Fixing Your Scattered Data Ecosystems

Karchem Consulting
Nov 21
5 min read

Our team helps biotechs build unified data systems that help your scientists focus on their research. By strategically integrating your lab's software and future-proofing your systems, you can transform disorganized data into a connected ecosystem that accelerates discovery.

Your biotech could be producing the world's most impactful and cutting-edge research, but if your lab's data isn't organized properly, you run the risk of losing important work. Data management isn't merely about jotting down bits of information and tucking it away somewhere, but rather it should be about implementing a system that integrates across all tools, allows teams to share samples seamlessly, and improves traceability and compliance.

Let's dive deeper into the challenges your lab might be facing and how Karchem Consulting can help.

1. Too Much Data, No Way to Use It

Biotechs generate massive volumes of data daily. This data can fuel innovation, but only if it’s captured, stored, and connected in a meaningful way. If a lab is still relying on outdated systems such as spreadsheets, shared drives, or handwritten notebooks, the risks of manual data entry errors, samples being lost during handoffs, and redundant work will compound quickly. The more data a lab produces, the more prominent these gaps become.

Downstream data can serve a number of functions within a lab, but without the right organization in place, it cannot be used properly. For example:

● Data Analysis

A lack of data management can lead to anything from mislabeled samples to too much variance in metadata, making it difficult for researchers to query and accurately compare data points across multiple tests.

● Data Transfer & Reproducibility

Teams receiving data from another department inherit confusing or incomplete datasets, which means that critical findings often become lost in translation. Disorganized data results in longer transfer timelines, duplicated efforts, and potential batch failures.

● Traceability & Audit Readiness

Missing metadata can cause labs to struggle with proving process consistency or Critical Quality Attributes, which can lead to regulators rejecting datasets due to poor traceability. These issues can cause delays in regulatory submissions, possible reworking of studies, and failed audits.

To avoid drowning in an ocean of information, Karchem Consulting helps labs build a comprehensive data organization strategy. Our team evaluates your current tools and workflows, identifies gaps in usability, interpretability, and scalability relevant to your scientific workflow, and designs systems that work as hard as your scientists do. Whether it’s implementing a centralized LIMS, optimizing ELN workflows, or integrating informatics platforms via APIs, the right data strategy ensures that data is FAIR and captured in intentional and specific ways so that it makes sense later on and allows for easier searching and analysis.

2. Tools That Don't Talk To Each Other

Lab data systems don't suddenly become scattered overnight. Instead, it's often the product of years of new tools being implemented and piled on top of each other. Instruments, cloud platforms, ELNs – These all do a lot of things on their own, but the problem arises when they don't do anything together. The result is a disconnected lab with data living in silos and too many extra steps needed to transfer samples.

These issues can lead to:

● Duplicated work

● Manual data transfers

● Gaps in traceability

● Delays in analysis and review

● Compliance challenges

Our team at Karchem makes sense of a lab's various workflows and puts tools in place that actually talk to each other. Through the use of APIs, middleware configuration, and data mapping, we ensure that data is stored properly and is interoperable. A unified, connected data ecosystem. This leads to real-time data flow, cleaner handoffs between teams, and easier validation.

Let's use Benchling, AWS, and Quilt Data as an example of how this all works in practice: RNA sequencing data that's in Amazon Simple Storage Service (S3) can be automatically transferred to Quilt, but the final key metrics also remain as separate items. By integrating Quilt and Benchling, these no longer operate as standalone systems, but rather as one cohesive ecosystem. This allows for users to search for and find data points more easily using either the metrics or the immutable Quilt links, helping them get to the bigger picture more efficiently.

This is just one of the many ways in which Karchem implements data systems that help your lab function smarter, not harder.

3. The Quick Fix That Didn't Fix Anything

We often work with clients who have tried to fix their data issues with a "one-size-fits-all" platform. However, for most biotechs, this type of investment can do more harm than good. Every lab is different, with its own distinct needs, and it's rarely useful to try and force a cookie-cutter system onto your unique workflows.

With consultants who are scientists as well as technical experts, we understand what makes each lab different, and that knowledge informs the work we do for our clients. We take the time to delve into your workflows, regulatory environments, and growth roadmaps. At Karchem Consulting, we help bring your system beyond the out-of-the-box capabilities, creating a tool that integrates effortlessly into your lab. The result is a data system that supports your science, not one your team has to struggle to work around.

4. A Lack of Lab Training & User Participation

Even the most advanced and well-structured data systems can't succeed without proper user adoption. When organizations put systems in place that users don't fully understand, problems are bound to pop up. Inconsistent training and poor scientist buy-in can lead to inaccurate results, compliance risks, and wasted time.

At Karchem, we prioritize upscaling your teams by tackling training gaps and encouraging end-user participation. We address user adoption issues by learning the underlying causes that are preventing full buy-in: Sometimes the system isn't easy enough to use, sometimes it's overly complex, and sometimes scientists are missing training. Often times, however, engagement fails either because the users themselves weren't involved closely enough with the implementation, or because they don't see the value in utilizing a whole new system.

No matter the case, our team will adapt our approach and work up-and-down your lab's communication chain in order to solve these issues. We work closely with all stakeholders, from scientists to IT teams and everyone in between, to ensure that the software we implement is being utilized effectively. This not only boosts data accuracy but also ensures lab readiness when it comes to tracing and reporting.

Karchem Consulting – Your Scientific Partners In The Lab

Fixing scattered data ecosystems isn’t an IT project, it’s a scientific one. The most successful integrations come from teams like us who actually understand the workflows behind the data. At Karchem Consulting, we bridge the gap between laboratory science and software engineering. Our team helps biotechs build unified data systems that help your scientists focus on their research. By strategically integrating your lab's software and future-proofing your systems, you can transform disorganized data into a connected ecosystem that accelerates discovery.