Despite the hype, practical artificial intelligence (AI) applications in the life sciences sometimes feel farther away than ever. This problem runs even deeper for the many labs that don’t have much centralized data about their labs’ operations or science. How can you expect to leverage AI when your scientists are still using USB sticks or trying to figure out where to save files in the shared drive? AI is tremendously hungry for huge volumes of clean, structured data; yet, from wet lab instruments and LIMS systems to analytical software and manual records, the data landscape is only getting more fragmented.
The solution runs through capturing your lab’s data in a central data platform to establish basic operational visibility and data automation. Imagine your team effortlessly synthesizing data from hundreds of instruments in dozens of global sites, compiling Investigational New Drug datasets in days rather than months, and detecting errors in assays in real-time. Central data platforms are no longer just nice-to-have; they are foundational to unlocking the full potential of AI and automation in the lab.
Why centralizing your data matters
Central data platforms are repositories that connect to data sources in your lab to assemble a single, combined source of truth, accessible in an app for users. Rather than scattering valuable datasets across disparate silos—such as spreadsheets, individual instrument files, and ad hoc cloud storage—a centralized repository aggregates these assets into one standardized filesystem and database, organized with metadata, and does so automatically by connecting to the lab instruments so scientists don’t have to figure out where to file things.
This unification does more than streamline file management. It improves data integrity, enhances reproducibility, and creates a foundation for accessing AI-driven insights. It makes your data FAIR–findable, accessible, interoperable, and reusable.
For example, machine learning models for predictive analytics in bioprocesses are only as good as the data fed into them. The same applies to chromatography column bleed modeling, or lab instrument utilization predictions, and any other data-driven process. If AI pipelines rely on incomplete, inconsistent, or duplicated data scattered across multiple sources, their outputs can become unreliable or even unusable; central data platforms address each of these issues.
Furthermore, these platforms enable seamless data retrieval and sharing across teams, reducing the time scientists and lab technicians spend searching for datasets or re-running assays due to missing information. This operational efficiency translates to faster scientific discoveries and reduced overhead.
Walk before you run: Prove out clean data with simpler insights before AI
It’s useful to think of AI as just an incredibly fancy statistical model. In that sense, it makes sense to approach AI by aiming for a simpler statistical model first. This is a helpful framing as you think about what you want to work towards with AI, because it helps clarify the questions of what problem you’re solving: Statistical model for what? With what inputs and outputs? What would the AI be predicting? Defining the problem to be solved can be half the battle. And once you have a problem in mind, the next step is to get clean data.
Data cleanliness is the accuracy, consistency, and completeness of datasets. Inconsistent naming conventions, missing metadata, and disparate file formats introduce friction into automated workflows. Worse, they can create errors when AI systems attempt to interpret or act on the data.
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Central data platforms help standardize data formatting and structure, enforce metadata requirements, and enable version control. This ensures that data meets the quality thresholds needed to automate processes like assay interpretation, anomaly detection, and even experimental design recommendations.
In this way, AI, automation, and statistics amplify the benefits of clean data—but they also magnify the costs of poor data hygiene. Labs that invest in centralization and standardization position themselves to fully leverage AI technologies without being held back by foundational data issues, and that investment looks very much like working towards more traditional statistical modeling or process automation. Most labs aren’t even at that point yet.
How to realize a centralized data repository and data strategy
Creating a central data platform may seem daunting, especially when dealing with legacy systems and ingrained processes. However, phasing out your approach can help simplify the work, and codifying all of this into a single data strategy can help your company stay aligned:
- Define your problem and value
Interview your scientists and leadership to determine where the greatest data frictions are and what their day-to-day bottlenecks are. These bottlenecks are often tied to critical core assays in your company’s science. Interviews also inform opportunities to pick early projects to apply your central data platform to and can help you build the business case. - Audit existing data sources and workflows
Begin by cataloging all data-generating instruments, software systems, and manual record-keeping practices. Understanding where data originates and how it is currently stored will help identify gaps, redundancies, and integration challenges. Map these sources of data to the assays and processes that are currently carried out in your lab to understand where all that data is flowing. - Select and implement the right infrastructure
Choose an infrastructure that can scale with your lab’s needs. Whether it's an on-premises system or a cloud-based SaaS, the repository should support data capture and parsing from multiple sources (including lab instruments), enforce data standards, and allow for flexible querying and integration with downstream tools. - Define data governance policies
Establish clear policies for data formatting, metadata requirements, versioning, and access controls. Data governance ensures consistency across teams and lays the groundwork for automated pipelines and AI applications. - Create test installations
Start small on a limited number of data sources to ensure that you can capture data seamlessly and clean it. Oftentimes, even getting to the point of asking yourself “What data do I want to capture from this instrument?” can require a fair amount of work, familiarity with a scientific process, and an understanding of the decisions this data will inform. - Integrate automation and AI gradually, deliver value early
Once you’ve built a reasonably large, clean dataset, you can begin layering on automation workflows or statistical models. Start by automating repetitive and well-defined tasks, such as data normalization or report generation, or creating some basic visualizations and regressions. These simple automations or statistical models will help drive early value and trust to get stakeholders excited, generating momentum for AI models later being integrated in as “upgrades” to handle more complex analysis, anomaly detection, or predictive modeling. - Train teams and foster a data-centric culture
As this spreads throughout your lab, ensure that lab personnel are trained on the repository's tools and capabilities, and on what projects are delivering value so they can be aligned with the data strategy. Creating a culture that values data integrity, documentation, and proper metadata usage is critical for long-term success.
Looking ahead: The future of AI-ready labs
There’s a long road to realizing AI in the lab, but the benefits are clear—reduced manual workload, faster time-to-insight, and increased confidence in experimental outcomes. Many labs are catching on, and labs that lack centralized and clean data infrastructures risk being left behind.
For lab managers, spearheading the development of a central data platform is a strategic investment. It not only improves operational efficiency but also unlocks the transformative potential of AI and automation. By laying a strong foundation today, labs can position themselves to meet the scientific and operational challenges of tomorrow with agility and confidence.
