
That phrase, “modernization without disruption,” is the core of this entire strategy. For years, the biggest hurdle to leveraging new tech like AI has been the monumental risk and cost of ripping and replacing legacy systems. Vertica sidesteps that by acting as a high-performance analytical layer that can sit right on top of existing, trusted data sources. The first practical step isn’t a massive migration; it’s about connection. A customer can begin by integrating Vertica with their core mainframe or on-premise databases, where their most critical business data resides. For example, a large bank can use this to run sophisticated AI fraud-detection models directly on their live transactional data, rather than moving petabytes of sensitive information to a separate cloud environment. This drastically reduces latency and security risks, allowing them to get immediate, actionable insights without turning their operations upside down.
As AI investment surges, turning data into business value has become more complex. How does Vertica’s technology directly address this challenge, and what specific metrics can enterprises use to measure the ROI of transforming their data warehouses for AI-driven decision-making?
It’s a paradox we see everywhere: companies are pouring money into AI but struggling to see a clear return because their data infrastructure can’t keep up. As Milan Shetti noted, the difficulty of turning data into real value has never been higher. Vertica addresses this head-on with its industrial-strength performance. It’s built for mission-critical, high-speed analytical workloads, which means it can process the massive queries required for training and running AI models without buckling. This speed is what turns a theoretical AI model into a practical business tool. To measure ROI, I advise clients to look beyond just cost savings. Key metrics should include the reduction in decision-making latency—how much faster can you react to market changes? You can also measure the accuracy lift in predictive models, like sales forecasting or supply chain optimization, and attribute the resulting revenue gains. Another crucial metric is the cost avoidance associated with repatriating workloads from expensive cloud platforms, which Vertica directly enables.
Given the recent integration of OpenText’s Application Modernization and Connectivity business, how does the acquisition of Vertica build upon that foundation? Please elaborate on the key synergies you expect to see between these two former OpenText assets.
This is where the strategy becomes truly powerful. Think of it as a two-pronged approach to modernization. The Application Modernization and Connectivity (AMC) acquisition, completed in May 2024, gave Rocket Software the tools to update and connect the core applications that run the business. Now, with Vertica, they are acquiring the engine to analyze the data those applications produce. The synergy is seamless. You can now modernize an aging but essential financial application while simultaneously deploying a state-of-the-art analytics platform to run AI-driven market analysis on that application’s data. This creates a cohesive ecosystem where the applications and the analytics evolve together. This unified approach ensures that as businesses modernize their core operational software, they can immediately unlock the value of the data within it, creating a powerful, self-reinforcing cycle of innovation.
Vertica supports cloud, on-premise, and hybrid deployments. How will you help customers navigate the trade-offs between performance, cost, and data sovereignty in these diverse environments, and what technical challenges do you anticipate in repatriating analytics workloads?
The flexibility to operate across cloud, on-premise, and hybrid environments is a massive advantage because no single solution fits all workloads. My role is to help customers map their needs to the right environment. For workloads involving highly sensitive customer data or requiring sub-second response times, on-premise might be the best choice to ensure data sovereignty and peak performance. Conversely, for less-critical, bursty analytical tasks, the cloud offers cost-effective scalability. The real magic is in the hybrid model, where you balance these needs. The primary technical challenge in repatriating workloads—moving them from the public cloud back on-premise—is often data gravity and integration. Moving massive datasets is complex and can cause disruptions. However, Vertica’s architecture is designed for this kind of flexibility, easing the transition and allowing companies to regain control over costs and data without sacrificing analytical power.
What is your forecast for the future of enterprise data analytics and AI modernization?
I foresee a significant shift away from the “cloud-at-all-costs” mentality toward a more pragmatic, hybrid approach. The future isn’t about choosing between the mainframe and the cloud; it’s about intelligently integrating them. We will see a wave of workload repatriation, as businesses become more sophisticated about the true costs and security implications of public cloud environments for their most sensitive data. The technologies that will win are those, like Vertica, that can bridge these worlds, enabling enterprises to run advanced AI and analytics directly on their core, trusted data, wherever it resides. This move will unlock a new level of performance and trust in AI, grounding its incredible potential in the operational reality and time-tested data of the enterprise.
