Harmonized Scientific Data: The Key to Faster Discovery
What Is Data Harmonization and Why It Is a Critical Ingredient for Biopharma 4.0?
Harmonizing scientific data enables it to become findable, accessible, interoperable, and reusable (FAIR). Discover how data harmonization is helping to shape the future of the biopharma industry.
Data Manipulation Slows Down Workflows
When running tests and experiments in any process and analytical workflow, scientists gather data from different instruments, sensors, systems, and databases. Once the data is collected, the next step is to clean and organize the data to allow comparison and analysis of data sets.
Since the data from instruments and bioinformatic systems is usually in proprietary formats, this data manipulation is a burden for many scientists who have to manually extract raw data, identify gaps or discrepancies, and transcribe findings.
In the biopharma industry, scientists spend 49% of their time on manual data retrieval and transformation, according to PharmaIQ and TetraScience's 2022 State of Digital Lab Transformation in Biopharma Survey.
Imagine if they could redirect all their efforts to higher-value activities. Data harmonization across data sets brings efficiency to labs because it reduces manual work, increasing productivity and throughput while minimizing errors that might influence final analysis.
Why Data Harmonization Solves Scientists' Pain Points
Here are some of the challenges that reinforce the importance of a single source of truth for your scientific data:
Data Volume and Variety Becomes a Burden
Life science is evolving at breakneck speed causing data to grow exponentially in quantity and diversity. According to IDC, global data creation and replication reached 64.2 zettabytes (ZB) in 2020, with an expected compound annual growth rate (CAGR) of 23% over the 2020-2025 forecast period.
One of the biggest challenges for scientists is the sheer volume of data that they generate, acquire, and use on a daily basis. As data is generated and multiplies over time, it tends to get scattered and is less accessible and usable. In order to unlock the full potential of lab experiments and research, scientific data needs to be centralized and harmonized.
Accessibility and Findability of Data Poses a Challenge
When data is siloed, it becomes significantly harder to gain scientific inferences and insights. Finding data from past experiment runs (i.e. over time and across instruments or workflows) is the first challenge. Searching data becomes much easier with metadata and common headers.
The next obstacle is accessing or retrieving the data in the correct tool and format. Data generated from multiple systems and at various stages of the workflow is necessary for visualization, advanced analytics, and data modeling.
Data siloes prevent easy and quick access and retrieval of data. In addition, siloes often introduce intermediary steps that require additional manipulation and manual effort. For example, files often need to be exported in CSV formats to be absorbed by the downstream tools and software.
Harmonized Data: The Key to Future-Proofing Scientific R&D
Manual data manipulation is solved by centralizing the data. However, having the data in a central location is not sufficient if the data is not harmonized.
With data harmonization, the goal is to not only aggregate data but also offer standardized views across different data sets. This process makes the data more accessible and prepared for artificial intelligence and machine learning (AI/ML) tools.
So, what is data harmonization? This term refers to the process in which data sets from different types and sources are organized and combined in a format that is compatible and comparable. In other words, it's a way of making data cross-comparable despite their beginnings in multiple data formats, languages, schemas, or structures.
Let’s use an analogy of a restaurant menu. Restaurants generally serve different types of cuisine. However, as a society, we’ve “harmonized” menus to include recognizable sections: appetizers, mains, desserts, and drinks. These categories allow you to cross-compare data: “Show me all appetizers from these three different restaurants.”
Without harmonization, data might appear disorganized. For example, data might have inconsistent units or values, or might simply not be in close enough formats to analyze together.
With data harmonization, it's easier to find, access, and use data. Data harmonization also allows organizations to make their data more actionable by following FAIR data principles. As a result, it helps save scientists' time and improve the quality of data analysis.
Benefits of Data Harmonization in Biopharma 4.0
The biopharma industry provides a valuable use case of how data engineering streamlines lab workflows – as part of a biopharma 4.0 strategy. The concept of biopharma 4.0 refers to the next generation of biomanufacturing, leveraging the use of smart factory technologies in biopharma.
Let's consider the example of absorption, distribution, metabolism, and excretion (ADME) assays, which give information on the pharmacokinetics (PK) properties of a drug candidate.
These tests are critical to drug discovery and often outsourced to contract research organizations (CROs). The challenge, however, is that most CROs maintain their own data formats for standard assays. Sometimes multiple CROs are involved, escalating the hurdles of data aggregation and harmonization.
With a platform able to harmonize data reports from several CROs, life sciences organizations can make the ADME/PK process more efficient and scalable. Scientists no longer need to go through different Excel spreadsheets, perform data quality checks, and unify results manually. An automated data harmonization process saves time, reduces errors, and boosts throughput.
To learn more about the benefits of harmonized CRO data in the cloud, download our case study.
The cloud is part of the solution because it makes actionable FAIR data more accessible. Three out of four biopharma organizations are planning, implementing, or have completed the process of replatforming scientific data to the cloud, according to the PharmaIQ and TetraScience's survey.
Discover why life sciences R&D organizations are replatforming their experimental data to the cloud. Watch this webinar with TetraScience Chief Scientific Officer, Mike Tarselli, Ph.D., MBA, to learn more.
Harmonize Data with TetraScience
Organizations can deploy Tetra Scientific Data Cloud to automatically ingest and centralize scientific data from instruments, sensors, informatics systems etc. in a cloud-based data lake. According to Ventana Research, 41% of companies managing big data with data lakes saw gains in competitive advantage, while 37% reported reduced costs.
TetraScience enables data harmonization by:
- Transforming raw data into a vendor-agnostic format called Intermediate Data Schema (IDS)
- Storing the data in relational tables
- Enabling easy access to data through electronic lab notebooks (ELNs), laboratory information management systems (LIMS), data science tools, and more
With data harmonization:
- Data is in an open, vendor-agnostic format
- Data that can flow across different instruments and applications
- Data is prepared for AI/ML and other advanced analytics
Take the next step toward FAIR data management with TetraScience. Book a demo now. We will show you how labs leverage our platform to eliminate manual data processing, reduce scientific data silos, and boost collaboration for accelerated R&D.
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