As enterprises embrace AI, they face mounting pressure to safeguard data integrity and uphold rigorous compliance standards. Centralized data management and observability can help.
Our “6Q4” series features six questions for the leaders, innovators, and operators in the real-time analytics arena who are using innovative technologies to transform the world as we know it.
RTInsights recently sat down with Benjamin Anderson, SVP of Technology and CTO of Cloud for EDB, to talk about the challenges of managing the explosive growth of unstructured data, particularly as such data plays an increasingly important role in enterprise AI efforts.
Here is a summary of our conversation:
RTInsights: What’s an innovative way to manage unstructured data?
Anderson: An innovative approach to managing unstructured data goes beyond outdated “data lake” methods, especially in the AI era. A unified, sovereign data platform integrates unstructured, semi-structured, and structured data in one system, eliminating the need for separate solutions. This approach delivers quality-of-service features previously available only for structured data. With a hybrid control plane, organizations can centrally manage their data across multiple environments, including various cloud platforms and on-premises infrastructure.
RTInsights: What makes this approach effective?
Anderson: When it comes to managing diverse data – whether that is structured, unstructured, or semi-structured – it has traditionally required multiple databases and storage solutions, adding operational complexity, cost, and compliance risk. Consolidating structured and unstructured data in a single multi-model data platform will help accelerate transactional, analytical, and AI workloads.
RTInsights: What’s the best way to deploy this technique?
Anderson: The most effective way to deploy a multi-modal data platform is through a single, comprehensive, off-the-shelf solution rather than piecing together various open-source tools. The integration costs of the DIY approach are prohibitively high for most organizations. Instead, opt for a container-driven software installation that consolidates both structured and unstructured data in one platform. This unified approach efficiently supports transactional, analytical, and AI workloads without the complexity of managing multiple systems.
RTInsights: What’s the best way to ensure its security?
Anderson: As teams look to adopt new tools, data sovereignty is now a core IT consideration as they balance innovation with adherence to stringent data localization laws. For those in highly regulated sectors like finance and healthcare, having the flexibility to run advanced AI applications while ensuring data remains secure and compliant across private and hybrid infrastructures is a top priority. They should look for a centralized management and automation solution that offers complete control over data with deployment flexibility across private, on-prem, and hybrid cloud environments.
RTInsights: How does ‘real-time’ impact data?
Anderson: The ability to act on data in real-time isn’t just beneficial—it’s a necessity in today’s fast-paced world. Accenture reports that companies able to leverage real-time data are 2.5 times more likely to outperform competitors. Consider Uber, which adjusts its pricing dynamically based on real-time factors like demand, traffic, and weather conditions. This near-instant capability drives business success by aligning offerings with evolving customer needs.
Companies stand a lot to gain by giving frontline employees the ability to make informed, real-time decisions. But in order to do so, they need a near-instant understanding of customer data. This means the data needs to flow seamlessly across domains so that real-time models can provide timely information to help workers make impactful decisions. Postgres supports such complex transactions and near real-time analytics, making it an ideal choice for enterprises that need to deliver instant value. Its extensibility lets businesses integrate AI models directly within the database, enabling personalized experiences at scale.
RTInsights: How does the evolving role of AI impact data governance, particularly in the context of handling sensitive data, as organizations adopt data management technology?
Anderson: Centralized data management and observability act as the unifier for organizations to extract insights from their data, and when you add AI into the mix, enterprises face mounting pressure to safeguard data integrity and uphold rigorous compliance standards. This evolving landscape puts data sovereignty front and center—where strict governance, security, and visibility are not just priorities but prerequisites. Organizations need to know and be certain about where their data is and where it’s going – this visibility and direct oversight of their data supports the sovereignty needed.
About Benjamin Anderson: Benjamin Anderson is a seasoned technologist specializing in open-source data platforms and AI innovation. As SVP, Technology and Strategy & CTO, Cloud at EDB, Anderson drives EDB’s portfolio technical strategy, bridging the needs of the individual developer and the enterprise c-suite executive. With a career spanning over a decade in engineering and leadership roles, Anderson has a proven track record of unifying complex technical systems and strategic business outcomes. His experience in open-source technology and data sovereignty fuels his mission to empower enterprises to innovate at scale while maintaining control and integrity over their data.
