The modern laboratory is no longer defined solely by its instruments, but by the intelligence of the systems that connect them. As labs evolve into highly networked, data-rich environments, there’s growing pressure to extract actionable insights from the wealth of information they generate. At the forefront of this shift is a new generation of optimization platforms, solutions that combine Internet of Things (IoT) infrastructure, cloud-based analytics, and predictive intelligence to improve lab performance while driving sustainability.
One standout example is a unified monitoring dashboard developed by a leading life sciences technology company, which was recently recognized for its real-world contributions to lab performance and environmental impact.
At the heart of that award-winning platform is Saradha Nagarajan, an expert data professional who served as the primary lead on the project’s data lake initiative. Designed to support future AI integration, this foundational infrastructure brought together service requests, asset lifecycle information, customer contact records, and operational metrics, delivered through a single interface powered by SAP HANA.
Building the Intelligence Layer for Labs
Behind the scenes, building this system required reimagining how lab data is collected, connected, and scaled. Saradha Nagarajan, a seasoned data leader and Globee Awards judge for Artificial Intelligence, served as the primary lead on the data lake initiative that underpins the platform’s intelligence.
Her work focused on unifying customer data from SAP CRM and ECC systems into a centralized architecture using SAP HANA. This consolidated view now supports key analytics around service requests, asset lifecycle tracking, and operational efficiency. “The challenge wasn’t just gathering data,” Nagarajan explains. “It was designing an infrastructure that could power predictive tools and support AI applications in the future.”
This data lake now powers the company’s CrossLab Connect Digital Services, a subscription-based platform that enables labs to track system health, monitor utilization trends, and reduce downtime through proactive, data-informed servicing.
Evaluating the Cutting Edge of AI in Practice
Saradha’s impact in digital transformation extends beyond product delivery. As a paper judge at the 2nd International Conference on Artificial Intelligence & Machine Vision, she has reviewed research exploring how machine learning and vision systems can drive efficiencies in industrial and scientific applications. Her role in evaluating AI-driven tools reflects a broader commitment to making artificial intelligence not only powerful, but practical.
This blend of academic and applied insight continues through her role on the Globee judging panel, where she assesses innovations in AI for their technical soundness, ethical design, and real-world scalability.
Where Data Meets Sustainability
The unified platform Saradha helped build isn’t just smart, it’s sustainable. By helping labs shift from reactive maintenance to proactive decision-making, the solution reduces unnecessary service calls, cuts energy consumption, and extends the life of high-value lab assets. It has also opened new revenue streams for the company through its scalable, subscription-based model.
Saradha’s leadership on this project aligns with her broader contributions to the AI and analytics community. She currently serves as a judge for the Globee Awards in Artificial Intelligence, where she evaluates AI-driven innovations for real-world utility, ethical design, and technical rigor. Her focus is not just on what AI can do, but how responsibly and effectively it can be applied to sectors like life sciences and sustainability.
As lab ecosystems grow in complexity, the ability to unify data, surface insights, and drive intelligent decision-making will separate those merely collecting data from those truly using it. With technologists like Saradha Nagarajan leading the charge, the lab of the future looks not only smarter, but more sustainable, agile, and insight-driven.
And while much of this work happens behind the scenes, its impact is already visible, in better lab performance, reduced downtime, and a measurable step forward in sustainable science.
