As the volume and complexity of lab-generated data continue to grow, the need for scalable, customizable analytics platforms has become essential. The question now is how to turn millions of data points into clear, meaningful insights that range from individual equipment readings to enterprise-wide performance trends.
To bridge that gap, companies are turning to unified data platforms that can capture live signals from equipment, environmental sensors, and quality systems—and present them in a single, trusted view. In this Q&A, Labcompare Editor-in-Chief Michelle Taylor discusses how to successfully cross that bridge with Rob Estrella, Chief Executive Officer of Elemental Machines. The end result shouldn’t just better reporting—it’s should be a shared operational picture that allows scientists, quality teams and management to make faster, evidence-based decisions with confidence.
Q: Labs generate highly fragmented, often siloed data across instruments and software. Why is it critical in today’s day and age to unify these data streams?
A: When data live in different systems, people can see different versions of reality. Unifying things like equipment signals, environmental conditions, and usage puts everyone on the same page and shortens the path from “What happened?” to “What do we do now?” In practice, that means fewer manual reconciliations, faster investigations, and clearer handoffs between QA, operations, and facilities. Our approach is to meet labs right where they are. We start with live monitoring and dashboards, then layer in deeper analytics and, when it’s needed, enterprise business intelligence (BI). This keeps the same facts flowing to every decision-maker without extra work. This is all about creating a shared operational picture that teams trust and can act on together.
Q: Transitioning from raw data collection to analytics often requires a cultural shift. How can lab managers help teams adopt and trust the insights?
A: I’d say make it useful first, not perfect. Start with a single question that matters to the team, like “Which assets can we safely power down overnight or weekends?” Then build a shared view that answers it. Tie the data to existing routines (e.g., shift huddles, weekly project management planning), keep all definitions consistent across departments, and publish scheduled summaries, so nobody has to waste time hunting for them. Trust grows when people see that having the same source of truth means fewer escalations, quicker CAPAs, and less spreadsheet wrangling. From there, you can drill down and add cross-site comparisons as needed.
Q: Given the sensitivity of lab-generated data, how do you ensure accuracy and protect against both technical errors and cybersecurity threats?
A: Accuracy starts with automated capture and time stamped, audit-ready records. From there, context should stay attached (asset, location, SOP/batch, etc.) and alerts should be routed, so small issues never become big deviations. On the platform side, we use a combination of bespoke solutions and industry-leading data sharing and BI tools (e.g., Big Query, Snowflake, Sigma) that comply with IT’s enterprise security, data retention, and data governance needs. In regulated settings, that combination (automated logs + controlled access + standard infrastructure) reduces data integrity errors and operational risk. When data flow into quality systems, direct integrations can ensure traceability, anchoring records to live conditions rather than delayed paperwork.
Q: How does your BI solution integrate with or complement platforms like LIMS — which many labs already utilize — rather than duplicating effort?
A: We complement and integrate with LIMS, ELN, EBR, CMMS, and MES rather than replace them. Live signals like environmental and utilization data are captured once and pushed to the right system of record — or they’re analyzed alongside quality data. The point is to centralize collection and context while letting each system continue to do what it does best, whether that’s sample tracking, batch execution, equipment service, etc. Our integrations strategy is broad by design, so labs don’t get boxed in by one vendor.
Q: How does the three-tiered model adapt to both a small startup’s needs and the complexity of a large pharmaceutical company’s operations?
A: We intentionally built three “on ramps”:
- Tier 1 – Platform: live monitoring, alerts, dashboards, and exports — fastest time to value, minimal IT
- Tier 2 – Dynamic Data Insights: built-in BI + warehouse to uncover trends, create custom reports, and score asset health
- Tier 3 – Connected Data Ecosystem: connect to enterprise BI and merge cross-source data for forecasting and portfolio-level decisions
A startup can stay in Tier 1 and still get actionable wins. A global manufacturer can run Tier 2 or Tier 3 to standardize KPIs across sites and feed enterprise analytics. It’s the same data foundation, scaled to the organization.
Q: Can you share a concrete example where your BI solution delivered measurable improvements for a lab client?
A: Sure. Using our dashboards, a team analyzed usage patterns and hour-by-hour ON time by room and by instrument. Two quick changes followed: 1) they moved non-critical runs into off-peak windows and 2) shifted maintenance to low-impact slots. Within a quarter, they reported fewer after-hours escalations, smoother project management, and clearer justification for capital planning. This happened because utilization and run duration were visible by category and by instrument, not buried in spreadsheets. The work took configuration, but not custom code, and the views are now part of their routine reviews.
Q: Do you see applications for this solution outside of scientific labs — for example, in manufacturing, healthcare, or other data-heavy industries?
A: This applies wherever people run repeatable workflows on machines. Pharma manufacturing is an obvious extension because the same mix of environmental context, utilization, and quality records powers uptime, audit readiness, and energy savings. The pattern also fits device assembly, pilot plants, and other data-heavy environments where teams benefit from shared, real-time visibility, and regular updates for leadership.
Q: As labs increasingly adopt automation and AI, where do you see the next frontier for business intelligence in the life sciences?
A: The next step is question-to-answer experiences that sit on top of trusted data. Imagine AI agents that understand your assets, criticality, and history, and can answer questions like “Which centrifuges are idle right now?” or “Did yesterday’s ambient spike contribute to that freezer’s drift?” These assistants don’t replace validation or quality systems, but they can speed up the transition from signal to decision while maintaining the guardrails. That’s where we’re investing. We’re making it easy to ask better questions of the lab and get grounded, operational answers.
About the interviewee
As CEO of Elemental Machines, Rob Estrella leads the company’s strategic vision and operational execution with a dual focus on solving client challenges across lab operations and accelerating company growth. With 20+ years of experience in enterprise technology and commercial leadership, Rob has a deep understanding of what it takes to scale data-driven platforms that serve the needs of organizations ranging from startups to global enterprises. Since joining Elemental Machines, Rob has played a central role in driving record-setting commercial success, building a high-performance go-to-market engine, and deepening customer trust. His prior leadership roles include building and leading global sales, customer success, strategic alliances, and commercial operations teams for companies spanning biotech, health care, and pharma.