Every lab manager knows that good data is essential for advancing science — whether it’s to track trends, confirm or disprove hypotheses, or compare results across experiments. But what’s the best way to ensure decisions are based on good data in your lab environment?
Modern clinical diagnostic labs generate a wealth of valuable data through the use of numerous tools and applications. All this data can end up stored in multiple locations or silos, including the laboratory information management system (LIMS). Even when labs use a data warehouse, not all of their data might be consolidated into “a single source of truth.” This can hinder analysis and reporting, limiting a lab’s ability to develop the deep insights it needs to be competitive.
However, by integrating data within a data warehouse or federating data in a virtual view from all these disparate sources, labs can gain a systemwide view of their data — which provides a significantly stronger foundation for reporting, decision-making, and innovation.
In this post, we’ll explain why you should consolidate your LIMS data with data from other sources. We’ll also look at the pros and cons of data warehouses and data federation.
A quick recap of the types of data in a lab environment
When you think about reporting on lab data, the first type of data likely to come to mind is your valuable LIMS data — including detailed information relating to samples, such as expiration date, storage, source, location, and names of researchers. LIMS data is obviously critical for efficient daily lab operations and decision-making. But this is just one source of information that you will want to query for a complete picture of the lab’s business.
Data also resides in other lab applications, such as:
- Analytical instruments, like liquid handlers, sequencers, and quality control instruments.
- Laboratory Information Systems (LIS).
- Laboratory and business software for order management, billing, inventory management, freezer management, post-sequencing analysis, and clinical interpretation and reporting.
Many labs we work with already store some of this data in a data warehouse. However, when we dive deeper into reporting and analysis, we often discover not all of their data is easily accessible or in a standardized format that adheres to FAIR principles. FAIR means the data is findable, accessible, interoperable, and reusable.
Why your lab needs a consolidated view of system-wide data
It’s only by consolidating the data from all of these sources that labs can perform the advanced analytics required for developing clinical breakthroughs or gaining a competitive advantage.
By consolidating data, your lab can:
- Enhance data accuracy and completeness for a more comprehensive and accurate view across all the systems in the lab.
- Facilitate better data analysis so you can conduct analysis and reporting across multiple systems and applications more efficiently.
- Improve data security and make it simpler to ensure sensitive data is only available to authorized users.
Integration with a data warehouse or federation of data — which is preferable?
Understanding the key terms
Before we get to the “how” of data consolidation, let’s define two key terms you might not be familiar with.
Data warehouse
A type of data management system that aggregates data physically from different sources into a single, central, consistent data store. Data within the data warehouse generally requires cleansing to ensure data quality or transforming so that it’s in a format that can be read and understood for reporting. It is also stored with its metadata to ensure data provenance is maintained.
Note that a data warehouse is not interchangeable with a data lake. While both store big data, a data warehouse holds highly structured data that has been processed for a defined purpose, whereas a data lake is a vast pool of raw data that has not yet been processed for a defined purpose.
Data federation
A data management strategy that enables the querying of data from multiple databases via a virtual view. It supports unified access to data from diverse sources by converting data into a common model. It also reduces the need for massive data storage systems, while maintaining data security and privacy.
Choosing integration with a data warehouse or data federation
Deciding whether to integrate your LIMS data into a data warehouse or use a data federation strategy will depend on factors such as the size and maturity of your lab, the size of the LIMS dataset, the complexity of your lab’s software stack, the budget, and your strategic roadmap.
Data warehouses provide a physical home for all the data, giving stakeholders a central location for finding the data they want to query or report on. They can be simpler for stakeholders to understand and quicker to implement. But they come with the overhead of requiring additional data storage, which can increase in size significantly over time, and requires ongoing maintenance.
Data federation is a more flexible solution for consolidating LIMS data with other data sources. It offers labs the ability to develop new models for additional use cases, and does not have the same data storage requirements because there’s no need to move or copy the data. Another advantage is that it does not allow the underlying data to be modified (although, there could be instances when this is a disadvantage). However, because it is a virtual view of the lab’s data, it can be more difficult for stakeholders to understand conceptually.
Challenges of integrating or federating data
Data warehouses and data federation both present technical challenges for labs. Data in different systems often have different data structures and relationships, making them incompatible without data mapping. Even if they use the same standard, they might use different versions of that standard. For example, most U.S. healthcare organizations (95%) use the older HL7 version 2. If you add a new tool to the lab that supports a newer version, the data might not be easily compatible with your existing software solutions.
This is why our Semaphore team considers ontology when we help labs add new tools and applications to their workflows. It’s also why we are such big proponents of FAIR principles, PROV standards, and standardized metadata, and why we plan every data integration or consolidation project with great care.
Gain valuable insights with a consolidated view of your data
Whether your lab has an existing data warehouse or not, it’s very likely that not all your lab data is accessible for reporting in a central physical or virtual location. If so, you might be missing out on the important business or scientific insights you could gain from a consolidated, systemwide view of your data.
If you would like to discuss how to consolidate your valuable LIMS data with data from other sources so you can gain access to insights and develop scientific breakthroughs, get in touch.
