Expert Series: Overcoming Data Silos in Life Sciences

The Pistoia Alliance, a global, not-for-profit alliance advocating for greater collaboration in life sciences R&D, has released the results of its annual Lab of the Future survey, conducted in partnership with Open Pharma Research. The survey gathered insights from 200 experts across Europe, the Americas, and APAC, uncovering several notable findings.

Among the most significant challenges cited were siloed data (59%), unstructured data (54%), and a lack of metadata standardization (48%), all major barriers to fully leveraging experimental data.

These findings echo a challenge we see across the industry: scientific data is more abundant than ever, but without the right infrastructure, much of its value remains locked away.

In this first installment of our Expert Series, Dan DeAlmeida, Vice President of Product Management and Marketing at ZONTAL, shares how life sciences organizations can break down data silos, harmonize fragmented information, and unlock the full potential of their research.

How would you describe “data silos” and “data fragmentation” for someone who isn’t a tech expert? What’s the real-world impact of these problems on a business?

Imagine a company as a team of chefs, all working in the same kitchen, but everyone has their own fridge, their own recipe book, and their own way of labelling ingredients. No one shares, no one speaks the same language, and — worst of all — no one sees the full menu.

That’s what data silos and data fragmentation look like in business. Data silos happen when teams or systems keep their data isolated either intentionally or just by default. Fragmentation is what you get when that data is stored in a dozen formats, systems, or spreadsheets, with no consistent structure or context tying it all together.

The result? Poor decisions, wasted time, duplicated work, and serious blind spots. Scientists can’t find the right data. Leaders can’t trust their reports. AI can’t learn from noise. And businesses end up flying half-blind despite sitting on a goldmine of information.

The bottom line: when your data doesn’t talk to each other, your teams won’t either. And in today’s world, that’s not just inefficient — it’s expensive.

Many solutions in the industry claim to connect data. What makes ZONTAL’s “data layer” approach truly different or more effective than other integration methods?

Most vendors stop at building integrations and connecting “pipes.” They move data from Point A to Point B, maybe slap on a dashboard, and call it “integration.” But that’s not solving the problem — it’s just moving the mess around.

Think of it like upgrading from a tangle of extension cords to a clean, intelligent circuit board. We don’t just move data — we make it understandable, reusable, and AI-ready from day one. That’s the difference between short-term duct tape and long-term digital infrastructure.

ZONTAL talks about “harmonizing and standardizing” data. Can you give us a simple example of how ZONTAL takes messy, disparate data and makes it usable and understandable across an organization?

Imagine five labs running the same type of experiment, but each one uses a different instrument, logs data in different formats, and labels things in their own way. One calls it “Sample ID,” another says “Specimen,” and another just uses a number. Good luck trying to compare results or build a consistent report across those systems.

ZONTAL steps in as the universal translator.

We ingest the raw files (PDFs, spreadsheets, instrument exports, ELN entries) and automatically extract the metadata, map it to a standard model, and apply business rules to make it consistent. That “Sample ID”? It gets normalized. Units? Standardized. Instrument parameters? Aligned and tagged.

How does ZONTAL ensure that users can trust the data they’re seeing, even when it comes from many different sources? What mechanisms are in place for data integrity and reliability?

Trust doesn’t come from pretty dashboards; it comes from traceability, transparency, and control.

ZONTAL is built to enforce all three.

• Every data point ingested into the platform includes full provenance. That means it’s traceable back to its source — instrument, file, user, timestamp. Nothing gets lost in translation.
• We standardize and harmonize data using controlled vocabularies and models, but we never discard the raw input. Users can always drill down to the original source as an immutable record. It’s not a black box; it’s a glass box.
• We apply strict version control and audit trails. Every transformation, annotation, or review is logged and attributable. You know who touched what, when, and why. That’s not just good scientific process — it’s GxP-grade data governance.

For a company struggling with slow decision-making due to scattered data, how quickly can ZONTAL start delivering tangible improvements in accessing and analyzing information?

Fast. We’re not talking about a two-year digital transformation project. ZONTAL is built to deliver value early and often.

In most cases, we can start integrating and harmonizing key data sources within weeks, not months. We support hundreds of instruments out of the box and have native data standards. That means teams begin seeing unified, structured data in a matter of days after onboarding. Searchability improves. Decision-making shifts from “waiting on someone to find the file” to “click and go.”

Even better: because ZONTAL preserves context and metadata automatically, that first wave of improvement compounds over time. Each new dataset adds value to the whole. You’re not just fixing one bottleneck — you’re building a continuously improving system of insight.

Beyond just connecting data, how does ZONTAL help organizations unlock new insights or even create new business opportunities from their consolidated data?

Yes, connecting data solves the access problem. But insight comes from context, consistency, and the ability to ask smarter questions. That’s where ZONTAL really shines.

Imagine a pharma company that’s been running experiments on a specific compound for years, across different teams, CROs, instruments, and sites. The data exists, but it’s fragmented — buried in PDFs, spreadsheets, ELNs, and shared drives. No one sees the full picture.

ZONTAL pulls that fragmented history into a single, structured, searchable layer. Suddenly, you can trace every experiment ever run on that compound, compare results, link outcomes to vendors or equipment, and surface patterns that were completely invisible before.

Let’s say you realize certain batches consistently perform better when produced under specific pH conditions using Vendor B’s raw materials. That’s not just a scientific “aha”; it’s a cost optimization and a quality improvement.

ZONTAL turns historical data into strategic insight and strategic insight into new business moves.

Many organizations have a significant usage of “legacy systems.” How does ZONTAL help companies transition from older, isolated systems to a more modern, integrated data environment without massive disruption?

We get it — ripping and replacing legacy systems isn’t realistic. They’re embedded, validated, and often mission-critical. ZONTAL doesn’t ask you to start over. We meet you where you are.

Our platform is designed to wrap around legacy systems, not bulldoze them. We connect to existing infrastructure through connectors, APIs, or even file drops — whatever works for your environment. Then we extract the data, preserve its context, and bring it into a modern, standardized model.

It’s like giving your old systems a second life — without interrupting business.

Over time, ZONTAL becomes the trusted source of truth, while legacy tools gradually fade into the background. No “big bang” cutovers — just a controlled evolution from scattered systems to a unified, future-ready platform.

In the age of AI and machine learning, clean and accessible data is paramount. How does ZONTAL specifically support or accelerate a company’s AI/ML initiatives?

AI is only as smart as the data it learns from. ZONTAL prepares your data for AI by ensuring it’s structured, contextualized, and machine-readable from the start.

We don’t just store files — we standardize data using scientific ontologies, enrich them with metadata, and make them FAIR (Findable, Accessible, Interoperable, and Reusable).

That means your AI team spends less time data-wrangling and more time building models that actually work — whether it’s predicting process outcomes, optimizing formulation, or training copilots for your scientists.

In short: we take your data from “dark archive” to AI fuel.

Turning Data Challenges into Opportunities

The challenges of siloed, fragmented, and inconsistent data aren’t just technical hurdles — they’re strategic risks that slow discovery, limit innovation, and increase costs. Addressing them requires more than point-to-point integrations; it calls for a holistic approach to how scientific data is managed, preserved, and activated across the organization.

ZONTAL delivers on that need by providing the structure, context, and governance required to transform data into a trusted, strategic asset. From harmonizing disparate formats to enabling AI-ready workflows, ZONTAL equips life sciences organizations with the foundation to innovate faster, collaborate better, and make confident, data-driven decisions.

We thank Dan DeAlmeida for sharing his expertise in this first edition of our Expert Series. His insights set the stage for continued conversations on building a future-ready data environment in life sciences — one of ZONTAL’s guiding missions.