The Importance of Data Centricity in Achieving a Successful Digital Transformation for Modern Laboratories

Embracing Data Centricity: A Key Strategy for Digital Transformation in Modern Labs

In the realm of data management, while concepts like data meshes and data fabrics gain significant attention, the execution of data centricity often falls short despite its profound implications. Data centricity entails placing data at the heart of operations and decision-making processes, transcending beyond its mere utilization in applications.

To effectively implement data centricity, three fundamental principles must be adhered to recognizing data as a vital asset, ensuring data is self-describing, and employing open, non-proprietary formats.

Understanding Data Centricity

Data centricity serves as an overarching framework encompassing the concepts of data mesh and data products. In simple terms, a data mesh represents a decentralized approach to data architecture, enabling domain-specific teams to manage their data autonomously at scale. A data product, on the other hand, is an instance of a data mesh architecture that generates business value, while a data platform acts as an enabler for data centricity, potentially executing the data mesh concept.

Implementing Data Centricity in Laboratories

Recognizing Data as a Key Asset

Acknowledging data as a key asset is fundamental, but what does this principle entail in practice? Consider examples such as a chemical structure, a new modality, an experiment, or a pharmaceutical product. Often, terminologies and data concepts are understood differently across functions, projects, or collaborations, leading to inefficiencies and resource wastage.

In laboratory settings, scientists may redo experiments because they lack trust in historical data. This mistrust arises from poorly described and untraceable data from previous years. In addition to the compound itself, contextual metadata, operational data across processes, and data life cycles are essential.

New compounds in living test systems exhibit biological reactions or molecular interactions with high variability. When planning new studies, scientists may find old experiment results unrepresentative of current conditions, deeming them unreliable for reuse. Instead of individually reassessing past experiments, a holistic approach to legacy data can uncover patterns that inform future projects, akin to consulting multiple senior scientists for advice.

Patterns in legacy data become apparent when old experiment results, along with their metadata and descriptions, are made FAIR (Findable, Accessible, Interoperable, Reusable). Statistical analyses, visualization techniques, and data mining can then reveal relationships that are not evident when examining individual assay results in isolation. This new understanding, even from failed experiments, aids automation processes by narrowing down design phase variables.

Ensuring Data is Self-Describing

Self-describing data means comprehensively documenting every detail about a compound tested in numerous experiments throughout its life cycle. This includes test results, conditions, requested parameters, methods, business context, and overall workflows.

By collecting all information in interconnected data packages, including operational and process data, solutions shift from application-centric to data-centric. Future LIMS (Laboratory Information Management Systems) or ELNs (Electronic Lab Notebooks) will resemble a portfolio of application services rather than standalone applications. This vision drives the development of ELN archiving and LIMS consolidation solutions to advance laboratory data management practices.

A robust data platform is crucial for enabling data centricity. It encompasses the end-to-end data lifecycle, operational efficiency from FAIR data principles to business process automation, and life science analytics from descriptive to prescriptive analytics. This empowers customers to develop their own AI models using their data assets.

Choosing a platform that embraces these aspects and remains vendor-agnostic is essential. The goal is to maintain full control of your data in a self-describing, flexible, and configurable manner.

The Importance of Open, Non-Proprietary Formats in the Modern Digital World

In much the same way that the MP3 format revolutionized the music industry by becoming the universal open standard, open standards are critical in various fields today. The Allotrope Foundation has introduced the Allotrope Data Format (ADF) and the Allotrope Simple Model (ASM) to serve as standardized formats for scientific data in laboratories. Similarly, the ISA 88 standard is vital for batch processing, and the ISO OAIS reference architecture plays a key role in ensuring long-term data preservation and system interoperability through an open API framework. It is crucial to consider these standards when choosing technology or solutions.

The Digital Laboratory: Vision and Maturity

Data from experiments and research can range from short-term to long-term and is often tied to specific products, such as chemical substances or biological materials. To maximize the value of these data products for both patients and researchers, the data and its integration architecture must be designed to support their extended lifecycles.

By adopting a data-centric approach, digital laboratories can fully leverage their data, drive innovation, and make well-informed decisions. This approach places data at the forefront of operations, allowing organizations to excel in the digital age.

Where do you stand in your digital transformation within the lab environment? Are you transitioning from FAIR data principles to automated testing? Are you moving from a data-centric to a business-centric approach? And how are you advancing in life sciences analytics, from descriptive analytics to prescriptive analytics? We are here to offer a maturity assessment of your digital lab journey. Regardless, we recommend saving costs, time, and effort by gaining deeper insights into effective and ineffective practices.